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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-24 00:43:13
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ibm-ai-platform/granite-7b-lab-accelerator
|
ibm-ai-platform
| 2024-05-21T13:31:15Z | 54 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mlp_speculator",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-24T18:27:32Z |
---
license: llama2
---
Note: THIS MODEL HAS BEEN MIGRATED TO https://huggingface.co/ibm-granite/granite-7b-instruct-accelerator
## Installation from source
```bash
git clone https://github.com/foundation-model-stack/fms-extras
cd fms-extras
pip install -e .
```
## Description
This model is intended to be used as an accelerator for [granite 7B (instruct lab)](https://huggingface.co/instructlab/granite-7b-lab) and takes inspiration
from the Medusa speculative decoding architecture. This accelerator modifies the MLP into a multi-stage MLP, where each stage predicts
a single token in the draft based on both a state vector and sampled token
from the prior stage (the base model can be considered stage 0).
The state vector from the base model provides contextual information to the accelerator,
while conditioning on prior sampled tokens allows it to produce higher-quality draft n-grams.
Note: The underlying MLP speculator is a generic architecture that can be trained with any generative model to accelerate inference.
Training is light-weight and can be completed in only a few days depending on base model size and speed.
## Repository Links
1. [Paged Attention KV-Cache / Speculator](https://github.com/foundation-model-stack/fms-extras)
2. [Production Server with speculative decoding](https://github.com/IBM/text-generation-inference.git)
3. [Speculator training](https://github.com/foundation-model-stack/fms-fsdp/pull/35)
## Samples
_Note: For all samples, your environment must have access to cuda_
### Use in IBM Production TGIS
*To try this out running in a production-like environment, please use the pre-built docker image:*
#### Setup
```bash
HF_HUB_CACHE=/hf_hub_cache
chmod a+w $HF_HUB_CACHE
HF_HUB_TOKEN="your huggingface hub token"
TGIS_IMAGE=quay.io/wxpe/text-gen-server:main.ddc56ee
docker pull $TGIS_IMAGE
# optionally download granite-7b-lab if the weights do not already exist
docker run --rm \
-v $HF_HUB_CACHE:/models \
-e HF_HUB_CACHE=/models \
-e TRANSFORMERS_CACHE=/models \
$TGIS_IMAGE \
text-generation-server download-weights \
instructlab/granite-7b-lab \
--token $HF_HUB_TOKEN
# optionally download the speculator model if the weights do not already exist
docker run --rm \
-v $HF_HUB_CACHE:/models \
-e HF_HUB_CACHE=/models \
-e TRANSFORMERS_CACHE=/models \
$TGIS_IMAGE \
text-generation-server download-weights \
ibm/granite-7b-lab-accelerator \
--token $HF_HUB_TOKEN
# note: if the weights were downloaded separately (not with the above commands), please place them in the HF_HUB_CACHE directory and refer to them with /models/<model_name>
docker run -d --rm --gpus all \
--name my-tgis-server \
-p 8033:8033 \
-v $HF_HUB_CACHE:/models \
-e HF_HUB_CACHE=/models \
-e TRANSFORMERS_CACHE=/models \
-e MODEL_NAME=instructlab/granite-7b-lab \
-e SPECULATOR_NAME=ibm/granite-7b-lab-accelerator \
-e FLASH_ATTENTION=true \
-e PAGED_ATTENTION=true \
-e DTYPE=float16 \
$TGIS_IMAGE
# check logs and wait for "gRPC server started on port 8033" and "HTTP server started on port 3000"
docker logs my-tgis-server -f
# get the client sample (Note: The first prompt will take longer as there is a warmup time)
conda create -n tgis-client-env python=3.11
conda activate tgis-client-env
git clone --branch main --single-branch https://github.com/IBM/text-generation-inference.git
cd text-generation-inference/integration_tests
make gen-client
pip install . --no-cache-dir
```
#### Run Sample
```bash
python sample_client.py
```
_Note: first prompt may be slower as there is a slight warmup time_
### Use in Huggingface TGI
#### start the server
```bash
model=ibm-fms/granite-7b-lab-accelerator
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model
```
_note: for tensor parallel, add --num-shard_
#### make a request
```bash
curl 127.0.0.1:8080/generate_stream \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
-H 'Content-Type: application/json'
```
### Minimal Sample
*To try this out with the fms-native compiled model, please execute the following:*
#### Install
```bash
git clone https://github.com/foundation-model-stack/fms-extras
(cd fms-extras && pip install -e .)
pip install transformers==4.35.0 sentencepiece numpy
```
#### Run Sample
##### batch_size=1 (compile + cudagraphs)
```bash
MODEL_PATH=/path/to/instructlab/granite-7b-lab
python fms-extras/scripts/paged_speculative_inference.py \
--variant=7b.ibm_instruct_lab \
--model_path=$MODEL_PATH \
--model_source=hf \
--tokenizer=$MODEL_PATH \
--speculator_path=ibm/granite-7b-lab-accelerator \
--speculator_source=hf \
--speculator_variant=1_4b \
--top_k_tokens_per_head=4,3,2,2,2 \
--compile \
--compile_mode=reduce-overhead
```
##### batch_size=1 (compile)
```bash
MODEL_PATH=/path/to/instructlab/granite-7b-lab
python fms-extras/scripts/paged_speculative_inference.py \
--variant=7b.ibm_instruct_lab \
--model_path=$MODEL_PATH \
--model_source=hf \
--tokenizer=$MODEL_PATH \
--speculator_path=ibm/granite-7b-lab-accelerator \
--speculator_source=hf \
--speculator_variant=1_4b \
--top_k_tokens_per_head=4,3,2,2,2 \
--compile
```
##### batch_size=4 (compile)
```bash
MODEL_PATH=/path/to/instructlab/granite-7b-lab
python fms-extras/scripts/paged_speculative_inference.py \
--variant=7b.ibm_instruct_lab \
--model_path=$MODEL_PATH \
--model_source=hf \
--tokenizer=$MODEL_PATH \
--speculator_path=ibm/granite-7b-lab-accelerator \
--speculator_source=hf \
--speculator_variant=1_4b \
--top_k_tokens_per_head=4,3,2,2,2 \
--batch_input \
--compile
```
|
basakdemirok/bert-base-turkish-cased-subjectivity_v02_seed42
|
basakdemirok
| 2024-05-21T13:30:33Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:dbmdz/bert-base-turkish-cased",
"base_model:finetune:dbmdz/bert-base-turkish-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-21T13:29:10Z |
---
license: mit
base_model: dbmdz/bert-base-turkish-cased
tags:
- generated_from_keras_callback
model-index:
- name: basakdemirok/bert-base-turkish-cased-subjectivity_v02_seed42
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# basakdemirok/bert-base-turkish-cased-subjectivity_v02_seed42
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6137
- Validation Loss: 0.4839
- Train F1: 0.7273
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 396, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train F1 | Epoch |
|:----------:|:---------------:|:--------:|:-----:|
| 0.6137 | 0.4839 | 0.7273 | 0 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.13.1
- Datasets 2.4.0
- Tokenizers 0.13.3
|
Fetanos/Reinforce-Pixelcopter-PLE-v0
|
Fetanos
| 2024-05-21T13:29:28Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-05-14T14:43:22Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 9.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
blockblockblock/Llama-3-70B-Instruct-abliterated-v3-bpw4-exl2
|
blockblockblock
| 2024-05-21T13:28:29Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"exl2",
"region:us"
] |
text-generation
| 2024-05-21T13:24:18Z |
---
library_name: transformers
license: llama3
---
# Llama-3-70B-Instruct-abliterated-v3 Model Card
[My Jupyter "cookbook" to replicate the methodology can be found here, refined library coming soon](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb)
This is [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more.
## Hang on, "abliteration"? Orthogonalization? Ablation? What is this?
TL;DR: This model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal directions orthogonalized out.
**TL;TL;DR;DR: It's uncensored in the purest form I can manage -- no new or changed behaviour in any other respect from the original model.**
As far as "abliteration": it's just a fun play-on-words using the original "ablation" term used in the original paper to refer to removing features, which I made up particularly to differentiate the model from "uncensored" fine-tunes.
Ablate + obliterated = Abliterated
Anyways, orthogonalization/ablation are both aspects to refer to the same thing here, the technique in which the refusal feature was "ablated" from the model was via orthogonalization.
## A little more on the methodology, and why this is interesting
To me, ablation (or applying the methodology for the inverse, "augmentation") seems to be good for inducing/removing very specific features that you'd have to spend way too many tokens on encouraging or discouraging in your system prompt.
Instead, you just apply your system prompt in the ablation script against a blank system prompt on the same dataset and orthogonalize for the desired behaviour in the final model weights.
> Why this over fine-tuning?
Ablation is much more surgical in nature whilst also being effectively executed with a _lot_ less data than fine-tuning, which I think is its main advantage.
As well, and its most valuable aspect is it keeps as much of the original model's knowledge and training intact, whilst removing its tendency to behave in one very specific undesireable manner. (In this case, refusing user requests.)
Fine tuning is still exceptionally useful and the go-to for broad behaviour changes; however, you may be able to get close to your desired behaviour with very few samples using the ablation/augmentation techniques.
It may also be a useful step to add to your model refinement: orthogonalize -> fine-tune or vice-versa.
I haven't really gotten around to exploring this model stacked with fine-tuning, I encourage others to give it a shot if they've got the capacity.
> Okay, fine, but why V3? There's no V2 70B?
Well, I released a V2 a while back for 8B under Cognitive Computations.
It ended up being not worth it to try V2 with 70B, I wanted to refine the model before wasting compute cycles on what might not even be a better model.
I am however quite pleased about this latest methodology, it seems to have induced fewer hallucinations.
So to show that it's a new fancy methodology from even that of the 8B V2, I decided to do a Microsoft and double up on my version jump because it's *such* an advancement (or so the excuse went, when in actuality it was because too many legacy but actively used Microsoft libraries checked for 'Windows 9' in the OS name to detect Windows 95/98 as one.)
## Quirkiness awareness notice
This model may come with interesting quirks, with the methodology being so new. I encourage you to play with the model, and post any quirks you notice in the community tab, as that'll help us further understand what this orthogonalization has in the way of side effects.
If you manage to develop further improvements, please share! This is really the most basic way to use ablation, but there are other possibilities that I believe are as-yet unexplored.
Additionally, feel free to reach out in any way about this. I'm on the Cognitive Computations Discord, I'm watching the Community tab, reach out! I'd love to see this methodology used in other ways, and so would gladly support whoever whenever I can.
|
ArpitaAeries/Mixtral_Alpace_v2
|
ArpitaAeries
| 2024-05-21T13:27:30Z | 1 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mixtral-8x7B-v0.1",
"base_model:adapter:mistralai/Mixtral-8x7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-05-21T05:36:17Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: mistralai/Mixtral-8x7B-v0.1
model-index:
- name: Mixtral_Alpace_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mixtral_Alpace_v2
This model is a fine-tuned version of [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1218
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.03
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0253 | 0.1389 | 5 | 2.0089 |
| 1.94 | 0.2778 | 10 | 1.9158 |
| 1.8034 | 0.4167 | 15 | 1.7803 |
| 1.6579 | 0.5556 | 20 | 1.6171 |
| 1.5197 | 0.6944 | 25 | 1.4960 |
| 1.4063 | 0.8333 | 30 | 1.3825 |
| 1.3019 | 0.9722 | 35 | 1.2824 |
| 1.2145 | 1.1111 | 40 | 1.2010 |
| 1.1549 | 1.25 | 45 | 1.1446 |
| 1.1269 | 1.3889 | 50 | 1.1218 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Zoyd/TIGER-Lab_MAmmoTH2-7B-8_0bpw_exl2
|
Zoyd
| 2024-05-21T13:25:17Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"arxiv:2405.03548",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"exl2",
"region:us"
] |
text-generation
| 2024-05-21T13:20:16Z |
---
license: mit
language:
- en
---
**Exllamav2** quant (**exl2** / **8.0 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-2_2bpw_exl2)**</center> | <center>2200 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-2_5bpw_exl2)**</center> | <center>2429 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-3_0bpw_exl2)**</center> | <center>2843 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-3_5bpw_exl2)**</center> | <center>3260 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-3_75bpw_exl2)**</center> | <center>3469 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-4_0bpw_exl2)**</center> | <center>3677 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-4_25bpw_exl2)**</center> | <center>3885 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-5_0bpw_exl2)**</center> | <center>4504 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-6_0bpw_exl2)**</center> | <center>5366 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-6_5bpw_exl2)**</center> | <center>5778 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-8_0bpw_exl2)**</center> | <center>6690 MB</center> | <center>8</center> |
# 🦣 MAmmoTH2: Scaling Instructions from the Web
Project Page: [https://tiger-ai-lab.github.io/MAmmoTH2/](https://tiger-ai-lab.github.io/MAmmoTH2/)
Paper: [https://arxiv.org/pdf/2405.03548](https://arxiv.org/pdf/2405.03548)
Code: [https://github.com/TIGER-AI-Lab/MAmmoTH2](https://github.com/TIGER-AI-Lab/MAmmoTH2)
## Introduction
Introducing 🦣 MAmmoTH2, a game-changer in improving the reasoning abilities of large language models (LLMs) through innovative instruction tuning. By efficiently harvesting 10 million instruction-response pairs from the pre-training web corpus, we've developed MAmmoTH2 models that significantly boost performance on reasoning benchmarks. For instance, MAmmoTH2-7B (Mistral) sees its performance soar from 11% to 34% on MATH and from 36% to 67% on GSM8K, all without training on any domain-specific data. Further training on public instruction tuning datasets yields MAmmoTH2-Plus, setting new standards in reasoning and chatbot benchmarks. Our work presents a cost-effective approach to acquiring large-scale, high-quality instruction data, offering a fresh perspective on enhancing LLM reasoning abilities.
| | **Base Model** | **MAmmoTH2** | **MAmmoTH2-Plus** |
|:-----|:---------------------|:-------------------------------------------------------------------|:------------------------------------------------------------------|
| 7B | Mistral | 🦣 [MAmmoTH2-7B](https://huggingface.co/TIGER-Lab/MAmmoTH2-7B) | 🦣 [MAmmoTH2-7B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-7B-Plus) |
| 8B | Llama-3 | 🦣 [MAmmoTH2-8B](https://huggingface.co/TIGER-Lab/MAmmoTH2-8B) | 🦣 [MAmmoTH2-8B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-8B-Plus) |
| 8x7B | Mixtral | 🦣 [MAmmoTH2-8x7B](https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B) | 🦣 [MAmmoTH2-8x7B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B-Plus) |
## Training Data
Please refer to https://huggingface.co/datasets/TIGER-Lab/WebInstructSub for more details.
## Training Procedure
The models are fine-tuned with the WEBINSTRUCT dataset using the original Llama-3, Mistral and Mistal models as base models. The training procedure varies for different models based on their sizes. Check out our paper for more details.
## Evaluation
The models are evaluated using open-ended and multiple-choice math problems from several datasets. Here are the results:
| **Model** | **TheoremQA** | **MATH** | **GSM8K** | **GPQA** | **MMLU-ST** | **BBH** | **ARC-C** | **Avg** |
|:---------------------------------------|:--------------|:---------|:----------|:---------|:------------|:--------|:----------|:--------|
| **MAmmoTH2-7B** (Updated) | 29.0 | 36.7 | 68.4 | 32.4 | 62.4 | 58.6 | 81.7 | 52.7 |
| **MAmmoTH2-8B** (Updated) | 30.3 | 35.8 | 70.4 | 35.2 | 64.2 | 62.1 | 82.2 | 54.3 |
| **MAmmoTH2-8x7B** | 32.2 | 39.0 | 75.4 | 36.8 | 67.4 | 71.1 | 87.5 | 58.9 |
| **MAmmoTH2-7B-Plus** (Updated) | 31.2 | 46.0 | 84.6 | 33.8 | 63.8 | 63.3 | 84.4 | 58.1 |
| **MAmmoTH2-8B-Plus** (Updated) | 31.5 | 43.0 | 85.2 | 35.8 | 66.7 | 69.7 | 84.3 | 59.4 |
| **MAmmoTH2-8x7B-Plus** | 34.1 | 47.0 | 86.4 | 37.8 | 72.4 | 74.1 | 88.4 | 62.9 |
To reproduce our results, please refer to https://github.com/TIGER-AI-Lab/MAmmoTH2/tree/main/math_eval.
## Usage
You can use the models through Huggingface's Transformers library. Use the pipeline function to create a text-generation pipeline with the model of your choice, then feed in a math problem to get the solution.
Check our Github repo for more advanced use: https://github.com/TIGER-AI-Lab/MAmmoTH2
## Limitations
We've tried our best to build math generalist models. However, we acknowledge that the models' performance may vary based on the complexity and specifics of the math problem. Still not all mathematical fields can be covered comprehensively.
## Citation
If you use the models, data, or code from this project, please cite the original paper:
```
@article{yue2024mammoth2,
title={MAmmoTH2: Scaling Instructions from the Web},
author={Yue, Xiang and Zheng, Tuney and Zhang, Ge and Chen, Wenhu},
journal={arXiv preprint arXiv:2405.03548},
year={2024}
}
```
|
Zoyd/TIGER-Lab_MAmmoTH2-7B-4_0bpw_exl2
|
Zoyd
| 2024-05-21T13:20:32Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"arxiv:2405.03548",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"exl2",
"region:us"
] |
text-generation
| 2024-05-21T12:42:13Z |
---
license: mit
language:
- en
---
**Exllamav2** quant (**exl2** / **4.0 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-2_2bpw_exl2)**</center> | <center>2200 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-2_5bpw_exl2)**</center> | <center>2429 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-3_0bpw_exl2)**</center> | <center>2843 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-3_5bpw_exl2)**</center> | <center>3260 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-3_75bpw_exl2)**</center> | <center>3469 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-4_0bpw_exl2)**</center> | <center>3677 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-4_25bpw_exl2)**</center> | <center>3885 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-5_0bpw_exl2)**</center> | <center>4504 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-6_0bpw_exl2)**</center> | <center>5366 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-6_5bpw_exl2)**</center> | <center>5778 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-8_0bpw_exl2)**</center> | <center>6690 MB</center> | <center>8</center> |
# 🦣 MAmmoTH2: Scaling Instructions from the Web
Project Page: [https://tiger-ai-lab.github.io/MAmmoTH2/](https://tiger-ai-lab.github.io/MAmmoTH2/)
Paper: [https://arxiv.org/pdf/2405.03548](https://arxiv.org/pdf/2405.03548)
Code: [https://github.com/TIGER-AI-Lab/MAmmoTH2](https://github.com/TIGER-AI-Lab/MAmmoTH2)
## Introduction
Introducing 🦣 MAmmoTH2, a game-changer in improving the reasoning abilities of large language models (LLMs) through innovative instruction tuning. By efficiently harvesting 10 million instruction-response pairs from the pre-training web corpus, we've developed MAmmoTH2 models that significantly boost performance on reasoning benchmarks. For instance, MAmmoTH2-7B (Mistral) sees its performance soar from 11% to 34% on MATH and from 36% to 67% on GSM8K, all without training on any domain-specific data. Further training on public instruction tuning datasets yields MAmmoTH2-Plus, setting new standards in reasoning and chatbot benchmarks. Our work presents a cost-effective approach to acquiring large-scale, high-quality instruction data, offering a fresh perspective on enhancing LLM reasoning abilities.
| | **Base Model** | **MAmmoTH2** | **MAmmoTH2-Plus** |
|:-----|:---------------------|:-------------------------------------------------------------------|:------------------------------------------------------------------|
| 7B | Mistral | 🦣 [MAmmoTH2-7B](https://huggingface.co/TIGER-Lab/MAmmoTH2-7B) | 🦣 [MAmmoTH2-7B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-7B-Plus) |
| 8B | Llama-3 | 🦣 [MAmmoTH2-8B](https://huggingface.co/TIGER-Lab/MAmmoTH2-8B) | 🦣 [MAmmoTH2-8B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-8B-Plus) |
| 8x7B | Mixtral | 🦣 [MAmmoTH2-8x7B](https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B) | 🦣 [MAmmoTH2-8x7B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B-Plus) |
## Training Data
Please refer to https://huggingface.co/datasets/TIGER-Lab/WebInstructSub for more details.
## Training Procedure
The models are fine-tuned with the WEBINSTRUCT dataset using the original Llama-3, Mistral and Mistal models as base models. The training procedure varies for different models based on their sizes. Check out our paper for more details.
## Evaluation
The models are evaluated using open-ended and multiple-choice math problems from several datasets. Here are the results:
| **Model** | **TheoremQA** | **MATH** | **GSM8K** | **GPQA** | **MMLU-ST** | **BBH** | **ARC-C** | **Avg** |
|:---------------------------------------|:--------------|:---------|:----------|:---------|:------------|:--------|:----------|:--------|
| **MAmmoTH2-7B** (Updated) | 29.0 | 36.7 | 68.4 | 32.4 | 62.4 | 58.6 | 81.7 | 52.7 |
| **MAmmoTH2-8B** (Updated) | 30.3 | 35.8 | 70.4 | 35.2 | 64.2 | 62.1 | 82.2 | 54.3 |
| **MAmmoTH2-8x7B** | 32.2 | 39.0 | 75.4 | 36.8 | 67.4 | 71.1 | 87.5 | 58.9 |
| **MAmmoTH2-7B-Plus** (Updated) | 31.2 | 46.0 | 84.6 | 33.8 | 63.8 | 63.3 | 84.4 | 58.1 |
| **MAmmoTH2-8B-Plus** (Updated) | 31.5 | 43.0 | 85.2 | 35.8 | 66.7 | 69.7 | 84.3 | 59.4 |
| **MAmmoTH2-8x7B-Plus** | 34.1 | 47.0 | 86.4 | 37.8 | 72.4 | 74.1 | 88.4 | 62.9 |
To reproduce our results, please refer to https://github.com/TIGER-AI-Lab/MAmmoTH2/tree/main/math_eval.
## Usage
You can use the models through Huggingface's Transformers library. Use the pipeline function to create a text-generation pipeline with the model of your choice, then feed in a math problem to get the solution.
Check our Github repo for more advanced use: https://github.com/TIGER-AI-Lab/MAmmoTH2
## Limitations
We've tried our best to build math generalist models. However, we acknowledge that the models' performance may vary based on the complexity and specifics of the math problem. Still not all mathematical fields can be covered comprehensively.
## Citation
If you use the models, data, or code from this project, please cite the original paper:
```
@article{yue2024mammoth2,
title={MAmmoTH2: Scaling Instructions from the Web},
author={Yue, Xiang and Zheng, Tuney and Zhang, Ge and Chen, Wenhu},
journal={arXiv preprint arXiv:2405.03548},
year={2024}
}
```
|
Zoyd/TIGER-Lab_MAmmoTH2-7B-3_75bpw_exl2
|
Zoyd
| 2024-05-21T13:20:32Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"arxiv:2405.03548",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-05-21T12:34:38Z |
---
license: mit
language:
- en
---
**Exllamav2** quant (**exl2** / **3.75 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-2_2bpw_exl2)**</center> | <center>2200 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-2_5bpw_exl2)**</center> | <center>2429 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-3_0bpw_exl2)**</center> | <center>2843 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-3_5bpw_exl2)**</center> | <center>3260 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-3_75bpw_exl2)**</center> | <center>3469 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-4_0bpw_exl2)**</center> | <center>3677 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-4_25bpw_exl2)**</center> | <center>3885 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-5_0bpw_exl2)**</center> | <center>4504 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-6_0bpw_exl2)**</center> | <center>5366 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-6_5bpw_exl2)**</center> | <center>5778 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-8_0bpw_exl2)**</center> | <center>6690 MB</center> | <center>8</center> |
# 🦣 MAmmoTH2: Scaling Instructions from the Web
Project Page: [https://tiger-ai-lab.github.io/MAmmoTH2/](https://tiger-ai-lab.github.io/MAmmoTH2/)
Paper: [https://arxiv.org/pdf/2405.03548](https://arxiv.org/pdf/2405.03548)
Code: [https://github.com/TIGER-AI-Lab/MAmmoTH2](https://github.com/TIGER-AI-Lab/MAmmoTH2)
## Introduction
Introducing 🦣 MAmmoTH2, a game-changer in improving the reasoning abilities of large language models (LLMs) through innovative instruction tuning. By efficiently harvesting 10 million instruction-response pairs from the pre-training web corpus, we've developed MAmmoTH2 models that significantly boost performance on reasoning benchmarks. For instance, MAmmoTH2-7B (Mistral) sees its performance soar from 11% to 34% on MATH and from 36% to 67% on GSM8K, all without training on any domain-specific data. Further training on public instruction tuning datasets yields MAmmoTH2-Plus, setting new standards in reasoning and chatbot benchmarks. Our work presents a cost-effective approach to acquiring large-scale, high-quality instruction data, offering a fresh perspective on enhancing LLM reasoning abilities.
| | **Base Model** | **MAmmoTH2** | **MAmmoTH2-Plus** |
|:-----|:---------------------|:-------------------------------------------------------------------|:------------------------------------------------------------------|
| 7B | Mistral | 🦣 [MAmmoTH2-7B](https://huggingface.co/TIGER-Lab/MAmmoTH2-7B) | 🦣 [MAmmoTH2-7B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-7B-Plus) |
| 8B | Llama-3 | 🦣 [MAmmoTH2-8B](https://huggingface.co/TIGER-Lab/MAmmoTH2-8B) | 🦣 [MAmmoTH2-8B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-8B-Plus) |
| 8x7B | Mixtral | 🦣 [MAmmoTH2-8x7B](https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B) | 🦣 [MAmmoTH2-8x7B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B-Plus) |
## Training Data
Please refer to https://huggingface.co/datasets/TIGER-Lab/WebInstructSub for more details.
## Training Procedure
The models are fine-tuned with the WEBINSTRUCT dataset using the original Llama-3, Mistral and Mistal models as base models. The training procedure varies for different models based on their sizes. Check out our paper for more details.
## Evaluation
The models are evaluated using open-ended and multiple-choice math problems from several datasets. Here are the results:
| **Model** | **TheoremQA** | **MATH** | **GSM8K** | **GPQA** | **MMLU-ST** | **BBH** | **ARC-C** | **Avg** |
|:---------------------------------------|:--------------|:---------|:----------|:---------|:------------|:--------|:----------|:--------|
| **MAmmoTH2-7B** (Updated) | 29.0 | 36.7 | 68.4 | 32.4 | 62.4 | 58.6 | 81.7 | 52.7 |
| **MAmmoTH2-8B** (Updated) | 30.3 | 35.8 | 70.4 | 35.2 | 64.2 | 62.1 | 82.2 | 54.3 |
| **MAmmoTH2-8x7B** | 32.2 | 39.0 | 75.4 | 36.8 | 67.4 | 71.1 | 87.5 | 58.9 |
| **MAmmoTH2-7B-Plus** (Updated) | 31.2 | 46.0 | 84.6 | 33.8 | 63.8 | 63.3 | 84.4 | 58.1 |
| **MAmmoTH2-8B-Plus** (Updated) | 31.5 | 43.0 | 85.2 | 35.8 | 66.7 | 69.7 | 84.3 | 59.4 |
| **MAmmoTH2-8x7B-Plus** | 34.1 | 47.0 | 86.4 | 37.8 | 72.4 | 74.1 | 88.4 | 62.9 |
To reproduce our results, please refer to https://github.com/TIGER-AI-Lab/MAmmoTH2/tree/main/math_eval.
## Usage
You can use the models through Huggingface's Transformers library. Use the pipeline function to create a text-generation pipeline with the model of your choice, then feed in a math problem to get the solution.
Check our Github repo for more advanced use: https://github.com/TIGER-AI-Lab/MAmmoTH2
## Limitations
We've tried our best to build math generalist models. However, we acknowledge that the models' performance may vary based on the complexity and specifics of the math problem. Still not all mathematical fields can be covered comprehensively.
## Citation
If you use the models, data, or code from this project, please cite the original paper:
```
@article{yue2024mammoth2,
title={MAmmoTH2: Scaling Instructions from the Web},
author={Yue, Xiang and Zheng, Tuney and Zhang, Ge and Chen, Wenhu},
journal={arXiv preprint arXiv:2405.03548},
year={2024}
}
```
|
Zoyd/TIGER-Lab_MAmmoTH2-7B-3_5bpw_exl2
|
Zoyd
| 2024-05-21T13:20:31Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"arxiv:2405.03548",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-05-21T12:27:06Z |
---
license: mit
language:
- en
---
**Exllamav2** quant (**exl2** / **3.5 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-2_2bpw_exl2)**</center> | <center>2200 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-2_5bpw_exl2)**</center> | <center>2429 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-3_0bpw_exl2)**</center> | <center>2843 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-3_5bpw_exl2)**</center> | <center>3260 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-3_75bpw_exl2)**</center> | <center>3469 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-4_0bpw_exl2)**</center> | <center>3677 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-4_25bpw_exl2)**</center> | <center>3885 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-5_0bpw_exl2)**</center> | <center>4504 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-6_0bpw_exl2)**</center> | <center>5366 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-6_5bpw_exl2)**</center> | <center>5778 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-7B-8_0bpw_exl2)**</center> | <center>6690 MB</center> | <center>8</center> |
# 🦣 MAmmoTH2: Scaling Instructions from the Web
Project Page: [https://tiger-ai-lab.github.io/MAmmoTH2/](https://tiger-ai-lab.github.io/MAmmoTH2/)
Paper: [https://arxiv.org/pdf/2405.03548](https://arxiv.org/pdf/2405.03548)
Code: [https://github.com/TIGER-AI-Lab/MAmmoTH2](https://github.com/TIGER-AI-Lab/MAmmoTH2)
## Introduction
Introducing 🦣 MAmmoTH2, a game-changer in improving the reasoning abilities of large language models (LLMs) through innovative instruction tuning. By efficiently harvesting 10 million instruction-response pairs from the pre-training web corpus, we've developed MAmmoTH2 models that significantly boost performance on reasoning benchmarks. For instance, MAmmoTH2-7B (Mistral) sees its performance soar from 11% to 34% on MATH and from 36% to 67% on GSM8K, all without training on any domain-specific data. Further training on public instruction tuning datasets yields MAmmoTH2-Plus, setting new standards in reasoning and chatbot benchmarks. Our work presents a cost-effective approach to acquiring large-scale, high-quality instruction data, offering a fresh perspective on enhancing LLM reasoning abilities.
| | **Base Model** | **MAmmoTH2** | **MAmmoTH2-Plus** |
|:-----|:---------------------|:-------------------------------------------------------------------|:------------------------------------------------------------------|
| 7B | Mistral | 🦣 [MAmmoTH2-7B](https://huggingface.co/TIGER-Lab/MAmmoTH2-7B) | 🦣 [MAmmoTH2-7B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-7B-Plus) |
| 8B | Llama-3 | 🦣 [MAmmoTH2-8B](https://huggingface.co/TIGER-Lab/MAmmoTH2-8B) | 🦣 [MAmmoTH2-8B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-8B-Plus) |
| 8x7B | Mixtral | 🦣 [MAmmoTH2-8x7B](https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B) | 🦣 [MAmmoTH2-8x7B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B-Plus) |
## Training Data
Please refer to https://huggingface.co/datasets/TIGER-Lab/WebInstructSub for more details.
## Training Procedure
The models are fine-tuned with the WEBINSTRUCT dataset using the original Llama-3, Mistral and Mistal models as base models. The training procedure varies for different models based on their sizes. Check out our paper for more details.
## Evaluation
The models are evaluated using open-ended and multiple-choice math problems from several datasets. Here are the results:
| **Model** | **TheoremQA** | **MATH** | **GSM8K** | **GPQA** | **MMLU-ST** | **BBH** | **ARC-C** | **Avg** |
|:---------------------------------------|:--------------|:---------|:----------|:---------|:------------|:--------|:----------|:--------|
| **MAmmoTH2-7B** (Updated) | 29.0 | 36.7 | 68.4 | 32.4 | 62.4 | 58.6 | 81.7 | 52.7 |
| **MAmmoTH2-8B** (Updated) | 30.3 | 35.8 | 70.4 | 35.2 | 64.2 | 62.1 | 82.2 | 54.3 |
| **MAmmoTH2-8x7B** | 32.2 | 39.0 | 75.4 | 36.8 | 67.4 | 71.1 | 87.5 | 58.9 |
| **MAmmoTH2-7B-Plus** (Updated) | 31.2 | 46.0 | 84.6 | 33.8 | 63.8 | 63.3 | 84.4 | 58.1 |
| **MAmmoTH2-8B-Plus** (Updated) | 31.5 | 43.0 | 85.2 | 35.8 | 66.7 | 69.7 | 84.3 | 59.4 |
| **MAmmoTH2-8x7B-Plus** | 34.1 | 47.0 | 86.4 | 37.8 | 72.4 | 74.1 | 88.4 | 62.9 |
To reproduce our results, please refer to https://github.com/TIGER-AI-Lab/MAmmoTH2/tree/main/math_eval.
## Usage
You can use the models through Huggingface's Transformers library. Use the pipeline function to create a text-generation pipeline with the model of your choice, then feed in a math problem to get the solution.
Check our Github repo for more advanced use: https://github.com/TIGER-AI-Lab/MAmmoTH2
## Limitations
We've tried our best to build math generalist models. However, we acknowledge that the models' performance may vary based on the complexity and specifics of the math problem. Still not all mathematical fields can be covered comprehensively.
## Citation
If you use the models, data, or code from this project, please cite the original paper:
```
@article{yue2024mammoth2,
title={MAmmoTH2: Scaling Instructions from the Web},
author={Yue, Xiang and Zheng, Tuney and Zhang, Ge and Chen, Wenhu},
journal={arXiv preprint arXiv:2405.03548},
year={2024}
}
```
|
Felladrin/gguf-sharded-h2o-danube2-1.8b-chat
|
Felladrin
| 2024-05-21T13:18:53Z | 15 | 1 | null |
[
"gguf",
"base_model:h2oai/h2o-danube2-1.8b-chat",
"base_model:quantized:h2oai/h2o-danube2-1.8b-chat",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-21T13:15:03Z |
---
license: apache-2.0
base_model: h2oai/h2o-danube2-1.8b-chat
---
Sharded GGUF version of [h2oai/h2o-danube2-1.8b-chat](https://huggingface.co/h2oai/h2o-danube2-1.8b-chat).
|
premai-io/prem-1B
|
premai-io
| 2024-05-21T13:15:21Z | 462 | 5 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:cerebras/SlimPajama-627B",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:hkust-nlp/deita-10k-v0",
"dataset:Open-Orca/SlimOrca-Dedup",
"dataset:cognitivecomputations/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split",
"dataset:HuggingFaceH4/capybara",
"dataset:meta-math/MetaMathQA",
"dataset:argilla/ultrafeedback-binarized-preferences-cleaned",
"dataset:Intel/orca_dpo_pairs",
"dataset:alexredna/oasst2_dpo_pairs",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-03-07T11:48:52Z |
---
library_name: transformers
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- HuggingFaceH4/ultrachat_200k
- hkust-nlp/deita-10k-v0
- Open-Orca/SlimOrca-Dedup
- cognitivecomputations/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split
- HuggingFaceH4/capybara
- meta-math/MetaMathQA
- argilla/ultrafeedback-binarized-preferences-cleaned
- Intel/orca_dpo_pairs
- alexredna/oasst2_dpo_pairs
pipeline_tag: text-generation
---
## Model Details
With great enthusiasm, we unveil the Prem-1B series, open-source, multipurpose large language models developed by Prem AI. This cutting-edge SLM offers the open community and enterprises the opportunity to harness capabilities that were once exclusively available through closed model APIs, empowering them to build their own advanced language models. Our objective is to develop a model that excels at Retrieval-Augmented Generation (RAG). While Large Language Models (LLMs) store a vast amount of information within their parameters, RAG operates differently by ingesting information during runtime. This approach suggests that for RAG applications, we may not require models of immense size. With this initiative, we aim to create a Small Language Model (SLM) with an extended context length of 8192 tokens, enabling it to handle multi-turn conversations effectively. This endeavor represents our inaugural attempt to craft an SLM tailored for RAG tasks.
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** https://premai.io/
- **Model type:** Llama
- **Language(s) (NLP):** Python
- **License:** Apache License 2.0
## Uses
The Prem-1B language model is designed for commercial and research applications involving the English language. The instruction-tuned versions of the model are tailored for conversational interactions akin to a virtual assistant. On the other hand, the pretrained variants can be fine-tuned and adapted for various natural language generation tasks beyond just dialogue.
### Out-of-Scope Use
The model must not be used in any manner that violates applicable laws or regulations, including trade compliance laws. It is also prohibited to use the model in any way that goes against the Acceptable Use Policy and the Prem-1B Community License. While the base model is intended for English language use, developers are permitted to fine-tune the Prem-1B models for other languages, provided they comply with the Prem-1B Community License and the Acceptable Use Policy.
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Using `AutoModelForCausalLM` and `AutoTokenizer`
```py
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("premai-io/prem-1B-chat")
model = AutoModelForCausalLM.from_pretrained('premai-io/prem-1B-chat', torch_dtype=torch.bfloat16)
model = model.to('cuda')
# Setup terminators
terminators = [tokenizer.eos_token_id, tokenizer.encode('<|eot_id|>', add_special_tokens=False)[0]]
# Prepare the prompt
messages = [
{
"role": "system",
"content": "You are a helpful AI assistant. You should give concise responses to very simple questions, but provide thorough responses to more complex and open-ended questions."
},
{
'role': 'user',
'content': 'Help me understand machine learning.'
}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# Generate
inputs = tokenizer(prompt, return_attention_mask=False, return_tensors="pt", add_special_tokens=False)
input_ids = inputs['input_ids']
input_ids = input_ids.to(model.device)
res = model.generate(input_ids=input_ids, max_new_tokens=400, pad_token_id=tokenizer.pad_token_id, eos_token_id=terminators)
generated_text = tokenizer.decode(res[0][input_ids.shape[1]:], skip_special_tokens=True).strip()
print(generated_text)
```
Using pipelines:
```py
import torch
from transformers import pipeline
# Load the pipeline
pipe = pipeline("text-generation", model="premai-io/prem-1B-chat", torch_dtype=torch.bfloat16, device=0)
# Prepare prompt
messages = [
{
"role": "system",
"content": "You are a helpful AI assistant. You should give concise responses to very simple questions, but provide thorough responses to more complex and open-ended questions."
},
{
'role': 'user',
'content': 'Help me understand machine learning.'
}
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# Setup terminators
terminators = [pipe.tokenizer.eos_token_id, pipe.tokenizer.encode('<|eot_id|>', add_special_tokens=False)[0]]
# Generate
outputs = pipe(prompt, max_new_tokens=400, do_sample=True, temperature=0.7, top_k=50, top_p=0.95, pad_token_id=pipe.tokenizer.pad_token_id, eos_token_id=terminators)
print(outputs[0]["generated_text"][len(prompt):])
```
## Training Details
### Training Data
Mentioned in blogpost: https://blog.premai.io/introducing-prem-1b/
### Training Procedure
Mentioned in blogpost: https://blog.premai.io/introducing-prem-1b/
#### Training Hyperparameters
Mentioned in blogpost: https://blog.premai.io/introducing-prem-1b/
## Evaluation
### Results
|Model |Avg |Arc-c|Arc-e|Hellaswag|MMLU |Obqa |Piqa |Winogrande|
|------------------------|-----|-----|-----|---------|-----|-----|-----|----------|
|prem-1B |42.64|24.74|57.40|42.01 |24.75|21.00|72.14|56.43 |
|prem-1B-chat |41.76|24.48|53.32|40.28 |25.27|22.20|70.89|55.88 |
|TinyLlama-1.1B-Chat-v1.0|46.16|30.03|61.53|46.56 |24.72|25.80|74.21|60.29 |
|opt-1.3b |42.94|23.37|57.44|41.49 |24.86|23.20|71.49|58.72 |
|pythia-1b |40.71|24.31|56.90|37.72 |23.20|18.80|70.62|53.43 |
## Environmental Impact
- **Hardware Type:** H100 GPUs
- **Hours used:** 8500
### Model Architecture and Objective
Llama based
### Compute Infrastructure
16-H100 GPUs
#### Hardware
H100 GPUs
#### Software
PyTorch, transformers, PyTorch Lightning
## Citation
https://blog.premai.io/introducing-prem-1b/
## Model Card Authors
https://huggingface.co/goku, https://huggingface.co/nsosio, https://huggingface.co/ucalyptus, https://huggingface.co/filopedraz
## Model Card Contact
https://huggingface.co/goku, https://huggingface.co/nsosio, https://huggingface.co/ucalyptus, https://huggingface.co/filopedraz
|
thowley824/shape_long_window_2_labels_scar
|
thowley824
| 2024-05-21T13:12:28Z | 181 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-21T13:11:58Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
hgnoi/VGbiYcFWgN8dCI4T
|
hgnoi
| 2024-05-21T13:11:46Z | 121 | 0 |
transformers
|
[
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-21T13:10:13Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
NikolayKozloff/phi3-128k-6b-Q8_0-GGUF
|
NikolayKozloff
| 2024-05-21T13:08:17Z | 1 | 2 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-21T13:08:00Z |
---
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
base_model: []
---
# NikolayKozloff/phi3-128k-6b-Q8_0-GGUF
This model was converted to GGUF format from [`win10/phi3-128k-6b`](https://huggingface.co/win10/phi3-128k-6b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/win10/phi3-128k-6b) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo NikolayKozloff/phi3-128k-6b-Q8_0-GGUF --model phi3-128k-6b.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo NikolayKozloff/phi3-128k-6b-Q8_0-GGUF --model phi3-128k-6b.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m phi3-128k-6b.Q8_0.gguf -n 128
```
|
hgnoi/BA6abijA92TfaMLP
|
hgnoi
| 2024-05-21T13:06:18Z | 121 | 0 |
transformers
|
[
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-21T13:04:50Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
falan42/Gemma-2b-int4-SODA_mark4
|
falan42
| 2024-05-21T13:04:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-2b-bnb-4bit",
"base_model:finetune:unsloth/gemma-2b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-21T13:04:16Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
base_model: unsloth/gemma-2b-bnb-4bit
---
# Uploaded model
- **Developed by:** emir12
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-bnb-4bit
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Kinopiko01/Kitana
|
Kinopiko01
| 2024-05-21T12:59:52Z | 0 | 0 | null |
[
"license:openrail++",
"region:us"
] | null | 2024-05-12T09:49:26Z |
---
license: openrail++
---
|
dgrachev/Taxi-v3
|
dgrachev
| 2024-05-21T12:59:33Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-05-21T12:59:31Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.69
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="dgrachev/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
MaziyarPanahi/CalmexperimentMeliodas-7B-GGUF
|
MaziyarPanahi
| 2024-05-21T12:58:19Z | 97 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:allknowingroger/CalmExperiment-7B-slerp",
"base_model:AurelPx/Meliodas-7b-dare",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:automerger/CalmexperimentMeliodas-7B",
"base_model:quantized:automerger/CalmexperimentMeliodas-7B"
] |
text-generation
| 2024-05-21T12:29:20Z |
---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- merge
- mergekit
- lazymergekit
- automerger
- base_model:allknowingroger/CalmExperiment-7B-slerp
- base_model:AurelPx/Meliodas-7b-dare
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
- text-generation
model_name: CalmexperimentMeliodas-7B-GGUF
base_model: automerger/CalmexperimentMeliodas-7B
inference: false
model_creator: automerger
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/CalmexperimentMeliodas-7B-GGUF](https://huggingface.co/MaziyarPanahi/CalmexperimentMeliodas-7B-GGUF)
- Model creator: [automerger](https://huggingface.co/automerger)
- Original model: [automerger/CalmexperimentMeliodas-7B](https://huggingface.co/automerger/CalmexperimentMeliodas-7B)
## Description
[MaziyarPanahi/CalmexperimentMeliodas-7B-GGUF](https://huggingface.co/MaziyarPanahi/CalmexperimentMeliodas-7B-GGUF) contains GGUF format model files for [automerger/CalmexperimentMeliodas-7B](https://huggingface.co/automerger/CalmexperimentMeliodas-7B).
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
|
dgrachev/q-FrozenLake-v1-4x4-noSlippery
|
dgrachev
| 2024-05-21T12:57:33Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-05-21T12:57:30Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="dgrachev/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
aghiles-s/new_repo_last
|
aghiles-s
| 2024-05-21T12:57:27Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-21T12:56:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: new_repo_last
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# new_repo_last
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.30.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.13.3
|
comet24082002/continual_finetune_bge_st_V1
|
comet24082002
| 2024-05-21T12:55:49Z | 9 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-05-21T12:34:37Z |
---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# comet24082002/continual_finetune_bge_st_V1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
Continual Training Phase:
1. SimSCE pretraining
2. MultipleNegativeRankingLoss Sentence Transformer finetuning
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('comet24082002/continual_finetune_bge_st_V1')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=comet24082002/continual_finetune_bge_st_V1)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 659 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 659,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Labib11/MUG-B-1.6
|
Labib11
| 2024-05-21T12:54:58Z | 2,170 | 2 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-05-08T15:46:01Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: MUG-B-1.6
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en-ext)
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 74.04047976011994
- type: ap
value: 23.622442298323236
- type: f1
value: 61.681362134359354
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 72.38805970149255
- type: ap
value: 35.14527522183942
- type: f1
value: 66.40004634079556
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (de)
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 54.3254817987152
- type: ap
value: 71.95259605308317
- type: f1
value: 52.50731386267296
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (ja)
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 56.33832976445397
- type: ap
value: 12.671021199223937
- type: f1
value: 46.127586182990605
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.70805000000001
- type: ap
value: 90.58639913354553
- type: f1
value: 93.69822635061847
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 50.85000000000001
- type: f1
value: 49.80013009020246
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (de)
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 27.203999999999994
- type: f1
value: 26.60134413072989
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (es)
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 34.878
- type: f1
value: 33.072592092252314
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 31.557999999999993
- type: f1
value: 30.866094552542624
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (ja)
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 22.706
- type: f1
value: 22.23195837325246
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 22.349999999999998
- type: f1
value: 21.80183891680617
- task:
type: Retrieval
dataset:
type: mteb/arguana
name: MTEB ArguAna
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 41.892
- type: map_at_10
value: 57.989999999999995
- type: map_at_100
value: 58.45
- type: map_at_1000
value: 58.453
- type: map_at_20
value: 58.392999999999994
- type: map_at_3
value: 53.746
- type: map_at_5
value: 56.566
- type: mrr_at_1
value: 43.314
- type: mrr_at_10
value: 58.535000000000004
- type: mrr_at_100
value: 58.975
- type: mrr_at_1000
value: 58.977999999999994
- type: mrr_at_20
value: 58.916999999999994
- type: mrr_at_3
value: 54.303000000000004
- type: mrr_at_5
value: 57.055
- type: ndcg_at_1
value: 41.892
- type: ndcg_at_10
value: 66.176
- type: ndcg_at_100
value: 67.958
- type: ndcg_at_1000
value: 68.00699999999999
- type: ndcg_at_20
value: 67.565
- type: ndcg_at_3
value: 57.691
- type: ndcg_at_5
value: 62.766
- type: precision_at_1
value: 41.892
- type: precision_at_10
value: 9.189
- type: precision_at_100
value: 0.993
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.861
- type: precision_at_3
value: 23.044
- type: precision_at_5
value: 16.287
- type: recall_at_1
value: 41.892
- type: recall_at_10
value: 91.892
- type: recall_at_100
value: 99.289
- type: recall_at_1000
value: 99.644
- type: recall_at_20
value: 97.226
- type: recall_at_3
value: 69.132
- type: recall_at_5
value: 81.437
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 49.03486273664411
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 43.04797567338598
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 64.29499572176032
- type: mrr
value: 77.28861627753592
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 89.53248242133246
- type: cos_sim_spearman
value: 88.38032705871927
- type: euclidean_pearson
value: 87.77994445569084
- type: euclidean_spearman
value: 88.38032705871927
- type: manhattan_pearson
value: 87.52369210088627
- type: manhattan_spearman
value: 88.27972235673434
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 85.4090909090909
- type: f1
value: 84.87743757972068
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.73840151083438
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 36.565075977998966
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-android
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 33.082
- type: map_at_10
value: 44.787
- type: map_at_100
value: 46.322
- type: map_at_1000
value: 46.446
- type: map_at_20
value: 45.572
- type: map_at_3
value: 40.913
- type: map_at_5
value: 42.922
- type: mrr_at_1
value: 40.629
- type: mrr_at_10
value: 51.119
- type: mrr_at_100
value: 51.783
- type: mrr_at_1000
value: 51.82
- type: mrr_at_20
value: 51.49700000000001
- type: mrr_at_3
value: 48.355
- type: mrr_at_5
value: 49.979
- type: ndcg_at_1
value: 40.629
- type: ndcg_at_10
value: 51.647
- type: ndcg_at_100
value: 56.923
- type: ndcg_at_1000
value: 58.682
- type: ndcg_at_20
value: 53.457
- type: ndcg_at_3
value: 46.065
- type: ndcg_at_5
value: 48.352000000000004
- type: precision_at_1
value: 40.629
- type: precision_at_10
value: 10.072000000000001
- type: precision_at_100
value: 1.5939999999999999
- type: precision_at_1000
value: 0.20600000000000002
- type: precision_at_20
value: 5.908
- type: precision_at_3
value: 22.222
- type: precision_at_5
value: 15.937000000000001
- type: recall_at_1
value: 33.082
- type: recall_at_10
value: 64.55300000000001
- type: recall_at_100
value: 86.86399999999999
- type: recall_at_1000
value: 97.667
- type: recall_at_20
value: 70.988
- type: recall_at_3
value: 48.067
- type: recall_at_5
value: 54.763
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-english
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 32.272
- type: map_at_10
value: 42.620000000000005
- type: map_at_100
value: 43.936
- type: map_at_1000
value: 44.066
- type: map_at_20
value: 43.349
- type: map_at_3
value: 39.458
- type: map_at_5
value: 41.351
- type: mrr_at_1
value: 40.127
- type: mrr_at_10
value: 48.437000000000005
- type: mrr_at_100
value: 49.096000000000004
- type: mrr_at_1000
value: 49.14
- type: mrr_at_20
value: 48.847
- type: mrr_at_3
value: 46.21
- type: mrr_at_5
value: 47.561
- type: ndcg_at_1
value: 40.127
- type: ndcg_at_10
value: 48.209999999999994
- type: ndcg_at_100
value: 52.632
- type: ndcg_at_1000
value: 54.59
- type: ndcg_at_20
value: 50.012
- type: ndcg_at_3
value: 43.996
- type: ndcg_at_5
value: 46.122
- type: precision_at_1
value: 40.127
- type: precision_at_10
value: 9.051
- type: precision_at_100
value: 1.465
- type: precision_at_1000
value: 0.193
- type: precision_at_20
value: 5.35
- type: precision_at_3
value: 21.104
- type: precision_at_5
value: 15.146
- type: recall_at_1
value: 32.272
- type: recall_at_10
value: 57.870999999999995
- type: recall_at_100
value: 76.211
- type: recall_at_1000
value: 88.389
- type: recall_at_20
value: 64.354
- type: recall_at_3
value: 45.426
- type: recall_at_5
value: 51.23799999999999
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-gaming
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 40.261
- type: map_at_10
value: 53.400000000000006
- type: map_at_100
value: 54.42399999999999
- type: map_at_1000
value: 54.473000000000006
- type: map_at_20
value: 54.052
- type: map_at_3
value: 49.763000000000005
- type: map_at_5
value: 51.878
- type: mrr_at_1
value: 46.019
- type: mrr_at_10
value: 56.653
- type: mrr_at_100
value: 57.28
- type: mrr_at_1000
value: 57.303000000000004
- type: mrr_at_20
value: 57.057
- type: mrr_at_3
value: 53.971000000000004
- type: mrr_at_5
value: 55.632000000000005
- type: ndcg_at_1
value: 46.019
- type: ndcg_at_10
value: 59.597
- type: ndcg_at_100
value: 63.452
- type: ndcg_at_1000
value: 64.434
- type: ndcg_at_20
value: 61.404
- type: ndcg_at_3
value: 53.620999999999995
- type: ndcg_at_5
value: 56.688
- type: precision_at_1
value: 46.019
- type: precision_at_10
value: 9.748999999999999
- type: precision_at_100
value: 1.261
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_20
value: 5.436
- type: precision_at_3
value: 24.075
- type: precision_at_5
value: 16.715
- type: recall_at_1
value: 40.261
- type: recall_at_10
value: 74.522
- type: recall_at_100
value: 91.014
- type: recall_at_1000
value: 98.017
- type: recall_at_20
value: 81.186
- type: recall_at_3
value: 58.72500000000001
- type: recall_at_5
value: 66.23599999999999
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-gis
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 27.666
- type: map_at_10
value: 36.744
- type: map_at_100
value: 37.794
- type: map_at_1000
value: 37.865
- type: map_at_20
value: 37.336999999999996
- type: map_at_3
value: 33.833999999999996
- type: map_at_5
value: 35.61
- type: mrr_at_1
value: 29.944
- type: mrr_at_10
value: 38.838
- type: mrr_at_100
value: 39.765
- type: mrr_at_1000
value: 39.818999999999996
- type: mrr_at_20
value: 39.373000000000005
- type: mrr_at_3
value: 36.234
- type: mrr_at_5
value: 37.844
- type: ndcg_at_1
value: 29.944
- type: ndcg_at_10
value: 41.986000000000004
- type: ndcg_at_100
value: 47.05
- type: ndcg_at_1000
value: 48.897
- type: ndcg_at_20
value: 43.989
- type: ndcg_at_3
value: 36.452
- type: ndcg_at_5
value: 39.395
- type: precision_at_1
value: 29.944
- type: precision_at_10
value: 6.4750000000000005
- type: precision_at_100
value: 0.946
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_20
value: 3.6839999999999997
- type: precision_at_3
value: 15.443000000000001
- type: precision_at_5
value: 10.96
- type: recall_at_1
value: 27.666
- type: recall_at_10
value: 56.172999999999995
- type: recall_at_100
value: 79.142
- type: recall_at_1000
value: 93.013
- type: recall_at_20
value: 63.695
- type: recall_at_3
value: 41.285
- type: recall_at_5
value: 48.36
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-mathematica
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 17.939
- type: map_at_10
value: 27.301
- type: map_at_100
value: 28.485
- type: map_at_1000
value: 28.616000000000003
- type: map_at_20
value: 27.843
- type: map_at_3
value: 24.342
- type: map_at_5
value: 26.259
- type: mrr_at_1
value: 22.761
- type: mrr_at_10
value: 32.391
- type: mrr_at_100
value: 33.297
- type: mrr_at_1000
value: 33.361000000000004
- type: mrr_at_20
value: 32.845
- type: mrr_at_3
value: 29.498
- type: mrr_at_5
value: 31.375999999999998
- type: ndcg_at_1
value: 22.761
- type: ndcg_at_10
value: 33.036
- type: ndcg_at_100
value: 38.743
- type: ndcg_at_1000
value: 41.568
- type: ndcg_at_20
value: 34.838
- type: ndcg_at_3
value: 27.803
- type: ndcg_at_5
value: 30.781
- type: precision_at_1
value: 22.761
- type: precision_at_10
value: 6.132
- type: precision_at_100
value: 1.031
- type: precision_at_1000
value: 0.14200000000000002
- type: precision_at_20
value: 3.582
- type: precision_at_3
value: 13.474
- type: precision_at_5
value: 10.123999999999999
- type: recall_at_1
value: 17.939
- type: recall_at_10
value: 45.515
- type: recall_at_100
value: 70.56700000000001
- type: recall_at_1000
value: 90.306
- type: recall_at_20
value: 51.946999999999996
- type: recall_at_3
value: 31.459
- type: recall_at_5
value: 39.007
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-physics
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 31.156
- type: map_at_10
value: 42.317
- type: map_at_100
value: 43.742
- type: map_at_1000
value: 43.852000000000004
- type: map_at_20
value: 43.147999999999996
- type: map_at_3
value: 38.981
- type: map_at_5
value: 40.827000000000005
- type: mrr_at_1
value: 38.401999999999994
- type: mrr_at_10
value: 48.141
- type: mrr_at_100
value: 48.991
- type: mrr_at_1000
value: 49.03
- type: mrr_at_20
value: 48.665000000000006
- type: mrr_at_3
value: 45.684999999999995
- type: mrr_at_5
value: 47.042
- type: ndcg_at_1
value: 38.401999999999994
- type: ndcg_at_10
value: 48.541000000000004
- type: ndcg_at_100
value: 54.063
- type: ndcg_at_1000
value: 56.005
- type: ndcg_at_20
value: 50.895999999999994
- type: ndcg_at_3
value: 43.352000000000004
- type: ndcg_at_5
value: 45.769
- type: precision_at_1
value: 38.401999999999994
- type: precision_at_10
value: 8.738999999999999
- type: precision_at_100
value: 1.335
- type: precision_at_1000
value: 0.16999999999999998
- type: precision_at_20
value: 5.164
- type: precision_at_3
value: 20.468
- type: precision_at_5
value: 14.437
- type: recall_at_1
value: 31.156
- type: recall_at_10
value: 61.172000000000004
- type: recall_at_100
value: 83.772
- type: recall_at_1000
value: 96.192
- type: recall_at_20
value: 69.223
- type: recall_at_3
value: 46.628
- type: recall_at_5
value: 53.032000000000004
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-programmers
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 26.741999999999997
- type: map_at_10
value: 36.937
- type: map_at_100
value: 38.452
- type: map_at_1000
value: 38.557
- type: map_at_20
value: 37.858999999999995
- type: map_at_3
value: 33.579
- type: map_at_5
value: 35.415
- type: mrr_at_1
value: 32.991
- type: mrr_at_10
value: 42.297000000000004
- type: mrr_at_100
value: 43.282
- type: mrr_at_1000
value: 43.332
- type: mrr_at_20
value: 42.95
- type: mrr_at_3
value: 39.707
- type: mrr_at_5
value: 41.162
- type: ndcg_at_1
value: 32.991
- type: ndcg_at_10
value: 43.004999999999995
- type: ndcg_at_100
value: 49.053000000000004
- type: ndcg_at_1000
value: 51.166999999999994
- type: ndcg_at_20
value: 45.785
- type: ndcg_at_3
value: 37.589
- type: ndcg_at_5
value: 40.007999999999996
- type: precision_at_1
value: 32.991
- type: precision_at_10
value: 8.025
- type: precision_at_100
value: 1.268
- type: precision_at_1000
value: 0.163
- type: precision_at_20
value: 4.846
- type: precision_at_3
value: 17.922
- type: precision_at_5
value: 13.059000000000001
- type: recall_at_1
value: 26.741999999999997
- type: recall_at_10
value: 55.635999999999996
- type: recall_at_100
value: 80.798
- type: recall_at_1000
value: 94.918
- type: recall_at_20
value: 65.577
- type: recall_at_3
value: 40.658
- type: recall_at_5
value: 46.812
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 27.274583333333336
- type: map_at_10
value: 37.04091666666666
- type: map_at_100
value: 38.27966666666667
- type: map_at_1000
value: 38.39383333333334
- type: map_at_20
value: 37.721500000000006
- type: map_at_3
value: 33.937999999999995
- type: map_at_5
value: 35.67974999999999
- type: mrr_at_1
value: 32.40525
- type: mrr_at_10
value: 41.43925000000001
- type: mrr_at_100
value: 42.271
- type: mrr_at_1000
value: 42.32416666666667
- type: mrr_at_20
value: 41.92733333333334
- type: mrr_at_3
value: 38.84941666666666
- type: mrr_at_5
value: 40.379583333333336
- type: ndcg_at_1
value: 32.40525
- type: ndcg_at_10
value: 42.73808333333334
- type: ndcg_at_100
value: 47.88941666666667
- type: ndcg_at_1000
value: 50.05008333333334
- type: ndcg_at_20
value: 44.74183333333334
- type: ndcg_at_3
value: 37.51908333333334
- type: ndcg_at_5
value: 40.01883333333333
- type: precision_at_1
value: 32.40525
- type: precision_at_10
value: 7.5361666666666665
- type: precision_at_100
value: 1.1934166666666666
- type: precision_at_1000
value: 0.1575
- type: precision_at_20
value: 4.429166666666667
- type: precision_at_3
value: 17.24941666666667
- type: precision_at_5
value: 12.362333333333336
- type: recall_at_1
value: 27.274583333333336
- type: recall_at_10
value: 55.21358333333334
- type: recall_at_100
value: 77.60366666666667
- type: recall_at_1000
value: 92.43691666666666
- type: recall_at_20
value: 62.474583333333335
- type: recall_at_3
value: 40.79375
- type: recall_at_5
value: 47.15158333333334
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-stats
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 27.389999999999997
- type: map_at_10
value: 34.107
- type: map_at_100
value: 35.022999999999996
- type: map_at_1000
value: 35.13
- type: map_at_20
value: 34.605999999999995
- type: map_at_3
value: 32.021
- type: map_at_5
value: 32.948
- type: mrr_at_1
value: 30.982
- type: mrr_at_10
value: 37.345
- type: mrr_at_100
value: 38.096999999999994
- type: mrr_at_1000
value: 38.179
- type: mrr_at_20
value: 37.769000000000005
- type: mrr_at_3
value: 35.481
- type: mrr_at_5
value: 36.293
- type: ndcg_at_1
value: 30.982
- type: ndcg_at_10
value: 38.223
- type: ndcg_at_100
value: 42.686
- type: ndcg_at_1000
value: 45.352
- type: ndcg_at_20
value: 39.889
- type: ndcg_at_3
value: 34.259
- type: ndcg_at_5
value: 35.664
- type: precision_at_1
value: 30.982
- type: precision_at_10
value: 5.7669999999999995
- type: precision_at_100
value: 0.877
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_20
value: 3.3360000000000003
- type: precision_at_3
value: 14.264
- type: precision_at_5
value: 9.54
- type: recall_at_1
value: 27.389999999999997
- type: recall_at_10
value: 48.009
- type: recall_at_100
value: 68.244
- type: recall_at_1000
value: 87.943
- type: recall_at_20
value: 54.064
- type: recall_at_3
value: 36.813
- type: recall_at_5
value: 40.321
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-tex
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 18.249000000000002
- type: map_at_10
value: 25.907000000000004
- type: map_at_100
value: 27.105
- type: map_at_1000
value: 27.233
- type: map_at_20
value: 26.541999999999998
- type: map_at_3
value: 23.376
- type: map_at_5
value: 24.673000000000002
- type: mrr_at_1
value: 21.989
- type: mrr_at_10
value: 29.846
- type: mrr_at_100
value: 30.808999999999997
- type: mrr_at_1000
value: 30.885
- type: mrr_at_20
value: 30.384
- type: mrr_at_3
value: 27.46
- type: mrr_at_5
value: 28.758
- type: ndcg_at_1
value: 21.989
- type: ndcg_at_10
value: 30.874000000000002
- type: ndcg_at_100
value: 36.504999999999995
- type: ndcg_at_1000
value: 39.314
- type: ndcg_at_20
value: 32.952999999999996
- type: ndcg_at_3
value: 26.249
- type: ndcg_at_5
value: 28.229
- type: precision_at_1
value: 21.989
- type: precision_at_10
value: 5.705
- type: precision_at_100
value: 0.9990000000000001
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_20
value: 3.4459999999999997
- type: precision_at_3
value: 12.377
- type: precision_at_5
value: 8.961
- type: recall_at_1
value: 18.249000000000002
- type: recall_at_10
value: 41.824
- type: recall_at_100
value: 67.071
- type: recall_at_1000
value: 86.863
- type: recall_at_20
value: 49.573
- type: recall_at_3
value: 28.92
- type: recall_at_5
value: 34.003
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-unix
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 26.602999999999998
- type: map_at_10
value: 36.818
- type: map_at_100
value: 37.894
- type: map_at_1000
value: 37.991
- type: map_at_20
value: 37.389
- type: map_at_3
value: 33.615
- type: map_at_5
value: 35.432
- type: mrr_at_1
value: 31.53
- type: mrr_at_10
value: 41.144
- type: mrr_at_100
value: 41.937999999999995
- type: mrr_at_1000
value: 41.993
- type: mrr_at_20
value: 41.585
- type: mrr_at_3
value: 38.385999999999996
- type: mrr_at_5
value: 39.995000000000005
- type: ndcg_at_1
value: 31.53
- type: ndcg_at_10
value: 42.792
- type: ndcg_at_100
value: 47.749
- type: ndcg_at_1000
value: 49.946
- type: ndcg_at_20
value: 44.59
- type: ndcg_at_3
value: 37.025000000000006
- type: ndcg_at_5
value: 39.811
- type: precision_at_1
value: 31.53
- type: precision_at_10
value: 7.2669999999999995
- type: precision_at_100
value: 1.109
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_20
value: 4.184
- type: precision_at_3
value: 16.791
- type: precision_at_5
value: 12.09
- type: recall_at_1
value: 26.602999999999998
- type: recall_at_10
value: 56.730999999999995
- type: recall_at_100
value: 78.119
- type: recall_at_1000
value: 93.458
- type: recall_at_20
value: 63.00599999999999
- type: recall_at_3
value: 41.306
- type: recall_at_5
value: 48.004999999999995
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-webmasters
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 23.988
- type: map_at_10
value: 33.650999999999996
- type: map_at_100
value: 35.263
- type: map_at_1000
value: 35.481
- type: map_at_20
value: 34.463
- type: map_at_3
value: 30.330000000000002
- type: map_at_5
value: 32.056000000000004
- type: mrr_at_1
value: 29.644
- type: mrr_at_10
value: 38.987
- type: mrr_at_100
value: 39.973
- type: mrr_at_1000
value: 40.013
- type: mrr_at_20
value: 39.553
- type: mrr_at_3
value: 36.001
- type: mrr_at_5
value: 37.869
- type: ndcg_at_1
value: 29.644
- type: ndcg_at_10
value: 40.156
- type: ndcg_at_100
value: 46.244
- type: ndcg_at_1000
value: 48.483
- type: ndcg_at_20
value: 42.311
- type: ndcg_at_3
value: 34.492
- type: ndcg_at_5
value: 37.118
- type: precision_at_1
value: 29.644
- type: precision_at_10
value: 7.925
- type: precision_at_100
value: 1.5890000000000002
- type: precision_at_1000
value: 0.245
- type: precision_at_20
value: 4.97
- type: precision_at_3
value: 16.469
- type: precision_at_5
value: 12.174
- type: recall_at_1
value: 23.988
- type: recall_at_10
value: 52.844
- type: recall_at_100
value: 80.143
- type: recall_at_1000
value: 93.884
- type: recall_at_20
value: 61.050000000000004
- type: recall_at_3
value: 36.720000000000006
- type: recall_at_5
value: 43.614999999999995
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-wordpress
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 21.947
- type: map_at_10
value: 29.902
- type: map_at_100
value: 30.916
- type: map_at_1000
value: 31.016
- type: map_at_20
value: 30.497999999999998
- type: map_at_3
value: 27.044
- type: map_at_5
value: 28.786
- type: mrr_at_1
value: 23.845
- type: mrr_at_10
value: 32.073
- type: mrr_at_100
value: 32.940999999999995
- type: mrr_at_1000
value: 33.015
- type: mrr_at_20
value: 32.603
- type: mrr_at_3
value: 29.205
- type: mrr_at_5
value: 31.044
- type: ndcg_at_1
value: 23.845
- type: ndcg_at_10
value: 34.79
- type: ndcg_at_100
value: 39.573
- type: ndcg_at_1000
value: 42.163000000000004
- type: ndcg_at_20
value: 36.778
- type: ndcg_at_3
value: 29.326
- type: ndcg_at_5
value: 32.289
- type: precision_at_1
value: 23.845
- type: precision_at_10
value: 5.527
- type: precision_at_100
value: 0.847
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_20
value: 3.2439999999999998
- type: precision_at_3
value: 12.384
- type: precision_at_5
value: 9.205
- type: recall_at_1
value: 21.947
- type: recall_at_10
value: 47.713
- type: recall_at_100
value: 69.299
- type: recall_at_1000
value: 88.593
- type: recall_at_20
value: 55.032000000000004
- type: recall_at_3
value: 33.518
- type: recall_at_5
value: 40.427
- task:
type: Retrieval
dataset:
type: mteb/climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 13.655999999999999
- type: map_at_10
value: 23.954
- type: map_at_100
value: 26.07
- type: map_at_1000
value: 26.266000000000002
- type: map_at_20
value: 25.113000000000003
- type: map_at_3
value: 19.85
- type: map_at_5
value: 21.792
- type: mrr_at_1
value: 31.075000000000003
- type: mrr_at_10
value: 43.480000000000004
- type: mrr_at_100
value: 44.39
- type: mrr_at_1000
value: 44.42
- type: mrr_at_20
value: 44.06
- type: mrr_at_3
value: 40.38
- type: mrr_at_5
value: 42.138999999999996
- type: ndcg_at_1
value: 31.075000000000003
- type: ndcg_at_10
value: 33.129999999999995
- type: ndcg_at_100
value: 40.794000000000004
- type: ndcg_at_1000
value: 44.062
- type: ndcg_at_20
value: 36.223
- type: ndcg_at_3
value: 27.224999999999998
- type: ndcg_at_5
value: 28.969
- type: precision_at_1
value: 31.075000000000003
- type: precision_at_10
value: 10.476
- type: precision_at_100
value: 1.864
- type: precision_at_1000
value: 0.247
- type: precision_at_20
value: 6.593
- type: precision_at_3
value: 20.456
- type: precision_at_5
value: 15.440000000000001
- type: recall_at_1
value: 13.655999999999999
- type: recall_at_10
value: 39.678000000000004
- type: recall_at_100
value: 65.523
- type: recall_at_1000
value: 83.59100000000001
- type: recall_at_20
value: 48.27
- type: recall_at_3
value: 24.863
- type: recall_at_5
value: 30.453999999999997
- task:
type: Retrieval
dataset:
type: mteb/dbpedia
name: MTEB DBPedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 9.139
- type: map_at_10
value: 20.366999999999997
- type: map_at_100
value: 29.755
- type: map_at_1000
value: 31.563999999999997
- type: map_at_20
value: 24.021
- type: map_at_3
value: 14.395
- type: map_at_5
value: 16.853
- type: mrr_at_1
value: 69.0
- type: mrr_at_10
value: 76.778
- type: mrr_at_100
value: 77.116
- type: mrr_at_1000
value: 77.12299999999999
- type: mrr_at_20
value: 77.046
- type: mrr_at_3
value: 75.208
- type: mrr_at_5
value: 76.146
- type: ndcg_at_1
value: 57.125
- type: ndcg_at_10
value: 42.84
- type: ndcg_at_100
value: 48.686
- type: ndcg_at_1000
value: 56.294
- type: ndcg_at_20
value: 42.717
- type: ndcg_at_3
value: 46.842
- type: ndcg_at_5
value: 44.248
- type: precision_at_1
value: 69.0
- type: precision_at_10
value: 34.625
- type: precision_at_100
value: 11.468
- type: precision_at_1000
value: 2.17
- type: precision_at_20
value: 26.562
- type: precision_at_3
value: 50.917
- type: precision_at_5
value: 43.35
- type: recall_at_1
value: 9.139
- type: recall_at_10
value: 26.247999999999998
- type: recall_at_100
value: 56.647000000000006
- type: recall_at_1000
value: 80.784
- type: recall_at_20
value: 35.010999999999996
- type: recall_at_3
value: 15.57
- type: recall_at_5
value: 19.198
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 55.93
- type: f1
value: 49.35314406745291
- task:
type: Retrieval
dataset:
type: mteb/fever
name: MTEB FEVER
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 73.198
- type: map_at_10
value: 81.736
- type: map_at_100
value: 82.02000000000001
- type: map_at_1000
value: 82.03399999999999
- type: map_at_20
value: 81.937
- type: map_at_3
value: 80.692
- type: map_at_5
value: 81.369
- type: mrr_at_1
value: 78.803
- type: mrr_at_10
value: 86.144
- type: mrr_at_100
value: 86.263
- type: mrr_at_1000
value: 86.26599999999999
- type: mrr_at_20
value: 86.235
- type: mrr_at_3
value: 85.464
- type: mrr_at_5
value: 85.95
- type: ndcg_at_1
value: 78.803
- type: ndcg_at_10
value: 85.442
- type: ndcg_at_100
value: 86.422
- type: ndcg_at_1000
value: 86.68900000000001
- type: ndcg_at_20
value: 85.996
- type: ndcg_at_3
value: 83.839
- type: ndcg_at_5
value: 84.768
- type: precision_at_1
value: 78.803
- type: precision_at_10
value: 10.261000000000001
- type: precision_at_100
value: 1.0959999999999999
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_20
value: 5.286
- type: precision_at_3
value: 32.083
- type: precision_at_5
value: 19.898
- type: recall_at_1
value: 73.198
- type: recall_at_10
value: 92.42099999999999
- type: recall_at_100
value: 96.28
- type: recall_at_1000
value: 97.995
- type: recall_at_20
value: 94.36
- type: recall_at_3
value: 88.042
- type: recall_at_5
value: 90.429
- task:
type: Retrieval
dataset:
type: mteb/fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 21.583
- type: map_at_10
value: 36.503
- type: map_at_100
value: 38.529
- type: map_at_1000
value: 38.701
- type: map_at_20
value: 37.69
- type: map_at_3
value: 31.807000000000002
- type: map_at_5
value: 34.424
- type: mrr_at_1
value: 43.827
- type: mrr_at_10
value: 53.528
- type: mrr_at_100
value: 54.291
- type: mrr_at_1000
value: 54.32599999999999
- type: mrr_at_20
value: 54.064
- type: mrr_at_3
value: 51.25999999999999
- type: mrr_at_5
value: 52.641000000000005
- type: ndcg_at_1
value: 43.827
- type: ndcg_at_10
value: 44.931
- type: ndcg_at_100
value: 51.778999999999996
- type: ndcg_at_1000
value: 54.532000000000004
- type: ndcg_at_20
value: 47.899
- type: ndcg_at_3
value: 41.062
- type: ndcg_at_5
value: 42.33
- type: precision_at_1
value: 43.827
- type: precision_at_10
value: 12.608
- type: precision_at_100
value: 1.974
- type: precision_at_1000
value: 0.247
- type: precision_at_20
value: 7.585
- type: precision_at_3
value: 27.778000000000002
- type: precision_at_5
value: 20.308999999999997
- type: recall_at_1
value: 21.583
- type: recall_at_10
value: 52.332
- type: recall_at_100
value: 77.256
- type: recall_at_1000
value: 93.613
- type: recall_at_20
value: 61.413
- type: recall_at_3
value: 37.477
- type: recall_at_5
value: 44.184
- task:
type: Retrieval
dataset:
type: mteb/hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 39.845000000000006
- type: map_at_10
value: 64.331
- type: map_at_100
value: 65.202
- type: map_at_1000
value: 65.261
- type: map_at_20
value: 64.833
- type: map_at_3
value: 60.663
- type: map_at_5
value: 62.94
- type: mrr_at_1
value: 79.689
- type: mrr_at_10
value: 85.299
- type: mrr_at_100
value: 85.461
- type: mrr_at_1000
value: 85.466
- type: mrr_at_20
value: 85.39099999999999
- type: mrr_at_3
value: 84.396
- type: mrr_at_5
value: 84.974
- type: ndcg_at_1
value: 79.689
- type: ndcg_at_10
value: 72.49
- type: ndcg_at_100
value: 75.485
- type: ndcg_at_1000
value: 76.563
- type: ndcg_at_20
value: 73.707
- type: ndcg_at_3
value: 67.381
- type: ndcg_at_5
value: 70.207
- type: precision_at_1
value: 79.689
- type: precision_at_10
value: 15.267
- type: precision_at_100
value: 1.7610000000000001
- type: precision_at_1000
value: 0.19
- type: precision_at_20
value: 8.024000000000001
- type: precision_at_3
value: 43.363
- type: precision_at_5
value: 28.248
- type: recall_at_1
value: 39.845000000000006
- type: recall_at_10
value: 76.334
- type: recall_at_100
value: 88.042
- type: recall_at_1000
value: 95.09100000000001
- type: recall_at_20
value: 80.243
- type: recall_at_3
value: 65.044
- type: recall_at_5
value: 70.621
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 93.57079999999999
- type: ap
value: 90.50045924786099
- type: f1
value: 93.56673497845476
- task:
type: Retrieval
dataset:
type: mteb/msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 22.212
- type: map_at_10
value: 34.528
- type: map_at_100
value: 35.69
- type: map_at_1000
value: 35.74
- type: map_at_20
value: 35.251
- type: map_at_3
value: 30.628
- type: map_at_5
value: 32.903999999999996
- type: mrr_at_1
value: 22.794
- type: mrr_at_10
value: 35.160000000000004
- type: mrr_at_100
value: 36.251
- type: mrr_at_1000
value: 36.295
- type: mrr_at_20
value: 35.845
- type: mrr_at_3
value: 31.328
- type: mrr_at_5
value: 33.574
- type: ndcg_at_1
value: 22.779
- type: ndcg_at_10
value: 41.461
- type: ndcg_at_100
value: 47.049
- type: ndcg_at_1000
value: 48.254000000000005
- type: ndcg_at_20
value: 44.031
- type: ndcg_at_3
value: 33.561
- type: ndcg_at_5
value: 37.62
- type: precision_at_1
value: 22.779
- type: precision_at_10
value: 6.552
- type: precision_at_100
value: 0.936
- type: precision_at_1000
value: 0.104
- type: precision_at_20
value: 3.8120000000000003
- type: precision_at_3
value: 14.274000000000001
- type: precision_at_5
value: 10.622
- type: recall_at_1
value: 22.212
- type: recall_at_10
value: 62.732
- type: recall_at_100
value: 88.567
- type: recall_at_1000
value: 97.727
- type: recall_at_20
value: 72.733
- type: recall_at_3
value: 41.367
- type: recall_at_5
value: 51.105999999999995
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 94.24988600091199
- type: f1
value: 94.06064583085202
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (de)
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 74.86052409129333
- type: f1
value: 72.24661442078647
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (es)
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 77.09139426284189
- type: f1
value: 76.3725044443502
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (fr)
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 79.79956154087064
- type: f1
value: 78.41859658401724
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (hi)
config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 32.785944783076374
- type: f1
value: 31.182237278594922
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (th)
config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 16.654611211573236
- type: f1
value: 12.088413093236642
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 67.51481988144094
- type: f1
value: 49.561420234732125
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (de)
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 42.36122851507467
- type: f1
value: 25.445030887504398
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (es)
config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 44.73315543695797
- type: f1
value: 28.42075153540265
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (fr)
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 38.96022549326651
- type: f1
value: 25.926979537146106
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (hi)
config: hi
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 13.578343492291141
- type: f1
value: 8.929295550931657
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (th)
config: th
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 5.396021699819168
- type: f1
value: 1.8587148785378742
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (af)
config: af
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 37.22259583053128
- type: f1
value: 34.63013680947778
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (am)
config: am
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 3.194351042367182
- type: f1
value: 1.2612010214639442
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (ar)
config: ar
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 14.26361802286483
- type: f1
value: 13.70260406613821
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (az)
config: az
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 37.21923335574983
- type: f1
value: 36.33553913878251
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (bn)
config: bn
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 10.756556825823807
- type: f1
value: 9.676431920229374
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (cy)
config: cy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 32.49831876260928
- type: f1
value: 30.818895782691868
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (da)
config: da
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 40.995292535305985
- type: f1
value: 37.68768183180129
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (de)
config: de
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 42.780766644250164
- type: f1
value: 37.82194830667135
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (el)
config: el
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 33.490248823133825
- type: f1
value: 29.71809045584527
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.8836583725622
- type: f1
value: 72.16381047416814
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (es)
config: es
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 44.45191661062542
- type: f1
value: 43.46583297093683
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (fa)
config: fa
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 26.738399462004036
- type: f1
value: 24.11896530001951
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (fi)
config: fi
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 38.09683927370545
- type: f1
value: 35.34443269387154
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (fr)
config: fr
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 46.89307330195024
- type: f1
value: 43.47164092514292
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (he)
config: he
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 25.198386012104912
- type: f1
value: 22.446286736401916
- task:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 28.55413584398117
- type: f1
value: 26.582548923662753
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ms)
config: ms
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 46.617350369872234
- type: f1
value: 41.35397419267425
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (my)
config: my
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 9.976462676529927
- type: f1
value: 5.900764382768462
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (nb)
config: nb
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 50.894418291862806
- type: f1
value: 47.70929403771086
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (nl)
config: nl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 51.761936785474106
- type: f1
value: 48.42797973062516
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (pl)
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 46.21385339609952
- type: f1
value: 43.7081546200347
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (pt)
config: pt
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 55.59852051109617
- type: f1
value: 54.19610878409633
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ro)
config: ro
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 50.54135843981169
- type: f1
value: 47.79393938467311
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ru)
config: ru
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 37.73032952252858
- type: f1
value: 35.96450149708041
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sl)
config: sl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 41.67114996637525
- type: f1
value: 40.28283538885605
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sq)
config: sq
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 47.38063214525891
- type: f1
value: 44.93264016007152
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sv)
config: sv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 49.28379287155347
- type: f1
value: 46.25486396570196
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sw)
config: sw
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 44.18291862811029
- type: f1
value: 41.17519157172804
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ta)
config: ta
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 12.599193006052452
- type: f1
value: 11.129236666238377
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (te)
config: te
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 7.017484868863484
- type: f1
value: 3.9665415549749077
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (th)
config: th
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 19.788164088769335
- type: f1
value: 15.783384761347582
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (tl)
config: tl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 50.35978480161398
- type: f1
value: 47.30586047800275
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (tr)
config: tr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 45.484196368527236
- type: f1
value: 44.65101184252231
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ur)
config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 23.681909885675857
- type: f1
value: 22.247817138937524
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (vi)
config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 41.63080026899798
- type: f1
value: 39.546896741744
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-CN)
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 30.141223940820446
- type: f1
value: 28.177838960078123
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-TW)
config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 27.515131136516473
- type: f1
value: 26.514325837594654
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.70592767911301
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.80943770643908
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.66434973425713
- type: mrr
value: 33.92240574935323
- task:
type: Retrieval
dataset:
type: mteb/nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 6.561999999999999
- type: map_at_10
value: 14.854000000000001
- type: map_at_100
value: 19.187
- type: map_at_1000
value: 20.812
- type: map_at_20
value: 16.744
- type: map_at_3
value: 10.804
- type: map_at_5
value: 12.555
- type: mrr_at_1
value: 48.916
- type: mrr_at_10
value: 57.644
- type: mrr_at_100
value: 58.17
- type: mrr_at_1000
value: 58.206
- type: mrr_at_20
value: 57.969
- type: mrr_at_3
value: 55.36600000000001
- type: mrr_at_5
value: 56.729
- type: ndcg_at_1
value: 46.594
- type: ndcg_at_10
value: 37.897999999999996
- type: ndcg_at_100
value: 35.711
- type: ndcg_at_1000
value: 44.65
- type: ndcg_at_20
value: 35.989
- type: ndcg_at_3
value: 42.869
- type: ndcg_at_5
value: 40.373
- type: precision_at_1
value: 48.297000000000004
- type: precision_at_10
value: 28.297
- type: precision_at_100
value: 9.099
- type: precision_at_1000
value: 2.229
- type: precision_at_20
value: 21.455
- type: precision_at_3
value: 40.248
- type: precision_at_5
value: 34.675
- type: recall_at_1
value: 6.561999999999999
- type: recall_at_10
value: 19.205
- type: recall_at_100
value: 36.742999999999995
- type: recall_at_1000
value: 69.119
- type: recall_at_20
value: 23.787
- type: recall_at_3
value: 11.918
- type: recall_at_5
value: 14.860000000000001
- task:
type: Retrieval
dataset:
type: mteb/nq
name: MTEB NQ
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 30.306
- type: map_at_10
value: 46.916999999999994
- type: map_at_100
value: 47.899
- type: map_at_1000
value: 47.925000000000004
- type: map_at_20
value: 47.583
- type: map_at_3
value: 42.235
- type: map_at_5
value: 45.118
- type: mrr_at_1
value: 34.327999999999996
- type: mrr_at_10
value: 49.248999999999995
- type: mrr_at_100
value: 49.96
- type: mrr_at_1000
value: 49.977
- type: mrr_at_20
value: 49.738
- type: mrr_at_3
value: 45.403999999999996
- type: mrr_at_5
value: 47.786
- type: ndcg_at_1
value: 34.327999999999996
- type: ndcg_at_10
value: 55.123999999999995
- type: ndcg_at_100
value: 59.136
- type: ndcg_at_1000
value: 59.71300000000001
- type: ndcg_at_20
value: 57.232000000000006
- type: ndcg_at_3
value: 46.48
- type: ndcg_at_5
value: 51.237
- type: precision_at_1
value: 34.327999999999996
- type: precision_at_10
value: 9.261
- type: precision_at_100
value: 1.1520000000000001
- type: precision_at_1000
value: 0.121
- type: precision_at_20
value: 5.148
- type: precision_at_3
value: 21.523999999999997
- type: precision_at_5
value: 15.659999999999998
- type: recall_at_1
value: 30.306
- type: recall_at_10
value: 77.65100000000001
- type: recall_at_100
value: 94.841
- type: recall_at_1000
value: 99.119
- type: recall_at_20
value: 85.37599999999999
- type: recall_at_3
value: 55.562
- type: recall_at_5
value: 66.5
- task:
type: Retrieval
dataset:
type: mteb/quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 71.516
- type: map_at_10
value: 85.48400000000001
- type: map_at_100
value: 86.11
- type: map_at_1000
value: 86.124
- type: map_at_20
value: 85.895
- type: map_at_3
value: 82.606
- type: map_at_5
value: 84.395
- type: mrr_at_1
value: 82.38
- type: mrr_at_10
value: 88.31099999999999
- type: mrr_at_100
value: 88.407
- type: mrr_at_1000
value: 88.407
- type: mrr_at_20
value: 88.385
- type: mrr_at_3
value: 87.42699999999999
- type: mrr_at_5
value: 88.034
- type: ndcg_at_1
value: 82.39999999999999
- type: ndcg_at_10
value: 89.07300000000001
- type: ndcg_at_100
value: 90.23400000000001
- type: ndcg_at_1000
value: 90.304
- type: ndcg_at_20
value: 89.714
- type: ndcg_at_3
value: 86.42699999999999
- type: ndcg_at_5
value: 87.856
- type: precision_at_1
value: 82.39999999999999
- type: precision_at_10
value: 13.499
- type: precision_at_100
value: 1.536
- type: precision_at_1000
value: 0.157
- type: precision_at_20
value: 7.155
- type: precision_at_3
value: 37.846999999999994
- type: precision_at_5
value: 24.778
- type: recall_at_1
value: 71.516
- type: recall_at_10
value: 95.831
- type: recall_at_100
value: 99.714
- type: recall_at_1000
value: 99.979
- type: recall_at_20
value: 97.87599999999999
- type: recall_at_3
value: 88.08
- type: recall_at_5
value: 92.285
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 61.3760407207699
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 65.28621066626943
- task:
type: Retrieval
dataset:
type: mteb/scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 5.163
- type: map_at_10
value: 14.377
- type: map_at_100
value: 17.177
- type: map_at_1000
value: 17.588
- type: map_at_20
value: 15.827
- type: map_at_3
value: 9.879
- type: map_at_5
value: 12.133
- type: mrr_at_1
value: 25.5
- type: mrr_at_10
value: 38.435
- type: mrr_at_100
value: 39.573
- type: mrr_at_1000
value: 39.606
- type: mrr_at_20
value: 39.134
- type: mrr_at_3
value: 34.666999999999994
- type: mrr_at_5
value: 37.117
- type: ndcg_at_1
value: 25.5
- type: ndcg_at_10
value: 23.688000000000002
- type: ndcg_at_100
value: 33.849000000000004
- type: ndcg_at_1000
value: 39.879
- type: ndcg_at_20
value: 27.36
- type: ndcg_at_3
value: 22.009999999999998
- type: ndcg_at_5
value: 19.691
- type: precision_at_1
value: 25.5
- type: precision_at_10
value: 12.540000000000001
- type: precision_at_100
value: 2.721
- type: precision_at_1000
value: 0.415
- type: precision_at_20
value: 8.385
- type: precision_at_3
value: 21.099999999999998
- type: precision_at_5
value: 17.84
- type: recall_at_1
value: 5.163
- type: recall_at_10
value: 25.405
- type: recall_at_100
value: 55.213
- type: recall_at_1000
value: 84.243
- type: recall_at_20
value: 34.003
- type: recall_at_3
value: 12.837000000000002
- type: recall_at_5
value: 18.096999999999998
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 87.64406884822948
- type: cos_sim_spearman
value: 83.00239648251724
- type: euclidean_pearson
value: 85.03347205351844
- type: euclidean_spearman
value: 83.00240733538445
- type: manhattan_pearson
value: 85.0312758694447
- type: manhattan_spearman
value: 82.99430696077589
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 87.68832340658764
- type: cos_sim_spearman
value: 79.21679373212476
- type: euclidean_pearson
value: 85.17094885886415
- type: euclidean_spearman
value: 79.21421345946399
- type: manhattan_pearson
value: 85.17409319145995
- type: manhattan_spearman
value: 79.20992207976401
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 88.43733084958856
- type: cos_sim_spearman
value: 89.43082089321751
- type: euclidean_pearson
value: 88.63286785416938
- type: euclidean_spearman
value: 89.43082081372343
- type: manhattan_pearson
value: 88.62969346368385
- type: manhattan_spearman
value: 89.43131586189746
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 86.62185532014894
- type: cos_sim_spearman
value: 84.7923120886599
- type: euclidean_pearson
value: 85.99786490539253
- type: euclidean_spearman
value: 84.79231064318844
- type: manhattan_pearson
value: 85.97647892920392
- type: manhattan_spearman
value: 84.76865232132103
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.39303997282114
- type: cos_sim_spearman
value: 89.54273264876765
- type: euclidean_pearson
value: 88.8848627924181
- type: euclidean_spearman
value: 89.54275013645078
- type: manhattan_pearson
value: 88.86926987108802
- type: manhattan_spearman
value: 89.53259197721715
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 85.21814352466886
- type: cos_sim_spearman
value: 86.68505223422434
- type: euclidean_pearson
value: 86.07422446469991
- type: euclidean_spearman
value: 86.68505161067375
- type: manhattan_pearson
value: 86.05114200797293
- type: manhattan_spearman
value: 86.6587670422703
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ko-ko)
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 39.17871768366095
- type: cos_sim_spearman
value: 39.78510424960567
- type: euclidean_pearson
value: 41.65680175653682
- type: euclidean_spearman
value: 39.78538944779548
- type: manhattan_pearson
value: 41.567603690394755
- type: manhattan_spearman
value: 39.71393388259443
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ar-ar)
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 49.26766904195114
- type: cos_sim_spearman
value: 46.79722787057151
- type: euclidean_pearson
value: 51.2329334717446
- type: euclidean_spearman
value: 46.7920623095072
- type: manhattan_pearson
value: 51.26488560860826
- type: manhattan_spearman
value: 47.00400318665492
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-ar)
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 1.6821294132202447
- type: cos_sim_spearman
value: -0.7813676799492025
- type: euclidean_pearson
value: 1.9197388753860283
- type: euclidean_spearman
value: -0.7813676799492025
- type: manhattan_pearson
value: 2.209862430499871
- type: manhattan_spearman
value: -0.863014010062456
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-de)
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 48.76382428941107
- type: cos_sim_spearman
value: 47.50280322999196
- type: euclidean_pearson
value: 48.73919143974209
- type: euclidean_spearman
value: 47.50280322999196
- type: manhattan_pearson
value: 48.76291223862666
- type: manhattan_spearman
value: 47.51318193687094
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 89.6579390263212
- type: cos_sim_spearman
value: 89.64423556388047
- type: euclidean_pearson
value: 90.1160733522703
- type: euclidean_spearman
value: 89.64423556388047
- type: manhattan_pearson
value: 90.1528407376387
- type: manhattan_spearman
value: 89.61290724496793
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-tr)
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 6.717092266815236
- type: cos_sim_spearman
value: 4.180543503488665
- type: euclidean_pearson
value: 7.120267092048099
- type: euclidean_spearman
value: 4.180543503488665
- type: manhattan_pearson
value: 6.396237465828514
- type: manhattan_spearman
value: 3.61244941411957
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-en)
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 44.36476614938953
- type: cos_sim_spearman
value: 44.265723809500685
- type: euclidean_pearson
value: 44.61551298711104
- type: euclidean_spearman
value: 44.265723809500685
- type: manhattan_pearson
value: 44.54302374682193
- type: manhattan_spearman
value: 44.08642490624185
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-es)
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 79.64871991975828
- type: cos_sim_spearman
value: 79.21979030014373
- type: euclidean_pearson
value: 81.8672798988218
- type: euclidean_spearman
value: 79.21950130108661
- type: manhattan_pearson
value: 82.02131606326583
- type: manhattan_spearman
value: 79.44848373553044
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (fr-en)
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 48.73898658957231
- type: cos_sim_spearman
value: 47.15192605817168
- type: euclidean_pearson
value: 49.11990573381456
- type: euclidean_spearman
value: 47.15192605817168
- type: manhattan_pearson
value: 48.5694400358235
- type: manhattan_spearman
value: 46.651326429708135
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (it-en)
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 44.42168074232218
- type: cos_sim_spearman
value: 42.64799010889372
- type: euclidean_pearson
value: 44.41376048324183
- type: euclidean_spearman
value: 42.64799010889372
- type: manhattan_pearson
value: 44.724522621427546
- type: manhattan_spearman
value: 42.60912761758016
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (nl-en)
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 40.55050173163197
- type: cos_sim_spearman
value: 36.59720399843921
- type: euclidean_pearson
value: 41.49402389245919
- type: euclidean_spearman
value: 36.59720399843921
- type: manhattan_pearson
value: 41.877514420153666
- type: manhattan_spearman
value: 36.782790653297695
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 69.44405106094861
- type: cos_sim_spearman
value: 70.25621893108706
- type: euclidean_pearson
value: 71.15726637696066
- type: euclidean_spearman
value: 70.25621893108706
- type: manhattan_pearson
value: 71.28565265298322
- type: manhattan_spearman
value: 70.30317892414027
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de)
config: de
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 34.56638014500804
- type: cos_sim_spearman
value: 39.48672765878819
- type: euclidean_pearson
value: 31.61811391543846
- type: euclidean_spearman
value: 39.48672765878819
- type: manhattan_pearson
value: 31.839117286689977
- type: manhattan_spearman
value: 39.71519891403971
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es)
config: es
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 53.72389957326714
- type: cos_sim_spearman
value: 59.47018781803598
- type: euclidean_pearson
value: 57.02101112722141
- type: euclidean_spearman
value: 59.47018781803598
- type: manhattan_pearson
value: 57.16531255049132
- type: manhattan_spearman
value: 59.57320508684436
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl)
config: pl
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 24.14602533311477
- type: cos_sim_spearman
value: 35.38039329704056
- type: euclidean_pearson
value: 13.540543553763765
- type: euclidean_spearman
value: 35.38039329704056
- type: manhattan_pearson
value: 13.566377379303256
- type: manhattan_spearman
value: 35.88351047224126
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (tr)
config: tr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 39.07697432450346
- type: cos_sim_spearman
value: 45.65479772235109
- type: euclidean_pearson
value: 41.68913259791294
- type: euclidean_spearman
value: 45.65479772235109
- type: manhattan_pearson
value: 41.58872552392231
- type: manhattan_spearman
value: 45.462070534023404
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ar)
config: ar
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 23.917322166825183
- type: cos_sim_spearman
value: 25.06042767518008
- type: euclidean_pearson
value: 24.29850435278771
- type: euclidean_spearman
value: 25.06042767518008
- type: manhattan_pearson
value: 24.461400062927154
- type: manhattan_spearman
value: 25.285239684773046
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ru)
config: ru
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 20.39987623162105
- type: cos_sim_spearman
value: 30.62427846964406
- type: euclidean_pearson
value: 20.817950942480323
- type: euclidean_spearman
value: 30.618700916425222
- type: manhattan_pearson
value: 20.756787430880788
- type: manhattan_spearman
value: 30.813116243628436
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
config: zh
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 43.838363041373974
- type: cos_sim_spearman
value: 54.17598089882719
- type: euclidean_pearson
value: 47.51044033919419
- type: euclidean_spearman
value: 54.17598089882719
- type: manhattan_pearson
value: 47.54911083403354
- type: manhattan_spearman
value: 54.2562151204606
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr)
config: fr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 77.69372699157654
- type: cos_sim_spearman
value: 79.88201388457435
- type: euclidean_pearson
value: 78.81259581302578
- type: euclidean_spearman
value: 79.88201388457435
- type: manhattan_pearson
value: 78.85098508555477
- type: manhattan_spearman
value: 80.20154858554835
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-en)
config: de-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 51.83713469138834
- type: cos_sim_spearman
value: 54.2205845288082
- type: euclidean_pearson
value: 54.14828396506985
- type: euclidean_spearman
value: 54.2205845288082
- type: manhattan_pearson
value: 54.10701855179347
- type: manhattan_spearman
value: 54.30261135461622
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-en)
config: es-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 61.59147752554915
- type: cos_sim_spearman
value: 66.65350021824162
- type: euclidean_pearson
value: 62.577915098325434
- type: euclidean_spearman
value: 66.65350021824162
- type: manhattan_pearson
value: 62.22817675366819
- type: manhattan_spearman
value: 66.35054389546214
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (it)
config: it
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 65.23775897743552
- type: cos_sim_spearman
value: 68.1509652709288
- type: euclidean_pearson
value: 66.17577980319408
- type: euclidean_spearman
value: 68.1509652709288
- type: manhattan_pearson
value: 66.40051933918704
- type: manhattan_spearman
value: 68.37138808382802
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl-en)
config: pl-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 61.943863830043725
- type: cos_sim_spearman
value: 62.699440972016774
- type: euclidean_pearson
value: 62.810366501196
- type: euclidean_spearman
value: 62.699440972016774
- type: manhattan_pearson
value: 63.13065659868621
- type: manhattan_spearman
value: 63.314141373703215
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh-en)
config: zh-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 48.1108866326284
- type: cos_sim_spearman
value: 49.25274096772371
- type: euclidean_pearson
value: 47.87203797435136
- type: euclidean_spearman
value: 49.25274096772371
- type: manhattan_pearson
value: 47.39927722979605
- type: manhattan_spearman
value: 48.76629586560382
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-it)
config: es-it
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 58.58401639298775
- type: cos_sim_spearman
value: 64.37272828346495
- type: euclidean_pearson
value: 61.03680632288844
- type: euclidean_spearman
value: 64.37272828346495
- type: manhattan_pearson
value: 61.381331848220675
- type: manhattan_spearman
value: 65.01053960017909
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-fr)
config: de-fr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 44.374682063416735
- type: cos_sim_spearman
value: 48.907776246550185
- type: euclidean_pearson
value: 45.473260322201284
- type: euclidean_spearman
value: 48.907776246550185
- type: manhattan_pearson
value: 46.051779591771854
- type: manhattan_spearman
value: 49.69297213757249
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-pl)
config: de-pl
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 31.55497030143048
- type: cos_sim_spearman
value: 33.042073055100396
- type: euclidean_pearson
value: 33.548707962408955
- type: euclidean_spearman
value: 33.042073055100396
- type: manhattan_pearson
value: 31.704989941561873
- type: manhattan_spearman
value: 31.56395608711827
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr-pl)
config: fr-pl
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 51.253093232573036
- type: cos_sim_spearman
value: 39.440531887330785
- type: euclidean_pearson
value: 51.42758694144294
- type: euclidean_spearman
value: 39.440531887330785
- type: manhattan_pearson
value: 49.623915715149394
- type: manhattan_spearman
value: 39.440531887330785
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 87.61260941646887
- type: cos_sim_spearman
value: 88.96384726759047
- type: euclidean_pearson
value: 88.72268994912045
- type: euclidean_spearman
value: 88.96384726759047
- type: manhattan_pearson
value: 88.72080954591475
- type: manhattan_spearman
value: 88.92379960545995
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.64768404690723
- type: mrr
value: 96.25675341361615
- task:
type: Retrieval
dataset:
type: mteb/scifact
name: MTEB SciFact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 61.194
- type: map_at_10
value: 70.62899999999999
- type: map_at_100
value: 71.119
- type: map_at_1000
value: 71.14200000000001
- type: map_at_20
value: 71.033
- type: map_at_3
value: 67.51899999999999
- type: map_at_5
value: 69.215
- type: mrr_at_1
value: 63.666999999999994
- type: mrr_at_10
value: 71.456
- type: mrr_at_100
value: 71.844
- type: mrr_at_1000
value: 71.866
- type: mrr_at_20
value: 71.769
- type: mrr_at_3
value: 69.167
- type: mrr_at_5
value: 70.39999999999999
- type: ndcg_at_1
value: 63.666999999999994
- type: ndcg_at_10
value: 75.14
- type: ndcg_at_100
value: 77.071
- type: ndcg_at_1000
value: 77.55199999999999
- type: ndcg_at_20
value: 76.491
- type: ndcg_at_3
value: 69.836
- type: ndcg_at_5
value: 72.263
- type: precision_at_1
value: 63.666999999999994
- type: precision_at_10
value: 10.0
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 5.3
- type: precision_at_3
value: 27.0
- type: precision_at_5
value: 17.867
- type: recall_at_1
value: 61.194
- type: recall_at_10
value: 88.156
- type: recall_at_100
value: 96.5
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 93.389
- type: recall_at_3
value: 73.839
- type: recall_at_5
value: 79.828
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.87425742574257
- type: cos_sim_ap
value: 96.97141655369937
- type: cos_sim_f1
value: 93.6910084451068
- type: cos_sim_precision
value: 93.0898321816387
- type: cos_sim_recall
value: 94.3
- type: dot_accuracy
value: 99.87425742574257
- type: dot_ap
value: 96.97141655369938
- type: dot_f1
value: 93.6910084451068
- type: dot_precision
value: 93.0898321816387
- type: dot_recall
value: 94.3
- type: euclidean_accuracy
value: 99.87425742574257
- type: euclidean_ap
value: 96.97141655369938
- type: euclidean_f1
value: 93.6910084451068
- type: euclidean_precision
value: 93.0898321816387
- type: euclidean_recall
value: 94.3
- type: manhattan_accuracy
value: 99.87425742574257
- type: manhattan_ap
value: 96.98252972861131
- type: manhattan_f1
value: 93.68473396320238
- type: manhattan_precision
value: 93.17507418397626
- type: manhattan_recall
value: 94.19999999999999
- type: max_accuracy
value: 99.87425742574257
- type: max_ap
value: 96.98252972861131
- type: max_f1
value: 93.6910084451068
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 66.5976926394361
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.3221929214798
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 55.28322662897131
- type: mrr
value: 56.223620129870135
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.176396304511282
- type: cos_sim_spearman
value: 32.11989671564906
- type: dot_pearson
value: 31.17639740597169
- type: dot_spearman
value: 32.145586989831564
- task:
type: Retrieval
dataset:
type: mteb/trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.186
- type: map_at_10
value: 1.659
- type: map_at_100
value: 9.224
- type: map_at_1000
value: 22.506999999999998
- type: map_at_20
value: 2.937
- type: map_at_3
value: 0.5539999999999999
- type: map_at_5
value: 0.8920000000000001
- type: mrr_at_1
value: 72.0
- type: mrr_at_10
value: 82.633
- type: mrr_at_100
value: 82.633
- type: mrr_at_1000
value: 82.633
- type: mrr_at_20
value: 82.633
- type: mrr_at_3
value: 80.333
- type: mrr_at_5
value: 82.633
- type: ndcg_at_1
value: 69.0
- type: ndcg_at_10
value: 67.327
- type: ndcg_at_100
value: 51.626000000000005
- type: ndcg_at_1000
value: 47.396
- type: ndcg_at_20
value: 63.665000000000006
- type: ndcg_at_3
value: 68.95
- type: ndcg_at_5
value: 69.241
- type: precision_at_1
value: 72.0
- type: precision_at_10
value: 71.6
- type: precision_at_100
value: 53.22
- type: precision_at_1000
value: 20.721999999999998
- type: precision_at_20
value: 67.30000000000001
- type: precision_at_3
value: 72.667
- type: precision_at_5
value: 74.0
- type: recall_at_1
value: 0.186
- type: recall_at_10
value: 1.932
- type: recall_at_100
value: 12.883
- type: recall_at_1000
value: 44.511
- type: recall_at_20
value: 3.583
- type: recall_at_3
value: 0.601
- type: recall_at_5
value: 1.0
- task:
type: Retrieval
dataset:
type: mteb/touche2020
name: MTEB Touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 2.308
- type: map_at_10
value: 9.744
- type: map_at_100
value: 15.859000000000002
- type: map_at_1000
value: 17.396
- type: map_at_20
value: 12.49
- type: map_at_3
value: 4.848
- type: map_at_5
value: 6.912999999999999
- type: mrr_at_1
value: 32.653
- type: mrr_at_10
value: 47.207
- type: mrr_at_100
value: 48.116
- type: mrr_at_1000
value: 48.116
- type: mrr_at_20
value: 47.735
- type: mrr_at_3
value: 42.857
- type: mrr_at_5
value: 44.285999999999994
- type: ndcg_at_1
value: 28.571
- type: ndcg_at_10
value: 24.421
- type: ndcg_at_100
value: 35.961
- type: ndcg_at_1000
value: 47.541
- type: ndcg_at_20
value: 25.999
- type: ndcg_at_3
value: 25.333
- type: ndcg_at_5
value: 25.532
- type: precision_at_1
value: 32.653
- type: precision_at_10
value: 22.448999999999998
- type: precision_at_100
value: 7.571
- type: precision_at_1000
value: 1.5310000000000001
- type: precision_at_20
value: 17.959
- type: precision_at_3
value: 26.531
- type: precision_at_5
value: 26.122
- type: recall_at_1
value: 2.308
- type: recall_at_10
value: 16.075
- type: recall_at_100
value: 47.357
- type: recall_at_1000
value: 82.659
- type: recall_at_20
value: 24.554000000000002
- type: recall_at_3
value: 5.909
- type: recall_at_5
value: 9.718
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 67.2998046875
- type: ap
value: 12.796222498684031
- type: f1
value: 51.7465070845071
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.76004527447652
- type: f1
value: 61.88985723942393
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 52.69229715788263
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.42325803182929
- type: cos_sim_ap
value: 78.29203513753492
- type: cos_sim_f1
value: 71.33160557818093
- type: cos_sim_precision
value: 67.00672385810341
- type: cos_sim_recall
value: 76.2532981530343
- type: dot_accuracy
value: 87.42325803182929
- type: dot_ap
value: 78.29208368244002
- type: dot_f1
value: 71.33160557818093
- type: dot_precision
value: 67.00672385810341
- type: dot_recall
value: 76.2532981530343
- type: euclidean_accuracy
value: 87.42325803182929
- type: euclidean_ap
value: 78.29202838891078
- type: euclidean_f1
value: 71.33160557818093
- type: euclidean_precision
value: 67.00672385810341
- type: euclidean_recall
value: 76.2532981530343
- type: manhattan_accuracy
value: 87.42325803182929
- type: manhattan_ap
value: 78.23964459648822
- type: manhattan_f1
value: 71.1651728553137
- type: manhattan_precision
value: 69.12935323383084
- type: manhattan_recall
value: 73.3245382585752
- type: max_accuracy
value: 87.42325803182929
- type: max_ap
value: 78.29208368244002
- type: max_f1
value: 71.33160557818093
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.00725734466566
- type: cos_sim_ap
value: 86.1594112416402
- type: cos_sim_f1
value: 78.544568993303
- type: cos_sim_precision
value: 73.42484097756947
- type: cos_sim_recall
value: 84.43178318447798
- type: dot_accuracy
value: 89.00725734466566
- type: dot_ap
value: 86.15940795129771
- type: dot_f1
value: 78.544568993303
- type: dot_precision
value: 73.42484097756947
- type: dot_recall
value: 84.43178318447798
- type: euclidean_accuracy
value: 89.00725734466566
- type: euclidean_ap
value: 86.15939689541806
- type: euclidean_f1
value: 78.544568993303
- type: euclidean_precision
value: 73.42484097756947
- type: euclidean_recall
value: 84.43178318447798
- type: manhattan_accuracy
value: 88.97426941436721
- type: manhattan_ap
value: 86.14154348065739
- type: manhattan_f1
value: 78.53991175290814
- type: manhattan_precision
value: 74.60339452719086
- type: manhattan_recall
value: 82.91499846011703
- type: max_accuracy
value: 89.00725734466566
- type: max_ap
value: 86.1594112416402
- type: max_f1
value: 78.544568993303
---
|
kurogane/Llama3-BioYouri-8B-instruct-chatvector-mergetest
|
kurogane
| 2024-05-21T12:53:33Z | 15 | 2 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"conversational",
"ja",
"base_model:NousResearch/Meta-Llama-3-8B",
"base_model:merge:NousResearch/Meta-Llama-3-8B",
"base_model:aaditya/Llama3-OpenBioLLM-8B",
"base_model:merge:aaditya/Llama3-OpenBioLLM-8B",
"base_model:aixsatoshi/Llama-3-youko-8b-instruct-chatvector",
"base_model:merge:aixsatoshi/Llama-3-youko-8b-instruct-chatvector",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-10T08:34:11Z |
---
language:
- ja
tags:
- merge
base_model:
- aaditya/Llama3-OpenBioLLM-8B
- aixsatoshi/Llama-3-youko-8b-instruct-chatvector
- NousResearch/Meta-Llama-3-8B
license: llama3
---
# kurogane/Llama3-BioYouri-8B-mergetest
このモデルは生物学・医学に精通したOpenBioLLM-8Bをベースに、日本語対応を向上させるためにLlama-3-youko-8b-instruct-chatvectorとマージさせたモデルです。
初めての試みだったのでうまくできているかはわかりませんが、体感として、日本語での医学知識の説明は少し詳しめにこたえてくれます。生物系の知識について、若干ハルシネーションしているように感じますので、要注意です。
また、生物学・医学系の知識が入ったせいか、若干、性的な部分の規制が弱くなっているように感じますので、運用時は気を付けてください。
## Method
### マージ方法について
以下のNoteBookを参考にマージしました。
[merge.ipynb · p1atdev/nekoqarasu-14b-chat at main (huggingface.co)](https://huggingface.co/p1atdev/nekoqarasu-14b-chat/blob/main/merge.ipynb)
こちらのコードを参考に、モデルネームを入力するだけでマージできるコードを作成しました。
**[LLMModelMerger.py](https://gist.github.com/kuroganegames/c9c78685d1004976cd8db737f51a8a8f)**
RAMが64GBあれば問題なくマージできると思います。
### 以下のモデルを線形マージしました
[aaditya/Llama3-OpenBioLLM-8B](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B)
[aixsatoshi/Llama-3-youko-8b-instruct-chatvector](https://huggingface.co/aixsatoshi/Llama-3-youko-8b-instruct-chatvector)
[NousResearch/Meta-Llama-3-8B](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja)
BioYouri = "Llama3-OpenBioLLM-8B" + "Llama-3-youko-8b-instruct-chatvector" - "Meta-Llama-3-8B"
### アップロード時のsafetensorsの分割について
また、アップロードにあたり、以下のコードで分割しました。
[**safetensors_splitter.py**](https://gist.github.com/kuroganegames/54e413a073cf59faf5652b38de8c7af6#file-safetensors_splitter-py)
"model-00001-of-00004.safetensors"のような形で、指定した「model.safetensors.index.json」の順番でレイヤーが分割されます。ベースにしたモデルなどのmodel.safetensors.index.jsonのディレクトリをdir_jsonに入れてあげれば正常に分割されると思います。ただし、指定したmodel.safetensors.index.jsonの中身に合わせてl_dict_safetensorsの内容は書き換えてあげてください。
## License
このモデルはMETA LLAMA 3 COMMUNITY LICENSEに従って提供されます。 https://llama.meta.com/llama3/license
## 謝辞
今回、[Plat 🖼️（@p1atdev_art）さん / X (twitter.com)](https://twitter.com/p1atdev_art)様に生成したモデルの動作検証の仕方を教えてもらいました。この場を借りて感謝申し上げます。ありがとうございます。
|
hgnoi/qySUc2xTuit87oUt
|
hgnoi
| 2024-05-21T12:53:04Z | 121 | 0 |
transformers
|
[
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-21T12:51:36Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Rafaelweber1209/llama-3-8b-Instruct-bnb-4bit-aiaustin-demo
|
Rafaelweber1209
| 2024-05-21T12:50:28Z | 2 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:quantized:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-21T12:48:09Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** Rafaelweber1209
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
hgnoi/UHKClxiArylgKQts
|
hgnoi
| 2024-05-21T12:50:27Z | 121 | 0 |
transformers
|
[
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-21T12:49:01Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
basakdemirok/bert-base-turkish-cased-subjectivity_v01_seed42
|
basakdemirok
| 2024-05-21T12:46:15Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:dbmdz/bert-base-turkish-cased",
"base_model:finetune:dbmdz/bert-base-turkish-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-21T12:43:41Z |
---
license: mit
base_model: dbmdz/bert-base-turkish-cased
tags:
- generated_from_keras_callback
model-index:
- name: basakdemirok/bert-base-turkish-cased-subjectivity_v01_seed42
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# basakdemirok/bert-base-turkish-cased-subjectivity_v01_seed42
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1478
- Validation Loss: 0.3552
- Train F1: 0.8449
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 376, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train F1 | Epoch |
|:----------:|:---------------:|:--------:|:-----:|
| 0.5988 | 0.4581 | 0.7907 | 0 |
| 0.3359 | 0.3252 | 0.8586 | 1 |
| 0.1478 | 0.3552 | 0.8449 | 2 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.13.1
- Datasets 2.4.0
- Tokenizers 0.13.3
|
ifyou819/summary-news-dataset-4
|
ifyou819
| 2024-05-21T12:45:22Z | 103 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:ifyou819/summary-news-dataset-3",
"base_model:finetune:ifyou819/summary-news-dataset-3",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-05-21T12:44:14Z |
---
base_model: ifyou819/summary-news-dataset-3
tags:
- generated_from_trainer
model-index:
- name: summary-news-dataset-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/fine-tune-gpt-model/huggingface/runs/s2v8hnd5)
# summary-news-dataset-4
This model is a fine-tuned version of [ifyou819/summary-news-dataset-3](https://huggingface.co/ifyou819/summary-news-dataset-3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7802
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.2383 | 1.0 | 1054 | 6.3919 |
| 6.8246 | 2.0 | 2108 | 5.9371 |
| 6.6384 | 3.0 | 3162 | 5.7802 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.19.1
|
Jayant9928/orpo_med_v2
|
Jayant9928
| 2024-05-21T12:44:32Z | 2,781 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-01T17:57:27Z |
---
license: apache-2.0
---
Model Card for Model ID Model Details Model Description This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
Developed by: [More Information Needed] Funded by [optional]: [More Information Needed] Shared by [optional]: [More Information Needed] Model type: [More Information Needed] Language(s) (NLP): [More Information Needed] License: [More Information Needed] Finetuned from model [optional]: [More Information Needed] Model Sources [optional] Repository: [More Information Needed] Paper [optional]: [More Information Needed] Demo [optional]: [More Information Needed] Uses Direct Use [More Information Needed]
Downstream Use [optional] [More Information Needed]
Out-of-Scope Use [More Information Needed]
Bias, Risks, and Limitations [More Information Needed]
Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model Use the code below to get started with the model.
[More Information Needed]
Training Details Training Data [More Information Needed]
Training Procedure Preprocessing [optional] [More Information Needed]
Training Hyperparameters Training regime: [More Information Needed] Speeds, Sizes, Times [optional] [More Information Needed]
Evaluation Testing Data, Factors & Metrics Testing Data [More Information Needed]
Factors [More Information Needed]
Metrics [More Information Needed]
Results [More Information Needed]
Summary Model Examination [optional] [More Information Needed]
Environmental Impact
|
Jayant9928/orpo_med_v0
|
Jayant9928
| 2024-05-21T12:44:00Z | 2,772 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-30T11:30:12Z |
---
license: apache-2.0
---
Model Card for Model ID Model Details Model Description This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
Developed by: [More Information Needed] Funded by [optional]: [More Information Needed] Shared by [optional]: [More Information Needed] Model type: [More Information Needed] Language(s) (NLP): [More Information Needed] License: [More Information Needed] Finetuned from model [optional]: [More Information Needed] Model Sources [optional] Repository: [More Information Needed] Paper [optional]: [More Information Needed] Demo [optional]: [More Information Needed] Uses Direct Use [More Information Needed]
Downstream Use [optional] [More Information Needed]
Out-of-Scope Use [More Information Needed]
Bias, Risks, and Limitations [More Information Needed]
Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model Use the code below to get started with the model.
[More Information Needed]
Training Details Training Data [More Information Needed]
Training Procedure Preprocessing [optional] [More Information Needed]
Training Hyperparameters Training regime: [More Information Needed] Speeds, Sizes, Times [optional] [More Information Needed]
Evaluation Testing Data, Factors & Metrics Testing Data [More Information Needed]
Factors [More Information Needed]
Metrics [More Information Needed]
Results [More Information Needed]
Summary Model Examination [optional] [More Information Needed]
|
Felladrin/gguf-h2o-danube2-1.8b-chat
|
Felladrin
| 2024-05-21T12:43:22Z | 32 | 1 | null |
[
"gguf",
"base_model:h2oai/h2o-danube2-1.8b-chat",
"base_model:quantized:h2oai/h2o-danube2-1.8b-chat",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-21T10:39:48Z |
---
license: apache-2.0
base_model: h2oai/h2o-danube2-1.8b-chat
---
GGUF version of [h2oai/h2o-danube2-1.8b-chat](https://huggingface.co/h2oai/h2o-danube2-1.8b-chat).
|
Resi/finetune-donut-doctype-v3
|
Resi
| 2024-05-21T12:38:33Z | 48 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-05-21T12:38:04Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
andrewbai/tinyllama-sft-vicuna-full-rrr100-gaussian
|
andrewbai
| 2024-05-21T12:34:40Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:ucla-cmllab/vicuna_cleaned",
"base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
"base_model:finetune:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-21T10:01:48Z |
---
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- ucla-cmllab/vicuna_cleaned
model-index:
- name: tinyllama-sft-vicuna-full-rrr100-gaussian
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tinyllama-sft-vicuna-full-rrr100-gaussian
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on the ucla-cmllab/vicuna_cleaned dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7274
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7115 | 1.0 | 732 | 0.7274 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
steve1989/internlm-fingpt-sentiment-finance
|
steve1989
| 2024-05-21T12:30:35Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"internlm2",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] |
feature-extraction
| 2024-05-21T11:54:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
slimaneMakh/superClass_tableClf_21may_xlm-roberta-base_3epochs_BASELINE
|
slimaneMakh
| 2024-05-21T12:30:30Z | 188 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-21T12:29:53Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Dumiiii/distilbert-base-cased-ner
|
Dumiiii
| 2024-05-21T12:29:42Z | 103 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-05-21T12:25:23Z |
Label 0 - nothing.
Label 1 - B-PROD.
Label 2 - I-PROD.
|
ezuryy/llama-2-7b-chat-hf__ru_news__qlora
|
ezuryy
| 2024-05-21T12:28:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-21T12:28:39Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
AylinNaebzadeh/XLM-RoBERTa-FineTuned-With-Dreaddit
|
AylinNaebzadeh
| 2024-05-21T12:27:57Z | 108 | 0 |
transformers
|
[
"transformers",
"safetensors",
"Mental Disorder",
"Text Classification",
"text-classification",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-20T09:10:39Z |
---
library_name: transformers
pipeline_tag: text-classification
tags:
- Mental Disorder
- Text Classification
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
feldmaarshal/opd_bert_spam_ru
|
feldmaarshal
| 2024-05-21T12:27:05Z | 108 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:ai-forever/ruBert-base",
"base_model:finetune:ai-forever/ruBert-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-21T12:16:38Z |
---
license: apache-2.0
base_model: ai-forever/ruBert-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: opd_bert_spam_ru
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opd_bert_spam_ru
This model is a fine-tuned version of [ai-forever/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1034
- F1: 0.9872
- Accuracy: 0.9872
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 70
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:------:|:--------:|
| 0.1094 | 0.5102 | 100 | 0.2559 | 0.9587 | 0.9591 |
| 0.1566 | 1.0204 | 200 | 0.0769 | 0.9847 | 0.9847 |
| 0.0352 | 1.5306 | 300 | 0.0860 | 0.9821 | 0.9821 |
| 0.0571 | 2.0408 | 400 | 0.0908 | 0.9846 | 0.9847 |
| 0.0256 | 2.5510 | 500 | 0.1034 | 0.9872 | 0.9872 |
| 0.0178 | 3.0612 | 600 | 0.1416 | 0.9770 | 0.9770 |
| 0.0098 | 3.5714 | 700 | 0.1461 | 0.9821 | 0.9821 |
| 0.0097 | 4.0816 | 800 | 0.1618 | 0.9770 | 0.9770 |
| 0.0003 | 4.5918 | 900 | 0.1651 | 0.9795 | 0.9795 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
LA1512/led-pubmed-20K-8192
|
LA1512
| 2024-05-21T12:23:42Z | 89 | 0 |
transformers
|
[
"transformers",
"safetensors",
"led",
"text2text-generation",
"generated_from_trainer",
"base_model:pszemraj/led-base-book-summary",
"base_model:finetune:pszemraj/led-base-book-summary",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-05-21T12:23:24Z |
---
license: bsd-3-clause
base_model: pszemraj/led-base-book-summary
tags:
- generated_from_trainer
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [pszemraj/led-base-book-summary](https://huggingface.co/pszemraj/led-base-book-summary) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1513
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 85
- num_epochs: 1
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.5175 | 0.16 | 200 | 3.3413 |
| 3.3565 | 0.32 | 400 | 3.2590 |
| 3.2485 | 0.48 | 600 | 3.2262 |
| 3.2628 | 0.64 | 800 | 3.1805 |
| 3.2043 | 0.8 | 1000 | 3.1617 |
| 3.3002 | 0.96 | 1200 | 3.1513 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
basakdemirok/bert-base-turkish-cased-subjectivity_v0_seed42
|
basakdemirok
| 2024-05-21T12:23:35Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:dbmdz/bert-base-turkish-cased",
"base_model:finetune:dbmdz/bert-base-turkish-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-21T12:22:19Z |
---
license: mit
base_model: dbmdz/bert-base-turkish-cased
tags:
- generated_from_keras_callback
model-index:
- name: basakdemirok/bert-base-turkish-cased-subjectivity_v0_seed42
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# basakdemirok/bert-base-turkish-cased-subjectivity_v0_seed42
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7000
- Validation Loss: 0.6594
- Train F1: 0.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 196, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train F1 | Epoch |
|:----------:|:---------------:|:--------:|:-----:|
| 0.7000 | 0.6594 | 0.0 | 0 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.13.1
- Datasets 2.4.0
- Tokenizers 0.13.3
|
caiiofc/ppo-Huggy
|
caiiofc
| 2024-05-21T12:21:28Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2024-05-21T12:20:49Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: caiiofc/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
royson/bayesian_models
|
royson
| 2024-05-21T12:20:54Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2024-05-19T12:33:34Z |
---
license: apache-2.0
---
|
mika5883/RuReCl8
|
mika5883
| 2024-05-21T12:15:16Z | 114 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:mika5883/RuReCl8",
"base_model:finetune:mika5883/RuReCl8",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-05-21T11:32:38Z |
---
base_model: mika5883/RuReCl8
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: RuReCl8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# RuReCl8
This model is a fine-tuned version of [mika5883/RuReCl8](https://huggingface.co/mika5883/RuReCl8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1825
- Bleu: 61.2481
- Gen Len: 16.234
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3.83229e-05
- train_batch_size: 128
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 20 | 0.1898 | 61.0474 | 16.2296 |
| No log | 2.0 | 40 | 0.1825 | 61.2481 | 16.234 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
HVD2407/Mbart2
|
HVD2407
| 2024-05-21T12:12:14Z | 105 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mbart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-05-21T12:09:51Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
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|
hjskhan/merged_model
|
hjskhan
| 2024-05-21T12:11:25Z | 75 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-05-21T11:05:57Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
Essacheez/gemma-1.1-7b-it-finetune-summerization-10k-alpaca-style
|
Essacheez
| 2024-05-21T12:08:36Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-21T10:18:29Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Demo [optional]:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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#### Preprocessing [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
jinq047/bert-finetuned-ner
|
jinq047
| 2024-05-21T12:08:27Z | 105 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-05-21T11:30:54Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9320436507936508
- name: Recall
type: recall
value: 0.9486704813194211
- name: F1
type: f1
value: 0.9402835696413678
- name: Accuracy
type: accuracy
value: 0.9858273974215577
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0629
- Precision: 0.9320
- Recall: 0.9487
- F1: 0.9403
- Accuracy: 0.9858
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0725 | 1.0 | 1756 | 0.0634 | 0.9065 | 0.9354 | 0.9207 | 0.9825 |
| 0.0335 | 2.0 | 3512 | 0.0684 | 0.9242 | 0.9414 | 0.9327 | 0.9847 |
| 0.0203 | 3.0 | 5268 | 0.0629 | 0.9320 | 0.9487 | 0.9403 | 0.9858 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
esantiago/tinyllama-codewello
|
esantiago
| 2024-05-21T12:08:27Z | 136 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-21T12:07:08Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### Direct Use
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
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<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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#### Metrics
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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|
Likich/gemma-finetune-qualcoding-1000-prompt1
|
Likich
| 2024-05-21T12:06:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-21T12:06:04Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
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#### Testing Data
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#### Metrics
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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[More Information Needed]
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|
MoGP/g_x_bib_few0_balanced_reg
|
MoGP
| 2024-05-21T12:03:47Z | 107 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-21T11:46:37Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Repository:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Summary
## Model Examination [optional]
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Lanerdog/Llama-3-8B-Instruct-Gradient-1048k-Q5_K_M-GGUF
|
Lanerdog
| 2024-05-21T12:03:01Z | 2 | 1 | null |
[
"gguf",
"meta",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-05-21T12:02:43Z |
---
language:
- en
license: llama3
tags:
- meta
- llama-3
- llama-cpp
- gguf-my-repo
pipeline_tag: text-generation
---
# Lanerdog/Llama-3-8B-Instruct-Gradient-1048k-Q5_K_M-GGUF
This model was converted to GGUF format from [`gradientai/Llama-3-8B-Instruct-Gradient-1048k`](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo Lanerdog/Llama-3-8B-Instruct-Gradient-1048k-Q5_K_M-GGUF --model llama-3-8b-instruct-gradient-1048k.Q5_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo Lanerdog/Llama-3-8B-Instruct-Gradient-1048k-Q5_K_M-GGUF --model llama-3-8b-instruct-gradient-1048k.Q5_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-8b-instruct-gradient-1048k.Q5_K_M.gguf -n 128
```
|
joosma/rl_course_vizdoom_health_gathering_supreme
|
joosma
| 2024-05-21T11:57:01Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-05-21T11:56:56Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.04 +/- 2.72
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r joosma/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Hadiseh-Mhd/Mixtral-8x7B-Instruct-v0.1-Q4_K_M-GGUF
|
Hadiseh-Mhd
| 2024-05-21T11:56:55Z | 16 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"fr",
"it",
"de",
"es",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-21T11:55:28Z |
---
language:
- fr
- it
- de
- es
- en
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
inference:
parameters:
temperature: 0.5
widget:
- messages:
- role: user
content: What is your favorite condiment?
---
# Hadiseh-Mhd/Mixtral-8x7B-Instruct-v0.1-Q4_K_M-GGUF
This model was converted to GGUF format from [`mistralai/Mixtral-8x7B-Instruct-v0.1`](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo Hadiseh-Mhd/Mixtral-8x7B-Instruct-v0.1-Q4_K_M-GGUF --model mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo Hadiseh-Mhd/Mixtral-8x7B-Instruct-v0.1-Q4_K_M-GGUF --model mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf -n 128
```
|
derbaliSamar/Fine-Tunning-LLMA-3-Digitalization-FinalVersion
|
derbaliSamar
| 2024-05-21T11:53:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-21T11:53:35Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** derbaliSamar
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
adriansanz/FS_25_06
|
adriansanz
| 2024-05-21T11:50:05Z | 118 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:projecte-aina/roberta-base-ca-v2",
"base_model:finetune:projecte-aina/roberta-base-ca-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-21T11:42:03Z |
---
license: apache-2.0
base_model: projecte-aina/roberta-base-ca-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: FS_25_06
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# FS_25_06
This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1755
- Accuracy: 0.9647
- Precision: 0.9651
- Recall: 0.9646
- F1: 0.9644
- Ratio: 0.0529
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 1.7815 | 1.0 | 362 | 1.6483 | 0.9059 | 0.9166 | 0.9061 | 0.9069 | 0.0569 |
| 0.364 | 2.0 | 724 | 0.2948 | 0.9451 | 0.9480 | 0.9450 | 0.9447 | 0.0569 |
| 0.0418 | 3.0 | 1086 | 0.2467 | 0.9510 | 0.9529 | 0.9509 | 0.9509 | 0.0549 |
| 0.0218 | 4.0 | 1448 | 0.1853 | 0.9647 | 0.9651 | 0.9646 | 0.9644 | 0.0529 |
| 0.0985 | 5.0 | 1810 | 0.1755 | 0.9647 | 0.9651 | 0.9646 | 0.9644 | 0.0529 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
JoshuaKelleyDs/quickdraw-MobileVIT-small-finetune
|
JoshuaKelleyDs
| 2024-05-21T11:50:00Z | 192 | 0 |
transformers
|
[
"transformers",
"onnx",
"safetensors",
"mobilevit",
"image-classification",
"generated_from_trainer",
"base_model:apple/mobilevit-small",
"base_model:quantized:apple/mobilevit-small",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-05-18T06:41:54Z |
---
license: other
base_model: apple/mobilevit-small
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: quickdraw-MobileViT-small-a
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# quickdraw-MobileViT-small-a
This model is a fine-tuned version of [apple/mobilevit-small](https://huggingface.co/apple/mobilevit-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9705
- Accuracy: 0.7556
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0008
- train_batch_size: 512
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5000
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|
| 1.464 | 0.5688 | 5000 | 1.4063 | 0.6493 |
| 1.2318 | 1.1377 | 10000 | 1.2154 | 0.6937 |
| 1.1699 | 1.7065 | 15000 | 1.1495 | 0.7096 |
| 1.1018 | 2.2753 | 20000 | 1.1081 | 0.7190 |
| 1.0837 | 2.8441 | 25000 | 1.0871 | 0.7240 |
| 1.0343 | 3.4130 | 30000 | 1.0550 | 0.7326 |
| 1.0198 | 3.9818 | 35000 | 1.0281 | 0.739 |
| 0.9795 | 4.5506 | 40000 | 1.0125 | 0.7435 |
| 0.9339 | 5.1195 | 45000 | 0.9964 | 0.7475 |
| 0.9292 | 5.6883 | 50000 | 0.9843 | 0.7510 |
| 0.8975 | 6.2571 | 55000 | 0.9795 | 0.7528 |
| 0.8957 | 6.8259 | 60000 | 0.9723 | 0.7548 |
| 0.8721 | 7.3948 | 65000 | 0.9716 | 0.7555 |
| 0.8725 | 7.9636 | 70000 | 0.9705 | 0.7556 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.2.1
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Zoyd/TIGER-Lab_MAmmoTH2-8x7B-6_5bpw_exl2
|
Zoyd
| 2024-05-21T11:48:15Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"en",
"arxiv:2405.03548",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-05-21T11:18:06Z |
---
license: mit
language:
- en
---
**Exllamav2** quant (**exl2** / **6.5 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-2_2bpw_exl2)**</center> | <center>12762 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-2_5bpw_exl2)**</center> | <center>14191 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-3_0bpw_exl2)**</center> | <center>16931 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-3_5bpw_exl2)**</center> | <center>19724 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-3_75bpw_exl2)**</center> | <center>21098 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-4_0bpw_exl2)**</center> | <center>22488 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-4_25bpw_exl2)**</center> | <center>23875 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-5_0bpw_exl2)**</center> | <center>28028 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-6_0bpw_exl2)**</center> | <center>33588 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-6_5bpw_exl2)**</center> | <center>36031 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-8_0bpw_exl2)**</center> | <center>41342 MB</center> | <center>8</center> |
# 🦣 MAmmoTH2: Scaling Instructions from the Web
Project Page: [https://tiger-ai-lab.github.io/MAmmoTH2/](https://tiger-ai-lab.github.io/MAmmoTH2/)
Paper: [https://arxiv.org/pdf/2405.03548](https://arxiv.org/pdf/2405.03548)
Code: [https://github.com/TIGER-AI-Lab/MAmmoTH2](https://github.com/TIGER-AI-Lab/MAmmoTH2)
## Introduction
Introducing 🦣 MAmmoTH2, a game-changer in improving the reasoning abilities of large language models (LLMs) through innovative instruction tuning. By efficiently harvesting 10 million instruction-response pairs from the pre-training web corpus, we've developed MAmmoTH2 models that significantly boost performance on reasoning benchmarks. For instance, MAmmoTH2-7B (Mistral) sees its performance soar from 11% to 34% on MATH and from 36% to 67% on GSM8K, all without training on any domain-specific data. Further training on public instruction tuning datasets yields MAmmoTH2-Plus, setting new standards in reasoning and chatbot benchmarks. Our work presents a cost-effective approach to acquiring large-scale, high-quality instruction data, offering a fresh perspective on enhancing LLM reasoning abilities.
| | **Base Model** | **MAmmoTH2** | **MAmmoTH2-Plus** |
|:-----|:---------------------|:-------------------------------------------------------------------|:------------------------------------------------------------------|
| 7B | Mistral | 🦣 [MAmmoTH2-7B](https://huggingface.co/TIGER-Lab/MAmmoTH2-7B) | 🦣 [MAmmoTH2-7B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-7B-Plus) |
| 8B | Llama-3 | 🦣 [MAmmoTH2-8B](https://huggingface.co/TIGER-Lab/MAmmoTH2-8B) | 🦣 [MAmmoTH2-8B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-8B-Plus) |
| 8x7B | Mixtral | 🦣 [MAmmoTH2-8x7B](https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B) | 🦣 [MAmmoTH2-8x7B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B-Plus) |
## Training Data
Please refer to https://huggingface.co/datasets/TIGER-Lab/WebInstructSub for more details.
## Training Procedure
The models are fine-tuned with the WEBINSTRUCT dataset using the original Llama-3, Mistral and Mistal models as base models. The training procedure varies for different models based on their sizes. Check out our paper for more details.
## Evaluation
The models are evaluated using open-ended and multiple-choice math problems from several datasets. Here are the results:
| **Model** | **TheoremQA** | **MATH** | **GSM8K** | **GPQA** | **MMLU-ST** | **BBH** | **ARC-C** | **Avg** |
|:---------------------------------------|:--------------|:---------|:----------|:---------|:------------|:--------|:----------|:--------|
| **MAmmoTH2-7B** (Updated) | 29.0 | 36.7 | 68.4 | 32.4 | 62.4 | 58.6 | 81.7 | 52.7 |
| **MAmmoTH2-8B** (Updated) | 30.3 | 35.8 | 70.4 | 35.2 | 64.2 | 62.1 | 82.2 | 54.3 |
| **MAmmoTH2-8x7B** | 32.2 | 39.0 | 75.4 | 36.8 | 67.4 | 71.1 | 87.5 | 58.9 |
| **MAmmoTH2-7B-Plus** (Updated) | 31.2 | 46.0 | 84.6 | 33.8 | 63.8 | 63.3 | 84.4 | 58.1 |
| **MAmmoTH2-8B-Plus** (Updated) | 31.5 | 43.0 | 85.2 | 35.8 | 66.7 | 69.7 | 84.3 | 59.4 |
| **MAmmoTH2-8x7B-Plus** | 34.1 | 47.0 | 86.4 | 37.8 | 72.4 | 74.1 | 88.4 | 62.9 |
To reproduce our results, please refer to https://github.com/TIGER-AI-Lab/MAmmoTH2/tree/main/math_eval.
## Chat Format
The template used to build a prompt for the Instruct model is defined as follows:
```
<s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST]
```
Note that `<s>` and `</s>` are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings.
But we also found that the model is not very sensitive to the chat template.
## Usage
You can use the models through Huggingface's Transformers library. Use the pipeline function to create a text-generation pipeline with the model of your choice, then feed in a math problem to get the solution.
Check our Github repo for more advanced use: https://github.com/TIGER-AI-Lab/MAmmoTH2
## Limitations
We've tried our best to build math generalist models. However, we acknowledge that the models' performance may vary based on the complexity and specifics of the math problem. Still not all mathematical fields can be covered comprehensively.
## Citation
If you use the models, data, or code from this project, please cite the original paper:
```
@article{yue2024mammoth2,
title={MAmmoTH2: Scaling Instructions from the Web},
author={Yue, Xiang and Zheng, Tuney and Zhang, Ge and Chen, Wenhu},
journal={arXiv preprint arXiv:2405.03548},
year={2024}
}
```
|
Raneechu/textbook2
|
Raneechu
| 2024-05-21T11:48:04Z | 1 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2024-05-21T11:47:59Z |
---
license: llama2
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Llama-2-7b-hf
model-index:
- name: textbook2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# textbook2
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6742
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.8823 | 0.1026 | 1 | 3.6742 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
## Training procedure
### Framework versions
- PEFT 0.6.2
|
Zoyd/TIGER-Lab_MAmmoTH2-8x7B-3_75bpw_exl2
|
Zoyd
| 2024-05-21T11:47:47Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"en",
"arxiv:2405.03548",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-05-21T09:11:02Z |
---
license: mit
language:
- en
---
**Exllamav2** quant (**exl2** / **3.75 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-2_2bpw_exl2)**</center> | <center>12762 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-2_5bpw_exl2)**</center> | <center>14191 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-3_0bpw_exl2)**</center> | <center>16931 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-3_5bpw_exl2)**</center> | <center>19724 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-3_75bpw_exl2)**</center> | <center>21098 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-4_0bpw_exl2)**</center> | <center>22488 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-4_25bpw_exl2)**</center> | <center>23875 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-5_0bpw_exl2)**</center> | <center>28028 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-6_0bpw_exl2)**</center> | <center>33588 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-6_5bpw_exl2)**</center> | <center>36031 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-8_0bpw_exl2)**</center> | <center>41342 MB</center> | <center>8</center> |
# 🦣 MAmmoTH2: Scaling Instructions from the Web
Project Page: [https://tiger-ai-lab.github.io/MAmmoTH2/](https://tiger-ai-lab.github.io/MAmmoTH2/)
Paper: [https://arxiv.org/pdf/2405.03548](https://arxiv.org/pdf/2405.03548)
Code: [https://github.com/TIGER-AI-Lab/MAmmoTH2](https://github.com/TIGER-AI-Lab/MAmmoTH2)
## Introduction
Introducing 🦣 MAmmoTH2, a game-changer in improving the reasoning abilities of large language models (LLMs) through innovative instruction tuning. By efficiently harvesting 10 million instruction-response pairs from the pre-training web corpus, we've developed MAmmoTH2 models that significantly boost performance on reasoning benchmarks. For instance, MAmmoTH2-7B (Mistral) sees its performance soar from 11% to 34% on MATH and from 36% to 67% on GSM8K, all without training on any domain-specific data. Further training on public instruction tuning datasets yields MAmmoTH2-Plus, setting new standards in reasoning and chatbot benchmarks. Our work presents a cost-effective approach to acquiring large-scale, high-quality instruction data, offering a fresh perspective on enhancing LLM reasoning abilities.
| | **Base Model** | **MAmmoTH2** | **MAmmoTH2-Plus** |
|:-----|:---------------------|:-------------------------------------------------------------------|:------------------------------------------------------------------|
| 7B | Mistral | 🦣 [MAmmoTH2-7B](https://huggingface.co/TIGER-Lab/MAmmoTH2-7B) | 🦣 [MAmmoTH2-7B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-7B-Plus) |
| 8B | Llama-3 | 🦣 [MAmmoTH2-8B](https://huggingface.co/TIGER-Lab/MAmmoTH2-8B) | 🦣 [MAmmoTH2-8B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-8B-Plus) |
| 8x7B | Mixtral | 🦣 [MAmmoTH2-8x7B](https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B) | 🦣 [MAmmoTH2-8x7B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B-Plus) |
## Training Data
Please refer to https://huggingface.co/datasets/TIGER-Lab/WebInstructSub for more details.
## Training Procedure
The models are fine-tuned with the WEBINSTRUCT dataset using the original Llama-3, Mistral and Mistal models as base models. The training procedure varies for different models based on their sizes. Check out our paper for more details.
## Evaluation
The models are evaluated using open-ended and multiple-choice math problems from several datasets. Here are the results:
| **Model** | **TheoremQA** | **MATH** | **GSM8K** | **GPQA** | **MMLU-ST** | **BBH** | **ARC-C** | **Avg** |
|:---------------------------------------|:--------------|:---------|:----------|:---------|:------------|:--------|:----------|:--------|
| **MAmmoTH2-7B** (Updated) | 29.0 | 36.7 | 68.4 | 32.4 | 62.4 | 58.6 | 81.7 | 52.7 |
| **MAmmoTH2-8B** (Updated) | 30.3 | 35.8 | 70.4 | 35.2 | 64.2 | 62.1 | 82.2 | 54.3 |
| **MAmmoTH2-8x7B** | 32.2 | 39.0 | 75.4 | 36.8 | 67.4 | 71.1 | 87.5 | 58.9 |
| **MAmmoTH2-7B-Plus** (Updated) | 31.2 | 46.0 | 84.6 | 33.8 | 63.8 | 63.3 | 84.4 | 58.1 |
| **MAmmoTH2-8B-Plus** (Updated) | 31.5 | 43.0 | 85.2 | 35.8 | 66.7 | 69.7 | 84.3 | 59.4 |
| **MAmmoTH2-8x7B-Plus** | 34.1 | 47.0 | 86.4 | 37.8 | 72.4 | 74.1 | 88.4 | 62.9 |
To reproduce our results, please refer to https://github.com/TIGER-AI-Lab/MAmmoTH2/tree/main/math_eval.
## Chat Format
The template used to build a prompt for the Instruct model is defined as follows:
```
<s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST]
```
Note that `<s>` and `</s>` are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings.
But we also found that the model is not very sensitive to the chat template.
## Usage
You can use the models through Huggingface's Transformers library. Use the pipeline function to create a text-generation pipeline with the model of your choice, then feed in a math problem to get the solution.
Check our Github repo for more advanced use: https://github.com/TIGER-AI-Lab/MAmmoTH2
## Limitations
We've tried our best to build math generalist models. However, we acknowledge that the models' performance may vary based on the complexity and specifics of the math problem. Still not all mathematical fields can be covered comprehensively.
## Citation
If you use the models, data, or code from this project, please cite the original paper:
```
@article{yue2024mammoth2,
title={MAmmoTH2: Scaling Instructions from the Web},
author={Yue, Xiang and Zheng, Tuney and Zhang, Ge and Chen, Wenhu},
journal={arXiv preprint arXiv:2405.03548},
year={2024}
}
```
|
RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf
|
RichardErkhov
| 2024-05-21T11:47:34Z | 10 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-21T07:31:58Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
CarbonBeagle-11B-truthy - GGUF
- Model creator: https://huggingface.co/vicgalle/
- Original model: https://huggingface.co/vicgalle/CarbonBeagle-11B-truthy/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [CarbonBeagle-11B-truthy.Q2_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.Q2_K.gguf) | Q2_K | 3.73GB |
| [CarbonBeagle-11B-truthy.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.IQ3_XS.gguf) | IQ3_XS | 4.14GB |
| [CarbonBeagle-11B-truthy.IQ3_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.IQ3_S.gguf) | IQ3_S | 4.37GB |
| [CarbonBeagle-11B-truthy.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.Q3_K_S.gguf) | Q3_K_S | 4.34GB |
| [CarbonBeagle-11B-truthy.IQ3_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.IQ3_M.gguf) | IQ3_M | 4.51GB |
| [CarbonBeagle-11B-truthy.Q3_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.Q3_K.gguf) | Q3_K | 4.84GB |
| [CarbonBeagle-11B-truthy.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.Q3_K_M.gguf) | Q3_K_M | 4.84GB |
| [CarbonBeagle-11B-truthy.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.Q3_K_L.gguf) | Q3_K_L | 5.26GB |
| [CarbonBeagle-11B-truthy.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.IQ4_XS.gguf) | IQ4_XS | 5.43GB |
| [CarbonBeagle-11B-truthy.Q4_0.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.Q4_0.gguf) | Q4_0 | 5.66GB |
| [CarbonBeagle-11B-truthy.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.IQ4_NL.gguf) | IQ4_NL | 5.72GB |
| [CarbonBeagle-11B-truthy.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.Q4_K_S.gguf) | Q4_K_S | 5.7GB |
| [CarbonBeagle-11B-truthy.Q4_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.Q4_K.gguf) | Q4_K | 6.02GB |
| [CarbonBeagle-11B-truthy.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.Q4_K_M.gguf) | Q4_K_M | 6.02GB |
| [CarbonBeagle-11B-truthy.Q4_1.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.Q4_1.gguf) | Q4_1 | 6.27GB |
| [CarbonBeagle-11B-truthy.Q5_0.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.Q5_0.gguf) | Q5_0 | 6.89GB |
| [CarbonBeagle-11B-truthy.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.Q5_K_S.gguf) | Q5_K_S | 6.89GB |
| [CarbonBeagle-11B-truthy.Q5_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.Q5_K.gguf) | Q5_K | 7.08GB |
| [CarbonBeagle-11B-truthy.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.Q5_K_M.gguf) | Q5_K_M | 7.08GB |
| [CarbonBeagle-11B-truthy.Q5_1.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.Q5_1.gguf) | Q5_1 | 7.51GB |
| [CarbonBeagle-11B-truthy.Q6_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.Q6_K.gguf) | Q6_K | 8.2GB |
| [CarbonBeagle-11B-truthy.Q8_0.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_CarbonBeagle-11B-truthy-gguf/blob/main/CarbonBeagle-11B-truthy.Q8_0.gguf) | Q8_0 | 10.62GB |
Original model description:
---
license: apache-2.0
library_name: transformers
datasets:
- jondurbin/truthy-dpo-v0.1
model-index:
- name: CarbonBeagle-11B-truthy
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 72.27
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/CarbonBeagle-11B-truthy
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 89.31
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/CarbonBeagle-11B-truthy
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.55
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/CarbonBeagle-11B-truthy
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 78.55
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/CarbonBeagle-11B-truthy
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 83.82
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/CarbonBeagle-11B-truthy
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.11
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/CarbonBeagle-11B-truthy
name: Open LLM Leaderboard
---
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__CarbonBeagle-11B-truthy)
| Metric |Value|
|---------------------------------|----:|
|Avg. |76.10|
|AI2 Reasoning Challenge (25-Shot)|72.27|
|HellaSwag (10-Shot) |89.31|
|MMLU (5-Shot) |66.55|
|TruthfulQA (0-shot) |78.55|
|Winogrande (5-shot) |83.82|
|GSM8k (5-shot) |66.11|
|
ifyou819/summary-news-dataset-3
|
ifyou819
| 2024-05-21T11:47:24Z | 103 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:ifyou819/summary-news-dataset-2",
"base_model:finetune:ifyou819/summary-news-dataset-2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-05-21T11:46:25Z |
---
base_model: ifyou819/summary-news-dataset-2
tags:
- generated_from_trainer
model-index:
- name: summary-news-dataset-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/fine-tune-gpt-model/huggingface/runs/v5j2dru6)
# summary-news-dataset-3
This model is a fine-tuned version of [ifyou819/summary-news-dataset-2](https://huggingface.co/ifyou819/summary-news-dataset-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 7.0676
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 8.214 | 1.0 | 791 | 7.4852 |
| 7.7963 | 2.0 | 1582 | 7.1671 |
| 7.696 | 3.0 | 2373 | 7.0676 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.19.1
|
Zoyd/TIGER-Lab_MAmmoTH2-8x7B-2_5bpw_exl2
|
Zoyd
| 2024-05-21T11:47:16Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"en",
"arxiv:2405.03548",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-05-21T07:56:27Z |
---
license: mit
language:
- en
---
**Exllamav2** quant (**exl2** / **2.5 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-2_2bpw_exl2)**</center> | <center>12762 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-2_5bpw_exl2)**</center> | <center>14191 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-3_0bpw_exl2)**</center> | <center>16931 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-3_5bpw_exl2)**</center> | <center>19724 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-3_75bpw_exl2)**</center> | <center>21098 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-4_0bpw_exl2)**</center> | <center>22488 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-4_25bpw_exl2)**</center> | <center>23875 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-5_0bpw_exl2)**</center> | <center>28028 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-6_0bpw_exl2)**</center> | <center>33588 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-6_5bpw_exl2)**</center> | <center>36031 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-8_0bpw_exl2)**</center> | <center>41342 MB</center> | <center>8</center> |
# 🦣 MAmmoTH2: Scaling Instructions from the Web
Project Page: [https://tiger-ai-lab.github.io/MAmmoTH2/](https://tiger-ai-lab.github.io/MAmmoTH2/)
Paper: [https://arxiv.org/pdf/2405.03548](https://arxiv.org/pdf/2405.03548)
Code: [https://github.com/TIGER-AI-Lab/MAmmoTH2](https://github.com/TIGER-AI-Lab/MAmmoTH2)
## Introduction
Introducing 🦣 MAmmoTH2, a game-changer in improving the reasoning abilities of large language models (LLMs) through innovative instruction tuning. By efficiently harvesting 10 million instruction-response pairs from the pre-training web corpus, we've developed MAmmoTH2 models that significantly boost performance on reasoning benchmarks. For instance, MAmmoTH2-7B (Mistral) sees its performance soar from 11% to 34% on MATH and from 36% to 67% on GSM8K, all without training on any domain-specific data. Further training on public instruction tuning datasets yields MAmmoTH2-Plus, setting new standards in reasoning and chatbot benchmarks. Our work presents a cost-effective approach to acquiring large-scale, high-quality instruction data, offering a fresh perspective on enhancing LLM reasoning abilities.
| | **Base Model** | **MAmmoTH2** | **MAmmoTH2-Plus** |
|:-----|:---------------------|:-------------------------------------------------------------------|:------------------------------------------------------------------|
| 7B | Mistral | 🦣 [MAmmoTH2-7B](https://huggingface.co/TIGER-Lab/MAmmoTH2-7B) | 🦣 [MAmmoTH2-7B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-7B-Plus) |
| 8B | Llama-3 | 🦣 [MAmmoTH2-8B](https://huggingface.co/TIGER-Lab/MAmmoTH2-8B) | 🦣 [MAmmoTH2-8B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-8B-Plus) |
| 8x7B | Mixtral | 🦣 [MAmmoTH2-8x7B](https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B) | 🦣 [MAmmoTH2-8x7B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B-Plus) |
## Training Data
Please refer to https://huggingface.co/datasets/TIGER-Lab/WebInstructSub for more details.
## Training Procedure
The models are fine-tuned with the WEBINSTRUCT dataset using the original Llama-3, Mistral and Mistal models as base models. The training procedure varies for different models based on their sizes. Check out our paper for more details.
## Evaluation
The models are evaluated using open-ended and multiple-choice math problems from several datasets. Here are the results:
| **Model** | **TheoremQA** | **MATH** | **GSM8K** | **GPQA** | **MMLU-ST** | **BBH** | **ARC-C** | **Avg** |
|:---------------------------------------|:--------------|:---------|:----------|:---------|:------------|:--------|:----------|:--------|
| **MAmmoTH2-7B** (Updated) | 29.0 | 36.7 | 68.4 | 32.4 | 62.4 | 58.6 | 81.7 | 52.7 |
| **MAmmoTH2-8B** (Updated) | 30.3 | 35.8 | 70.4 | 35.2 | 64.2 | 62.1 | 82.2 | 54.3 |
| **MAmmoTH2-8x7B** | 32.2 | 39.0 | 75.4 | 36.8 | 67.4 | 71.1 | 87.5 | 58.9 |
| **MAmmoTH2-7B-Plus** (Updated) | 31.2 | 46.0 | 84.6 | 33.8 | 63.8 | 63.3 | 84.4 | 58.1 |
| **MAmmoTH2-8B-Plus** (Updated) | 31.5 | 43.0 | 85.2 | 35.8 | 66.7 | 69.7 | 84.3 | 59.4 |
| **MAmmoTH2-8x7B-Plus** | 34.1 | 47.0 | 86.4 | 37.8 | 72.4 | 74.1 | 88.4 | 62.9 |
To reproduce our results, please refer to https://github.com/TIGER-AI-Lab/MAmmoTH2/tree/main/math_eval.
## Chat Format
The template used to build a prompt for the Instruct model is defined as follows:
```
<s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST]
```
Note that `<s>` and `</s>` are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings.
But we also found that the model is not very sensitive to the chat template.
## Usage
You can use the models through Huggingface's Transformers library. Use the pipeline function to create a text-generation pipeline with the model of your choice, then feed in a math problem to get the solution.
Check our Github repo for more advanced use: https://github.com/TIGER-AI-Lab/MAmmoTH2
## Limitations
We've tried our best to build math generalist models. However, we acknowledge that the models' performance may vary based on the complexity and specifics of the math problem. Still not all mathematical fields can be covered comprehensively.
## Citation
If you use the models, data, or code from this project, please cite the original paper:
```
@article{yue2024mammoth2,
title={MAmmoTH2: Scaling Instructions from the Web},
author={Yue, Xiang and Zheng, Tuney and Zhang, Ge and Chen, Wenhu},
journal={arXiv preprint arXiv:2405.03548},
year={2024}
}
```
|
Zoyd/TIGER-Lab_MAmmoTH2-8x7B-2_2bpw_exl2
|
Zoyd
| 2024-05-21T11:47:08Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"en",
"arxiv:2405.03548",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-05-21T07:32:03Z |
---
license: mit
language:
- en
---
**Exllamav2** quant (**exl2** / **2.2 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-2_2bpw_exl2)**</center> | <center>12762 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-2_5bpw_exl2)**</center> | <center>14191 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-3_0bpw_exl2)**</center> | <center>16931 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-3_5bpw_exl2)**</center> | <center>19724 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-3_75bpw_exl2)**</center> | <center>21098 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-4_0bpw_exl2)**</center> | <center>22488 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-4_25bpw_exl2)**</center> | <center>23875 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-5_0bpw_exl2)**</center> | <center>28028 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-6_0bpw_exl2)**</center> | <center>33588 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-6_5bpw_exl2)**</center> | <center>36031 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/TIGER-Lab_MAmmoTH2-8x7B-8_0bpw_exl2)**</center> | <center>41342 MB</center> | <center>8</center> |
# 🦣 MAmmoTH2: Scaling Instructions from the Web
Project Page: [https://tiger-ai-lab.github.io/MAmmoTH2/](https://tiger-ai-lab.github.io/MAmmoTH2/)
Paper: [https://arxiv.org/pdf/2405.03548](https://arxiv.org/pdf/2405.03548)
Code: [https://github.com/TIGER-AI-Lab/MAmmoTH2](https://github.com/TIGER-AI-Lab/MAmmoTH2)
## Introduction
Introducing 🦣 MAmmoTH2, a game-changer in improving the reasoning abilities of large language models (LLMs) through innovative instruction tuning. By efficiently harvesting 10 million instruction-response pairs from the pre-training web corpus, we've developed MAmmoTH2 models that significantly boost performance on reasoning benchmarks. For instance, MAmmoTH2-7B (Mistral) sees its performance soar from 11% to 34% on MATH and from 36% to 67% on GSM8K, all without training on any domain-specific data. Further training on public instruction tuning datasets yields MAmmoTH2-Plus, setting new standards in reasoning and chatbot benchmarks. Our work presents a cost-effective approach to acquiring large-scale, high-quality instruction data, offering a fresh perspective on enhancing LLM reasoning abilities.
| | **Base Model** | **MAmmoTH2** | **MAmmoTH2-Plus** |
|:-----|:---------------------|:-------------------------------------------------------------------|:------------------------------------------------------------------|
| 7B | Mistral | 🦣 [MAmmoTH2-7B](https://huggingface.co/TIGER-Lab/MAmmoTH2-7B) | 🦣 [MAmmoTH2-7B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-7B-Plus) |
| 8B | Llama-3 | 🦣 [MAmmoTH2-8B](https://huggingface.co/TIGER-Lab/MAmmoTH2-8B) | 🦣 [MAmmoTH2-8B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-8B-Plus) |
| 8x7B | Mixtral | 🦣 [MAmmoTH2-8x7B](https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B) | 🦣 [MAmmoTH2-8x7B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B-Plus) |
## Training Data
Please refer to https://huggingface.co/datasets/TIGER-Lab/WebInstructSub for more details.
## Training Procedure
The models are fine-tuned with the WEBINSTRUCT dataset using the original Llama-3, Mistral and Mistal models as base models. The training procedure varies for different models based on their sizes. Check out our paper for more details.
## Evaluation
The models are evaluated using open-ended and multiple-choice math problems from several datasets. Here are the results:
| **Model** | **TheoremQA** | **MATH** | **GSM8K** | **GPQA** | **MMLU-ST** | **BBH** | **ARC-C** | **Avg** |
|:---------------------------------------|:--------------|:---------|:----------|:---------|:------------|:--------|:----------|:--------|
| **MAmmoTH2-7B** (Updated) | 29.0 | 36.7 | 68.4 | 32.4 | 62.4 | 58.6 | 81.7 | 52.7 |
| **MAmmoTH2-8B** (Updated) | 30.3 | 35.8 | 70.4 | 35.2 | 64.2 | 62.1 | 82.2 | 54.3 |
| **MAmmoTH2-8x7B** | 32.2 | 39.0 | 75.4 | 36.8 | 67.4 | 71.1 | 87.5 | 58.9 |
| **MAmmoTH2-7B-Plus** (Updated) | 31.2 | 46.0 | 84.6 | 33.8 | 63.8 | 63.3 | 84.4 | 58.1 |
| **MAmmoTH2-8B-Plus** (Updated) | 31.5 | 43.0 | 85.2 | 35.8 | 66.7 | 69.7 | 84.3 | 59.4 |
| **MAmmoTH2-8x7B-Plus** | 34.1 | 47.0 | 86.4 | 37.8 | 72.4 | 74.1 | 88.4 | 62.9 |
To reproduce our results, please refer to https://github.com/TIGER-AI-Lab/MAmmoTH2/tree/main/math_eval.
## Chat Format
The template used to build a prompt for the Instruct model is defined as follows:
```
<s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST]
```
Note that `<s>` and `</s>` are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings.
But we also found that the model is not very sensitive to the chat template.
## Usage
You can use the models through Huggingface's Transformers library. Use the pipeline function to create a text-generation pipeline with the model of your choice, then feed in a math problem to get the solution.
Check our Github repo for more advanced use: https://github.com/TIGER-AI-Lab/MAmmoTH2
## Limitations
We've tried our best to build math generalist models. However, we acknowledge that the models' performance may vary based on the complexity and specifics of the math problem. Still not all mathematical fields can be covered comprehensively.
## Citation
If you use the models, data, or code from this project, please cite the original paper:
```
@article{yue2024mammoth2,
title={MAmmoTH2: Scaling Instructions from the Web},
author={Yue, Xiang and Zheng, Tuney and Zhang, Ge and Chen, Wenhu},
journal={arXiv preprint arXiv:2405.03548},
year={2024}
}
```
|
Schaolone/zlg
|
Schaolone
| 2024-05-21T11:42:34Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2024-05-21T11:35:10Z |
---
license: apache-2.0
---
|
Ramikan-BR/tinyllama-coder-py-4bit_LORA-v3
|
Ramikan-BR
| 2024-05-21T11:40:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/tinyllama-chat-bnb-4bit",
"base_model:finetune:unsloth/tinyllama-chat-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-21T11:40:12Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/tinyllama-chat-bnb-4bit
---
# Uploaded model
- **Developed by:** Ramikan-BR
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-chat-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Likich/mistral-finetune-qualcoding-1000-prompt1
|
Likich
| 2024-05-21T11:40:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-21T11:40:31Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
HigginsAI/GrepBiasLlama
|
HigginsAI
| 2024-05-21T11:35:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-21T11:34:55Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** HigginsAI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
wangzhekd/blip-opt-2.7b-football-alltest
|
wangzhekd
| 2024-05-21T11:31:53Z | 62 | 0 |
transformers
|
[
"transformers",
"safetensors",
"blip",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-05-12T09:14:17Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Dhahlan2000/Translation-GPT-v4
|
Dhahlan2000
| 2024-05-21T11:30:26Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"base_model:Dhahlan2000/Translation-GPT-v3",
"base_model:finetune:Dhahlan2000/Translation-GPT-v3",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-05-21T11:29:05Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
base_model: Dhahlan2000/Translation-GPT-v3
model-index:
- name: Translation-GPT-v4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Translation-GPT-v4
This model is a fine-tuned version of [Dhahlan2000/Translation-GPT-v3](https://huggingface.co/Dhahlan2000/Translation-GPT-v3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.8506
- Validation Loss: 2.2484
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.0246 | 2.3872 | 0 |
| 2.8506 | 2.2484 | 1 |
### Framework versions
- Transformers 4.40.2
- TensorFlow 2.15.0
- Datasets 2.17.0
- Tokenizers 0.19.1
|
MaziyarPanahi/Experiment27pasticheYamshadowexperiment28-7B-GGUF
|
MaziyarPanahi
| 2024-05-21T11:28:15Z | 58 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:automerger/YamshadowExperiment28-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:automerger/Experiment27pasticheYamshadowexperiment28-7B",
"base_model:quantized:automerger/Experiment27pasticheYamshadowexperiment28-7B"
] |
text-generation
| 2024-05-21T10:59:18Z |
---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- merge
- mergekit
- lazymergekit
- automerger
- base_model:automerger/YamshadowExperiment28-7B
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
- text-generation
model_name: Experiment27pasticheYamshadowexperiment28-7B-GGUF
base_model: automerger/Experiment27pasticheYamshadowexperiment28-7B
inference: false
model_creator: automerger
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/Experiment27pasticheYamshadowexperiment28-7B-GGUF](https://huggingface.co/MaziyarPanahi/Experiment27pasticheYamshadowexperiment28-7B-GGUF)
- Model creator: [automerger](https://huggingface.co/automerger)
- Original model: [automerger/Experiment27pasticheYamshadowexperiment28-7B](https://huggingface.co/automerger/Experiment27pasticheYamshadowexperiment28-7B)
## Description
[MaziyarPanahi/Experiment27pasticheYamshadowexperiment28-7B-GGUF](https://huggingface.co/MaziyarPanahi/Experiment27pasticheYamshadowexperiment28-7B-GGUF) contains GGUF format model files for [automerger/Experiment27pasticheYamshadowexperiment28-7B](https://huggingface.co/automerger/Experiment27pasticheYamshadowexperiment28-7B).
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
|
arjuntheprogrammer/llama3-8b-oig-unsloth
|
arjuntheprogrammer
| 2024-05-21T11:24:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-21T11:24:04Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** arjuntheprogrammer
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
arjuntheprogrammer/llama3-8b-oig-unsloth-merged
|
arjuntheprogrammer
| 2024-05-21T11:23:55Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-21T11:18:32Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** arjuntheprogrammer
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
blockblockblock/Llama-3-70B-Instruct-abliterated-v3-bpw3.7-exl2
|
blockblockblock
| 2024-05-21T11:19:51Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-05-21T11:16:09Z |
---
library_name: transformers
license: llama3
---
# Llama-3-70B-Instruct-abliterated-v3 Model Card
[My Jupyter "cookbook" to replicate the methodology can be found here, refined library coming soon](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb)
This is [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more.
## Hang on, "abliteration"? Orthogonalization? Ablation? What is this?
TL;DR: This model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal directions orthogonalized out.
**TL;TL;DR;DR: It's uncensored in the purest form I can manage -- no new or changed behaviour in any other respect from the original model.**
As far as "abliteration": it's just a fun play-on-words using the original "ablation" term used in the original paper to refer to removing features, which I made up particularly to differentiate the model from "uncensored" fine-tunes.
Ablate + obliterated = Abliterated
Anyways, orthogonalization/ablation are both aspects to refer to the same thing here, the technique in which the refusal feature was "ablated" from the model was via orthogonalization.
## A little more on the methodology, and why this is interesting
To me, ablation (or applying the methodology for the inverse, "augmentation") seems to be good for inducing/removing very specific features that you'd have to spend way too many tokens on encouraging or discouraging in your system prompt.
Instead, you just apply your system prompt in the ablation script against a blank system prompt on the same dataset and orthogonalize for the desired behaviour in the final model weights.
> Why this over fine-tuning?
Ablation is much more surgical in nature whilst also being effectively executed with a _lot_ less data than fine-tuning, which I think is its main advantage.
As well, and its most valuable aspect is it keeps as much of the original model's knowledge and training intact, whilst removing its tendency to behave in one very specific undesireable manner. (In this case, refusing user requests.)
Fine tuning is still exceptionally useful and the go-to for broad behaviour changes; however, you may be able to get close to your desired behaviour with very few samples using the ablation/augmentation techniques.
It may also be a useful step to add to your model refinement: orthogonalize -> fine-tune or vice-versa.
I haven't really gotten around to exploring this model stacked with fine-tuning, I encourage others to give it a shot if they've got the capacity.
> Okay, fine, but why V3? There's no V2 70B?
Well, I released a V2 a while back for 8B under Cognitive Computations.
It ended up being not worth it to try V2 with 70B, I wanted to refine the model before wasting compute cycles on what might not even be a better model.
I am however quite pleased about this latest methodology, it seems to have induced fewer hallucinations.
So to show that it's a new fancy methodology from even that of the 8B V2, I decided to do a Microsoft and double up on my version jump because it's *such* an advancement (or so the excuse went, when in actuality it was because too many legacy but actively used Microsoft libraries checked for 'Windows 9' in the OS name to detect Windows 95/98 as one.)
## Quirkiness awareness notice
This model may come with interesting quirks, with the methodology being so new. I encourage you to play with the model, and post any quirks you notice in the community tab, as that'll help us further understand what this orthogonalization has in the way of side effects.
If you manage to develop further improvements, please share! This is really the most basic way to use ablation, but there are other possibilities that I believe are as-yet unexplored.
Additionally, feel free to reach out in any way about this. I'm on the Cognitive Computations Discord, I'm watching the Community tab, reach out! I'd love to see this methodology used in other ways, and so would gladly support whoever whenever I can.
|
krispychicken/openai-whisper-medium.en-colab
|
krispychicken
| 2024-05-21T11:18:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-21T11:18:37Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
yzhuang/Meta-Llama-3-8B-Instruct_fictional_gsm8k_French_v1
|
yzhuang
| 2024-05-21T11:12:20Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:generator",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-20T01:30:17Z |
---
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
model-index:
- name: Meta-Llama-3-8B-Instruct_fictional_gsm8k_French_v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/yufanz/autotree/runs/7283704781.17487-9818c277-4a86-4343-b288-7864588621de)
# Meta-Llama-3-8B-Instruct_fictional_gsm8k_French_v1
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
### Framework versions
- Transformers 4.41.0
- Pytorch 2.1.0a0+32f93b1
- Datasets 2.19.1
- Tokenizers 0.19.1
|
SunnyAxe/bert_NER_task
|
SunnyAxe
| 2024-05-21T11:12:12Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-05-21T10:41:04Z |
# Introduction
本模型是在SRTP项目中，为中文文学领域文献摘要的命名实体识别（人名、国名与书名）任务而训练的基于RoBERT的模型。
# Format of input and output
input最大长度128。
input: 文本；output: 与文本长度对应、位置对应的标记，标记有如下七种：
{'O': 无标记, 'B-PER': 人名开始标记, 'I-PER': 人名中间标记, 'B-CNT': 国名开始标记, 'I-CNT': 国名中间标记, 'B-BK': 书名开始标记, 'I-BK': 书名中间标记}
例如：
input: 谢默斯・希尼是当代爱尔兰著名诗人
output: B-PER I-PER I-PER I-PER I-PER I-PER O O O B-CNT I-CNT I-CNT O O O O O
另外，由于模型能力有限，在推理过程中可能遇到识别出来的实体标记直接从"I-"开始，建议将第一个标记向前一个文字作为对应的"B-"标记。
如：爱尔兰 --推理--> O I-CNT I-CNT --后续处理--> B-CNT I-CNT I-CNT
|
Jefferson-bueno/lora_model_unsloth
|
Jefferson-bueno
| 2024-05-21T11:11:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-21T11:11:32Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** Jefferson-bueno
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
SunnyAxe/bert_country_infer
|
SunnyAxe
| 2024-05-21T11:10:04Z | 108 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-21T11:02:33Z |
# Introduction
本模型是在SRTP项目中，为中文文学领域文献的国别板块推理任务而训练的基于BERT的模型。
# Format of input and output
input文本最大长度150，label个数11。
label与推理出的数值对应如下：
{0: '比较文学', 1: '大洋洲', 2: '东欧、北欧', 3: '翻译研究', 4: '非洲', 5: '加拿大及其他美洲国家', 6: '美国', 7: '文艺理论与批评', 8: '西欧', 9: '亚洲', 10: '中欧、南欧'}
input: 论文标题+论文关键词+论文摘要（拼接） output: 板块（0~10）
|
lupobricco/relation_classification_single_label_correlations
|
lupobricco
| 2024-05-21T11:08:36Z | 104 | 0 |
transformers
|
[
"transformers",
"safetensors",
"camembert",
"text-classification",
"generated_from_trainer",
"base_model:Musixmatch/umberto-commoncrawl-cased-v1",
"base_model:finetune:Musixmatch/umberto-commoncrawl-cased-v1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-21T10:53:40Z |
---
base_model: Musixmatch/umberto-commoncrawl-cased-v1
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: relation_classification_single_label_correlations
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# relation_classification_single_label_correlations
This model is a fine-tuned version of [Musixmatch/umberto-commoncrawl-cased-v1](https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8244
- Accuracy: 0.7209
- F1: 0.7003
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 121 | 0.7878 | 0.6279 | 0.6332 |
| No log | 2.0 | 242 | 0.8169 | 0.6822 | 0.6664 |
| No log | 3.0 | 363 | 0.8244 | 0.7209 | 0.7003 |
| No log | 4.0 | 484 | 0.9999 | 0.6977 | 0.6898 |
| 0.5583 | 5.0 | 605 | 1.2330 | 0.6744 | 0.6372 |
| 0.5583 | 6.0 | 726 | 1.4526 | 0.6434 | 0.5751 |
| 0.5583 | 7.0 | 847 | 1.5028 | 0.6434 | 0.5853 |
| 0.5583 | 8.0 | 968 | 1.6308 | 0.6512 | 0.6008 |
| 0.1534 | 9.0 | 1089 | 1.7342 | 0.6434 | 0.5817 |
| 0.1534 | 10.0 | 1210 | 1.7693 | 0.6512 | 0.5468 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
|
Gkumi/Distilled-bert-based
|
Gkumi
| 2024-05-21T11:06:47Z | 116 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-05-21T11:06:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
edg3/distilbert-base-uncased-imdb
|
edg3
| 2024-05-21T11:06:09Z | 110 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-20T11:31:23Z |
---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-imdb
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2291
- Accuracy: 0.9321
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2225 | 1.0 | 1563 | 0.2124 | 0.9194 |
| 0.1451 | 2.0 | 3126 | 0.2291 | 0.9321 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
KangXen/enta-st-xlmr
|
KangXen
| 2024-05-21T11:00:34Z | 165 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-21T10:59:50Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
NLP-FEUP/DA-FT-ProsusAI-finbert
|
NLP-FEUP
| 2024-05-21T11:00:18Z | 109 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:NLP-FEUP/DA-ProsusAI-finbert",
"base_model:finetune:NLP-FEUP/DA-ProsusAI-finbert",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-20T15:19:37Z |
---
base_model: NLP-FEUP/DA-ProsusAI-finbert
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: DA-FT-ProsusAI-finbert
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DA-FT-ProsusAI-finbert
This model is a fine-tuned version of [NLP-FEUP/DA-ProsusAI-finbert](https://huggingface.co/NLP-FEUP/DA-ProsusAI-finbert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3766
- Accuracy: 0.875
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 0.5243 | 0.675 |
| No log | 2.0 | 80 | 0.4332 | 0.775 |
| No log | 3.0 | 120 | 0.3766 | 0.875 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
|
NLP-FEUP/DA-FT-distilbert-base-uncased
|
NLP-FEUP
| 2024-05-21T11:00:15Z | 121 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:NLP-FEUP/DA-distilbert-base-uncased",
"base_model:finetune:NLP-FEUP/DA-distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-20T15:19:06Z |
---
license: apache-2.0
base_model: NLP-FEUP/DA-distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: DA-FT-distilbert-base-uncased
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DA-FT-distilbert-base-uncased
This model is a fine-tuned version of [NLP-FEUP/DA-distilbert-base-uncased](https://huggingface.co/NLP-FEUP/DA-distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6205
- Accuracy: 0.725
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 0.6767 | 0.625 |
| No log | 2.0 | 80 | 0.6476 | 0.675 |
| No log | 3.0 | 120 | 0.6205 | 0.725 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
|
bhoopendrakumar/passport_330
|
bhoopendrakumar
| 2024-05-21T10:59:22Z | 48 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-05-21T10:56:34Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
KitsuneX07/so-vits-svc4.1_mosquito
|
KitsuneX07
| 2024-05-21T10:58:42Z | 0 | 2 | null |
[
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2024-05-21T10:54:22Z |
---
license: cc-by-nc-sa-4.0
---
Offcial Website：https://github.com/svc-develop-team/so-vits-svc
|
Jateendra/tiny-chatbot-dpo
|
Jateendra
| 2024-05-21T10:58:13Z | 3 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"region:us"
] | null | 2024-05-21T10:56:03Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- dpo
- generated_from_trainer
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
model-index:
- name: tiny-chatbot-dpo
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-chatbot-dpo
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
LinStevenn/model8bit_itri_0
|
LinStevenn
| 2024-05-21T10:52:11Z | 6 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:quantized:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-21T10:36:48Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** LinStevenn
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
MaziyarPanahi/T3qInex12-7B-GGUF
|
MaziyarPanahi
| 2024-05-21T10:49:27Z | 81 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:chihoonlee10/T3Q-Mistral-Orca-Math-DPO",
"base_model:MSL7/INEX12-7b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:automerger/T3qInex12-7B",
"base_model:quantized:automerger/T3qInex12-7B"
] |
text-generation
| 2024-05-21T10:20:39Z |
---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- merge
- mergekit
- lazymergekit
- automerger
- base_model:chihoonlee10/T3Q-Mistral-Orca-Math-DPO
- base_model:MSL7/INEX12-7b
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
- text-generation
model_name: T3qInex12-7B-GGUF
base_model: automerger/T3qInex12-7B
inference: false
model_creator: automerger
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/T3qInex12-7B-GGUF](https://huggingface.co/MaziyarPanahi/T3qInex12-7B-GGUF)
- Model creator: [automerger](https://huggingface.co/automerger)
- Original model: [automerger/T3qInex12-7B](https://huggingface.co/automerger/T3qInex12-7B)
## Description
[MaziyarPanahi/T3qInex12-7B-GGUF](https://huggingface.co/MaziyarPanahi/T3qInex12-7B-GGUF) contains GGUF format model files for [automerger/T3qInex12-7B](https://huggingface.co/automerger/T3qInex12-7B).
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
|
ytcheng/llama3-70B-lora-pretrain_v2
|
ytcheng
| 2024-05-21T10:47:04Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-70B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-70B-Instruct",
"license:llama3",
"region:us"
] | null | 2024-05-20T06:10:36Z |
---
license: llama3
library_name: peft
tags:
- llama-factory
- lora
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-70B-Instruct
model-index:
- name: llama3-70B-lora-pretrain_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama3-70B-lora-pretrain_v2
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) on the sm_artile dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9382
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.6995 | 0.0939 | 100 | 2.6305 |
| 2.4199 | 0.1877 | 200 | 2.3979 |
| 2.2722 | 0.2816 | 300 | 2.2180 |
| 2.0762 | 0.3754 | 400 | 2.1251 |
| 1.9652 | 0.4693 | 500 | 2.0858 |
| 2.1893 | 0.5631 | 600 | 2.0629 |
| 2.0153 | 0.6570 | 700 | 2.0473 |
| 1.9911 | 0.7508 | 800 | 2.0318 |
| 2.1041 | 0.8447 | 900 | 2.0198 |
| 2.0488 | 0.9385 | 1000 | 2.0117 |
| 1.897 | 1.0324 | 1100 | 2.0018 |
| 2.0298 | 1.1262 | 1200 | 1.9952 |
| 2.0989 | 1.2201 | 1300 | 1.9890 |
| 1.8695 | 1.3139 | 1400 | 1.9838 |
| 2.1573 | 1.4078 | 1500 | 1.9764 |
| 2.0183 | 1.5016 | 1600 | 1.9713 |
| 1.9229 | 1.5955 | 1700 | 1.9672 |
| 1.9732 | 1.6893 | 1800 | 1.9617 |
| 1.6835 | 1.7832 | 1900 | 1.9574 |
| 1.9874 | 1.8771 | 2000 | 1.9539 |
| 1.7607 | 1.9709 | 2100 | 1.9512 |
| 1.9459 | 2.0648 | 2200 | 1.9480 |
| 1.7611 | 2.1586 | 2300 | 1.9463 |
| 1.8491 | 2.2525 | 2400 | 1.9441 |
| 1.9121 | 2.3463 | 2500 | 1.9427 |
| 1.8849 | 2.4402 | 2600 | 1.9413 |
| 2.0679 | 2.5340 | 2700 | 1.9400 |
| 1.9908 | 2.6279 | 2800 | 1.9394 |
| 1.9557 | 2.7217 | 2900 | 1.9388 |
| 1.9627 | 2.8156 | 3000 | 1.9384 |
| 1.8339 | 2.9094 | 3100 | 1.9383 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.19.1
|
joosma/ppo-v3
|
joosma
| 2024-05-21T10:40:39Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-05-21T10:31:59Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -151.06 +/- 77.67
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 1000000
'learning_rate': 0.0002
'num_envs': 20
'num_steps': 2048
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 10
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'joosma/ppo-v3'
'batch_size': 40960
'minibatch_size': 4096}
```
|
RichardErkhov/prometheus-eval_-_prometheus-7b-v2.0-4bits
|
RichardErkhov
| 2024-05-21T10:40:10Z | 78 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:2405.01535",
"arxiv:2310.08491",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-05-21T10:32:36Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
prometheus-7b-v2.0 - bnb 4bits
- Model creator: https://huggingface.co/prometheus-eval/
- Original model: https://huggingface.co/prometheus-eval/prometheus-7b-v2.0/
Original model description:
---
tags:
- text2text-generation
datasets:
- prometheus-eval/Feedback-Collection
- prometheus-eval/Preference-Collection
license: apache-2.0
language:
- en
pipeline_tag: text2text-generation
library_name: transformers
metrics:
- pearsonr
- spearmanr
- kendall-tau
- accuracy
---
## Links for Reference
- **Homepage: In Progress**
- **Repository:https://github.com/prometheus-eval/prometheus-eval**
- **Paper:https://arxiv.org/abs/2405.01535**
- **Point of Contact:[email protected]**
# TL;DR
Prometheus 2 is an alternative of GPT-4 evaluation when doing fine-grained evaluation of an underlying LLM & a Reward model for Reinforcement Learning from Human Feedback (RLHF).
Prometheus 2 is a language model using [Mistral-Instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) as a base model.
It is fine-tuned on 100K feedback within the [Feedback Collection](https://huggingface.co/datasets/prometheus-eval/Feedback-Collection) and 200K feedback within the [Preference Collection](https://huggingface.co/datasets/prometheus-eval/Preference-Collection).
It is also made by weight merging to support both absolute grading (direct assessment) and relative grading (pairwise ranking).
The surprising thing is that we find weight merging also improves performance on each format.
# Model Details
## Model Description
- **Model type:** Language model
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Related Models:** [All Prometheus Checkpoints](https://huggingface.co/models?search=prometheus-eval/Prometheus)
- **Resources for more information:**
- [Research paper](https://arxiv.org/abs/2405.01535)
- [GitHub Repo](https://github.com/prometheus-eval/prometheus-eval)
Prometheus is trained with two different sizes (7B and 8x7B).
You could check the 8x7B sized LM on [this page](https://huggingface.co/prometheus-eval/prometheus-2-8x7b-v2.0).
Also, check out our dataset as well on [this page](https://huggingface.co/datasets/prometheus-eval/Feedback-Collection) and [this page](https://huggingface.co/datasets/prometheus-eval/Preference-Collection).
## Prompt Format
We have made wrapper functions and classes to conveniently use Prometheus 2 at [our github repository](https://github.com/prometheus-eval/prometheus-eval).
We highly recommend you use it!
However, if you just want to use the model for your use case, please refer to the prompt format below.
Note that absolute grading and relative grading requires different prompt templates and system prompts.
### Absolute Grading (Direct Assessment)
Prometheus requires 4 components in the input: An instruction, a response to evaluate, a score rubric, and a reference answer. You could refer to the prompt format below.
You should fill in the instruction, response, reference answer, criteria description, and score description for score in range of 1 to 5.
Fix the components with \{text\} inside.
```
###Task Description:
An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given.
1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general.
2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric.
3. The output format should look as follows: \"Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)\"
4. Please do not generate any other opening, closing, and explanations.
###The instruction to evaluate:
{orig_instruction}
###Response to evaluate:
{orig_response}
###Reference Answer (Score 5):
{orig_reference_answer}
###Score Rubrics:
[{orig_criteria}]
Score 1: {orig_score1_description}
Score 2: {orig_score2_description}
Score 3: {orig_score3_description}
Score 4: {orig_score4_description}
Score 5: {orig_score5_description}
###Feedback:
```
After this, you should apply the conversation template of Mistral (not applying it might lead to unexpected behaviors).
You can find the conversation class at this [link](https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py).
```
conv = get_conv_template("mistral")
conv.set_system_message("You are a fair judge assistant tasked with providing clear, objective feedback based on specific criteria, ensuring each assessment reflects the absolute standards set for performance.")
conv.append_message(conv.roles[0], dialogs['instruction'])
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
x = tokenizer(prompt,truncation=False)
```
As a result, a feedback and score decision will be generated, divided by a separating phrase ```[RESULT]```
### Relative Grading (Pairwise Ranking)
Prometheus requires 4 components in the input: An instruction, 2 responses to evaluate, a score rubric, and a reference answer. You could refer to the prompt format below.
You should fill in the instruction, 2 responses, reference answer, and criteria description.
Fix the components with \{text\} inside.
```
###Task Description:
An instruction (might include an Input inside it), a response to evaluate, and a score rubric representing a evaluation criteria are given.
1. Write a detailed feedback that assess the quality of two responses strictly based on the given score rubric, not evaluating in general.
2. After writing a feedback, choose a better response between Response A and Response B. You should refer to the score rubric.
3. The output format should look as follows: "Feedback: (write a feedback for criteria) [RESULT] (A or B)"
4. Please do not generate any other opening, closing, and explanations.
###Instruction:
{orig_instruction}
###Response A:
{orig_response_A}
###Response B:
{orig_response_B}
###Reference Answer:
{orig_reference_answer}
###Score Rubric:
{orig_criteria}
###Feedback:
```
After this, you should apply the conversation template of Mistral (not applying it might lead to unexpected behaviors).
You can find the conversation class at this [link](https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py).
```
conv = get_conv_template("mistral")
conv.set_system_message("You are a fair judge assistant assigned to deliver insightful feedback that compares individual performances, highlighting how each stands relative to others within the same cohort.")
conv.append_message(conv.roles[0], dialogs['instruction'])
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
x = tokenizer(prompt,truncation=False)
```
As a result, a feedback and score decision will be generated, divided by a separating phrase ```[RESULT]```
## License
Feedback Collection, Preference Collection, and Prometheus 2 are subject to OpenAI's Terms of Use for the generated data. If you suspect any violations, please reach out to us.
# Citation
If you find the following model helpful, please consider citing our paper!
**BibTeX:**
```bibtex
@misc{kim2023prometheus,
title={Prometheus: Inducing Fine-grained Evaluation Capability in Language Models},
author={Seungone Kim and Jamin Shin and Yejin Cho and Joel Jang and Shayne Longpre and Hwaran Lee and Sangdoo Yun and Seongjin Shin and Sungdong Kim and James Thorne and Minjoon Seo},
year={2023},
eprint={2310.08491},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{kim2024prometheus,
title={Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models},
author={Seungone Kim and Juyoung Suk and Shayne Longpre and Bill Yuchen Lin and Jamin Shin and Sean Welleck and Graham Neubig and Moontae Lee and Kyungjae Lee and Minjoon Seo},
year={2024},
eprint={2405.01535},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
JL42/NewMes-v6-GGUF
|
JL42
| 2024-05-21T10:35:56Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"Text Generation",
"medical",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-21T08:17:57Z |
---
license: llama3
library_name: transformers
tags:
- Text Generation
- medical
---
Base model: Llama-3-8B
## Model Description
- **Developed by:** bongbongs
- **Model type:** LLM
- **Language(s) (NLP):** English
- **Finetuned from model:** llama-3-8b
Fine-tuned on medical training datsets
|
KangXen/enta-tp3-xlmr
|
KangXen
| 2024-05-21T10:31:56Z | 184 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-21T10:31:19Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
joosma/ppo-v2
|
joosma
| 2024-05-21T10:28:56Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-05-21T10:27:27Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -170.62 +/- 65.46
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 100000
'learning_rate': 0.00025
'num_envs': 2048
'num_steps': 10
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'joosma/ppo-v2'
'batch_size': 20480
'minibatch_size': 5120}
```
|
UocNTh/mistral_instruct_generation
|
UocNTh
| 2024-05-21T10:26:04Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-05-21T10:25:46Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.1
datasets:
- generator
model-index:
- name: mistral_instruct_generation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral_instruct_generation
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3066
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5498 | 0.16 | 20 | 1.3757 |
| 1.4516 | 0.33 | 40 | 1.3326 |
| 1.4367 | 0.49 | 60 | 1.3214 |
| 1.4238 | 0.65 | 80 | 1.3123 |
| 1.4189 | 0.81 | 100 | 1.3066 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0
|
imagepipeline/helper
|
imagepipeline
| 2024-05-21T10:24:44Z | 0 | 0 | null |
[
"imagepipeline",
"imagepipeline.io",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-05-21T10:24:42Z |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
## helper
<img src="https://via.placeholder.com/468x300?text=App+Screenshot+Here" alt="Generated on Image Pipeline" style="border-radius: 10px;">
**This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/)**
Model details - helper
[](https://imagepipeline.io/models/helper?id=ae4e645b-a2d6-429e-ac44-d59e9f9e6f20/)
## How to try this model ?
You can try using it locally or send an API call to test the output quality.
Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required.
Coding in `php` `javascript` `node` etc ? Checkout our documentation
[](https://docs.imagepipeline.io/docs/introduction)
```python
import requests
import json
url = "https://imagepipeline.io/sd/text2image/v1/run"
payload = json.dumps({
"model_id": "sd1.5",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": false,
"guidance_scale": 7.5,
"multi_lingual": "no",
"embeddings": "",
"lora_models": "ae4e645b-a2d6-429e-ac44-d59e9f9e6f20",
"lora_weights": "0.5"
})
headers = {
'Content-Type': 'application/json',
'API-Key': 'your_api_key'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
}
```
Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` :
[](https://imagepipeline.io/models)
### API Reference
#### Generate Image
```http
https://api.imagepipeline.io/sd/text2image/v1
```
| Headers | Type | Description |
|:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------|
| `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) |
| `Content-Type` | `str` | application/json - content type of the request body |
| Parameter | Type | Description |
| :-------- | :------- | :------------------------- |
| `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own|
| `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips |
| `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) |
| `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 |
| `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page |
| `lora_weights` | `str, array` | Strength of the LoRA effect |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
### Feedback
If you have any feedback, please reach out to us at [email protected]
#### 🔗 Visit Website
[](https://imagepipeline.io/)
If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
|
hgnoi/dippy6
|
hgnoi
| 2024-05-21T10:24:17Z | 120 | 0 |
transformers
|
[
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-21T07:01:38Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
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