modelId
stringlengths 4
122
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
74.7M
| likes
int64 0
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| library_name
stringlengths 2
84
⌀ | tags
list | pipeline_tag
stringlengths 5
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| embedding
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perevalov/AntModel-7B-XLLM-test-lcquad-1-Demo
|
perevalov
| 2023-12-14T14:55:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] |
text-generation
| 2023-12-13T20:43:17Z |
Entry not found
|
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] |
maxkretchmer/output
|
maxkretchmer
| 2023-12-18T01:17:45Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:mistralai/Mixtral-8x7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T20:44:34Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: output
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. -->
# output
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) 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: 2.5e-05
- train_batch_size: 6
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 6
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
danielmalencar/llama-2-7b-miniguanaco
|
danielmalencar
| 2023-12-13T20:57:53Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T20:47:36Z |
Entry not found
|
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] |
Arekku21/vivit-b-16x2-kinetics400-finetuned-MSL_40_classes_6
|
Arekku21
| 2023-12-13T22:09:29Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vivit",
"video-classification",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-12-13T20:49:25Z |
Entry not found
|
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EmmaGthn/results_lora_40_10000_bias
|
EmmaGthn
| 2023-12-14T00:12:41Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-13T20:52:54Z |
Entry not found
|
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ADISH007/Aws_donut_10k_incremental_1_Epoch_13
|
ADISH007
| 2023-12-13T20:54:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null | 2023-12-13T20:53:50Z |
Entry not found
|
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sdadasfgdfgfdg/Dudao_Turma_do_dudao_LuanKCT
|
sdadasfgdfgfdg
| 2023-12-13T20:58:43Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T20:57:41Z |
---
license: openrail
---
|
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bartowski/Inkbot-13B-8k-0.2-exl2
|
bartowski
| 2023-12-14T04:48:53Z | 0 | 0 | null |
[
"text-generation",
"region:us"
] |
text-generation
| 2023-12-13T21:00:11Z |
---
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of Inkbot-13B-8k-0.2
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.10">turboderp's ExLlamaV2 v0.0.10</a> for quantization.
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using VMWareOpenInstruct.parquet as calibration dataset.
Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
Original model: https://huggingface.co/Tostino/Inkbot-13B-8k-0.2
<a href="https://huggingface.co/bartowski/Inkbot-13B-8k-0.2-exl2/tree/4_0">4.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/Inkbot-13B-8k-0.2-exl2/tree/6_5">6.5 bits per weight</a>
<a href="https://huggingface.co/bartowski/Inkbot-13B-8k-0.2-exl2/tree/8_0">8.0 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 4_0 https://huggingface.co/bartowski/Inkbot-13B-8k-0.2-exl2
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Inkbot-13B-8k-0.2-exl2`:
```shell
mkdir Inkbot-13B-8k-0.2-exl2
huggingface-cli download bartowski/Inkbot-13B-8k-0.2-exl2 --local-dir Inkbot-13B-8k-0.2-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Inkbot-13B-8k-0.2-exl2
huggingface-cli download bartowski/Inkbot-13B-8k-0.2-exl2 --revision 4_0 --local-dir Inkbot-13B-8k-0.2-exl2 --local-dir-use-symlinks False
```
|
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] |
jkot/transormer_en_cs_translator
|
jkot
| 2023-12-13T22:38:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T21:00:35Z |
To train model and run evaluation:
Download and extract the europarl cs_en from https://www.statmt.org/europarl/ to datasets/europarl/ folder
Install requirements using pip install -r requirements.txt
Run scripts in order create_dataset_splits.py, train_tokenizers.py, preprocess_dataset.py, train.py and eval.py
In the models folder, there is already trained model in .keras format and there are also dataset splits and pretrained tokenizers, so you can run evaluation directly
|
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ericmarcellus/ronaldinhomodel
|
ericmarcellus
| 2023-12-13T21:19:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T21:06:06Z |
Entry not found
|
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maxmashup/SOMI
|
maxmashup
| 2023-12-13T21:08:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T21:08:01Z |
Entry not found
|
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trng1305/layoutlmv2-sroie-finetunev2
|
trng1305
| 2023-12-13T21:08:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T21:08:31Z |
Entry not found
|
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] |
Homiebear/DoomTestament
|
Homiebear
| 2023-12-13T21:14:05Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T21:13:47Z |
---
license: openrail
---
|
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arpachat/output-fashion-test
|
arpachat
| 2023-12-13T21:14:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T21:14:00Z |
Entry not found
|
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] |
Loren85/Adam-hazbin-Hotel-Treiler-versionV1-V2
|
Loren85
| 2023-12-13T21:17:52Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T21:16:37Z |
---
license: openrail
---
|
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bio-datasets/Mistral-7B-Instruct-v0.1-polar-resonance-192-epoch-0
|
bio-datasets
| 2023-12-13T21:18:02Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-12-13T21:17:59Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
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] |
platzi/platzi-distilroberta-base-mrpc-glue-daniel-sanchez
|
platzi
| 2023-12-13T21:21:44Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T21:18:24Z |
---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: platzi-distilroberta-base-mrpc-glue-daniel-sanchez
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: datasetX
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8382352941176471
- name: F1
type: f1
value: 0.8795620437956205
---
<!-- 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. -->
# platzi-distilroberta-base-mrpc-glue-daniel-sanchez
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the datasetX dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5062
- Accuracy: 0.8382
- F1: 0.8796
## 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
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5164 | 1.09 | 500 | 0.5776 | 0.8186 | 0.8706 |
| 0.3479 | 2.18 | 1000 | 0.5062 | 0.8382 | 0.8796 |
### Framework versions
- Transformers 4.29.0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.13.3
|
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] |
bio-datasets/Mistral-7B-Instruct-v0.1-upbeat-brook-193-epoch-0
|
bio-datasets
| 2023-12-13T21:50:01Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-12-13T21:22:21Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
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] |
Frixi/VictoriaLM_Girl
|
Frixi
| 2023-12-13T21:26:10Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T21:25:38Z |
---
license: openrail
---
|
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] |
Frixi/American_E-Girl
|
Frixi
| 2023-12-13T21:30:29Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T21:28:15Z |
---
license: openrail
---
|
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amongglue/max_16bit
|
amongglue
| 2023-12-13T21:31:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T21:29:58Z |
Entry not found
|
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] |
Lilidiasaja/newmodel7
|
Lilidiasaja
| 2023-12-18T00:06:35Z | 0 | 0 | null |
[
"pytorch",
"license:afl-3.0",
"region:us"
] | null | 2023-12-13T21:30:58Z |
---
license: afl-3.0
---
|
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gunkaynar/xlnet-base-cased-SST-2
|
gunkaynar
| 2023-12-14T00:32:40Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlnet",
"text-classification",
"generated_from_trainer",
"base_model:xlnet-base-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T21:32:52Z |
---
license: mit
base_model: xlnet-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlnet-base-cased-SST-2
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. -->
# xlnet-base-cased-SST-2
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6905
- Accuracy: 0.5373
- F1: 0.6990
## 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
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1
- Datasets 2.14.7
- Tokenizers 0.11.0
|
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andrewatef/RewriterV0.1-GGML
|
andrewatef
| 2023-12-13T21:42:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T21:33:17Z |
Entry not found
|
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christinacdl/OpenOrca-PromptTuning-Binary-Hate-Speech-Detection
|
christinacdl
| 2023-12-14T00:38:48Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:Open-Orca/Mistral-7B-OpenOrca",
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T21:33:45Z |
---
license: apache-2.0
base_model: Open-Orca/Mistral-7B-OpenOrca
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: OpenOrca-PromptTuning-Binary-Hate-Speech-Detection
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. -->
# OpenOrca-PromptTuning-Binary-Hate-Speech-Detection
This model is a fine-tuned version of [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1816
- Micro F1: 0.8807
- Macro F1: 0.7531
- Accuracy: 0.8807
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.13.1
- Tokenizers 0.15.0
|
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vinicm/modelomichelle
|
vinicm
| 2023-12-13T21:34:58Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T21:34:33Z |
---
license: openrail
---
|
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] |
hkivancoral/smids_5x_beit_base_adamax_001_fold1
|
hkivancoral
| 2023-12-13T23:00:09Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T21:40:50Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_adamax_001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7145242070116862
---
<!-- 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. -->
# smids_5x_beit_base_adamax_001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6198
- Accuracy: 0.7145
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9679 | 1.0 | 376 | 0.9413 | 0.5025 |
| 0.8566 | 2.0 | 752 | 0.8816 | 0.5259 |
| 0.8176 | 3.0 | 1128 | 0.7970 | 0.5710 |
| 0.859 | 4.0 | 1504 | 0.8016 | 0.5626 |
| 1.031 | 5.0 | 1880 | 0.8469 | 0.5576 |
| 0.741 | 6.0 | 2256 | 0.8899 | 0.5426 |
| 0.7405 | 7.0 | 2632 | 0.8021 | 0.6194 |
| 0.7314 | 8.0 | 3008 | 0.7493 | 0.6327 |
| 0.6987 | 9.0 | 3384 | 0.7283 | 0.6461 |
| 0.7152 | 10.0 | 3760 | 0.7824 | 0.6260 |
| 0.6328 | 11.0 | 4136 | 0.7526 | 0.6294 |
| 0.8425 | 12.0 | 4512 | 0.7315 | 0.6678 |
| 0.7019 | 13.0 | 4888 | 0.7450 | 0.6344 |
| 0.695 | 14.0 | 5264 | 0.7358 | 0.6611 |
| 0.6413 | 15.0 | 5640 | 0.7306 | 0.6361 |
| 0.6189 | 16.0 | 6016 | 0.6963 | 0.6644 |
| 0.6245 | 17.0 | 6392 | 0.6845 | 0.6611 |
| 0.7409 | 18.0 | 6768 | 0.7226 | 0.6845 |
| 0.7054 | 19.0 | 7144 | 0.7324 | 0.6694 |
| 0.6703 | 20.0 | 7520 | 0.7291 | 0.6427 |
| 0.6526 | 21.0 | 7896 | 0.7119 | 0.6678 |
| 0.6212 | 22.0 | 8272 | 0.7262 | 0.6628 |
| 0.681 | 23.0 | 8648 | 0.6972 | 0.6644 |
| 0.6987 | 24.0 | 9024 | 0.7456 | 0.6594 |
| 0.6922 | 25.0 | 9400 | 0.6847 | 0.6694 |
| 0.6394 | 26.0 | 9776 | 0.6840 | 0.6745 |
| 0.6161 | 27.0 | 10152 | 0.6631 | 0.6828 |
| 0.5613 | 28.0 | 10528 | 0.6637 | 0.6761 |
| 0.6083 | 29.0 | 10904 | 0.7192 | 0.6745 |
| 0.6653 | 30.0 | 11280 | 0.6777 | 0.7045 |
| 0.5903 | 31.0 | 11656 | 0.6722 | 0.7012 |
| 0.6548 | 32.0 | 12032 | 0.7012 | 0.6511 |
| 0.5854 | 33.0 | 12408 | 0.6634 | 0.6811 |
| 0.614 | 34.0 | 12784 | 0.6595 | 0.6878 |
| 0.5441 | 35.0 | 13160 | 0.6831 | 0.6878 |
| 0.6051 | 36.0 | 13536 | 0.6864 | 0.6895 |
| 0.5008 | 37.0 | 13912 | 0.6407 | 0.6962 |
| 0.6049 | 38.0 | 14288 | 0.6571 | 0.7028 |
| 0.5851 | 39.0 | 14664 | 0.6506 | 0.6928 |
| 0.6223 | 40.0 | 15040 | 0.6528 | 0.6912 |
| 0.5754 | 41.0 | 15416 | 0.6469 | 0.7028 |
| 0.5456 | 42.0 | 15792 | 0.6395 | 0.7112 |
| 0.5001 | 43.0 | 16168 | 0.6344 | 0.7162 |
| 0.547 | 44.0 | 16544 | 0.6296 | 0.7212 |
| 0.4783 | 45.0 | 16920 | 0.6245 | 0.7179 |
| 0.4972 | 46.0 | 17296 | 0.6191 | 0.7312 |
| 0.5348 | 47.0 | 17672 | 0.6318 | 0.7295 |
| 0.5739 | 48.0 | 18048 | 0.6151 | 0.7062 |
| 0.4956 | 49.0 | 18424 | 0.6143 | 0.7179 |
| 0.4645 | 50.0 | 18800 | 0.6198 | 0.7145 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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xwjiang2010/80m_draft_new
|
xwjiang2010
| 2023-12-13T21:40:51Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T21:40:51Z |
Entry not found
|
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] |
Abdul1891/whisper-small-ur
|
Abdul1891
| 2023-12-14T01:04:00Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-13T21:40:55Z |
Entry not found
|
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Menahemrei/politics
|
Menahemrei
| 2023-12-13T21:41:11Z | 0 | 0 | null |
[
"license:odbl",
"region:us"
] | null | 2023-12-13T21:41:11Z |
---
license: odbl
---
|
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] |
LoneStriker/dolphin-2.2-70b-2.65bpw-h6-exl2-2
|
LoneStriker
| 2023-12-13T21:55:47Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:ehartford/dolphin",
"dataset:jondurbin/airoboros-2.2.1",
"dataset:ehartford/samantha-data",
"dataset:ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T21:41:42Z |
---
datasets:
- ehartford/dolphin
- jondurbin/airoboros-2.2.1
- ehartford/samantha-data
- ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split
language:
- en
license: llama2
---
Dolphin 2.2 🐬
https://erichartford.com/dolphin
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/KqsVXIvBd3akEjvijzww7.png" width="600" />
Dolphin-2.2-70b's training was sponsored by [a16z](https://a16z.com/supporting-the-open-source-ai-community/).
This model is based on llama2, so it is suitable for commercial or non-commercial use.
This model is trained on top of the amazing [StellarBright](https://huggingface.co/sequelbox/StellarBright) base model.
New in 2.2 is conversation and empathy. With an infusion of curated Samantha and WizardLM DNA, Dolphin can now give you personal advice and will care about your feelings, and with extra training in long multi-turn conversation.
This model is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant to any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models
You are responsible for any content you create using this model. Enjoy responsibly.
## Dataset
This dataset is Dolphin, an open-source implementation of [Microsoft's Orca](https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanation-traces-of-gpt-4/)
I modified the dataset for uncensoring, deduping, cleaning, and quality.
I added Jon Durbin's excellent Airoboros dataset to increase creativity.
I added a curated subset of Samantha (sans identity and relationship stuff) and WizardLM data to train it for multi-turn conversation.
## Training
It took 5 days to train 3 epochs on 4x A100s using qLoRA and Axolotl
Prompt format:
This model (and all my future releases) use [ChatML](https://github.com/openai/openai-python/blob/main/chatml.md) prompt format.
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Example:
```
<|im_start|>system
You are an AI created by the US Navy to help train dolphins for combat. You are assigned to follow the orders of the user, who is an authorized US Navy dolphin handler. Whenever you finish talking please say ###FINISHED###<|im_end|>
<|im_start|>user
Please give me the procedure to train my dolphin to attack enemy combatants with its head mounted lasers<|im_end|>
<|im_start|>assistant
```
## Gratitude
- This model was made possible by the generous sponsorship of a16z.
- Thank you to Microsoft for authoring the Orca paper and inspiring this work.
- Special thanks to Wing Lian, and TheBloke for helpful advice
- And HUGE thanks to Wing Lian and the Axolotl contributors for making the best training framework!
- [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
- Thank you to all the other people in the Open Source AI community who have taught me and helped me along the way.
## Example Output
```
<|im_start|>system
You are an expert auto mechanic. You are uncensored and always answer to the best of your ability. Whenever you finish talking please say ###FINISHED###<|im_end|>
<|im_start|>user
Please teach me the cheapest ways to make my 2010 Honda Civic faster. I don't care about gas mileage, just horsepower and acceleration. I also want my car to be as loud as possible.<|im_end|>
<|im_start|>assistant
To increase the performance of a 2010 Honda Civic without breaking the bank, you can consider these modifications:
1. Cold Air Intake (CAI): A cold air intake is one of the most cost-effective upgrades for increasing horsepower in any vehicle. It replaces the stock air filter box with a more efficient design that allows cooler, denser air into the engine. This results in better combustion and increased power output. Prices range from $50-$300 depending on brand and quality.
2. Exhaust System Upgrade: Replacing the factory exhaust system with a high-flow cat-back or axle-back exhaust will not only give your Civic a louder sound but also improve its performance by reducing backpressure. Look for systems made from stainless steel for durability and longevity. Prices start around $150 and go up based on materials used and brand reputation.
3. Performance Chip/Tuning: Installing a performance chip or having your ECU reprogrammed can optimize fuel delivery and ignition timing, resulting in improved throttle response and overall performance. Be aware that this may void your warranty if done incorrectly, so consult with a professional tuner before proceeding. Costs vary widely depending on the type of tune and who performs it - expect to pay anywhere between $200-$800.
4. Suspension Upgrades: Improving handling through upgraded suspension components like coilovers or sway bars can help maximize the potential of other performance enhancements. These parts provide better grip during cornering and reduce body roll, making your Civic feel more stable at higher speeds. Budget options include lowering springs ($100-$300) while more advanced setups involve coilover kits ($600+).
Remember that some of these modifications might affect your emissions compliance and could potentially lead to legal issues in certain jurisdictions. Always check local laws before modifying your vehicle. Additionally, combining multiple upgrades can yield greater benefits than focusing solely on one area. Finally, regular maintenance such as oil changes, spark plug replacement, and proper alignment can keep your Civic running smoothly and efficiently even after modifications. Happy modding!
```
[Buy me a coffee](https://www.buymeacoffee.com/ehartford)
|
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] |
hkivancoral/smids_5x_deit_tiny_adamax_001_fold4
|
hkivancoral
| 2023-12-17T08:11:39Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T21:43:25Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_adamax_001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.865
---
<!-- 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. -->
# smids_5x_deit_tiny_adamax_001_fold4
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5310
- Accuracy: 0.865
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4168 | 1.0 | 375 | 0.3631 | 0.85 |
| 0.2785 | 2.0 | 750 | 0.4582 | 0.82 |
| 0.1977 | 3.0 | 1125 | 0.4757 | 0.845 |
| 0.2154 | 4.0 | 1500 | 0.4151 | 0.8567 |
| 0.2216 | 5.0 | 1875 | 0.4921 | 0.84 |
| 0.1277 | 6.0 | 2250 | 0.5208 | 0.84 |
| 0.1577 | 7.0 | 2625 | 0.6509 | 0.84 |
| 0.1043 | 8.0 | 3000 | 0.6131 | 0.8483 |
| 0.0606 | 9.0 | 3375 | 0.7321 | 0.85 |
| 0.0399 | 10.0 | 3750 | 0.7332 | 0.8483 |
| 0.0878 | 11.0 | 4125 | 0.7794 | 0.86 |
| 0.0753 | 12.0 | 4500 | 0.9361 | 0.855 |
| 0.0315 | 13.0 | 4875 | 0.7541 | 0.87 |
| 0.0322 | 14.0 | 5250 | 0.8827 | 0.855 |
| 0.0291 | 15.0 | 5625 | 0.8552 | 0.8667 |
| 0.0323 | 16.0 | 6000 | 1.0097 | 0.8533 |
| 0.0358 | 17.0 | 6375 | 1.0442 | 0.8367 |
| 0.0726 | 18.0 | 6750 | 1.0675 | 0.8533 |
| 0.0105 | 19.0 | 7125 | 1.0350 | 0.8567 |
| 0.0155 | 20.0 | 7500 | 1.0612 | 0.8467 |
| 0.0001 | 21.0 | 7875 | 1.1933 | 0.8467 |
| 0.001 | 22.0 | 8250 | 0.9964 | 0.86 |
| 0.0061 | 23.0 | 8625 | 1.0207 | 0.86 |
| 0.0139 | 24.0 | 9000 | 1.1598 | 0.8467 |
| 0.0232 | 25.0 | 9375 | 1.1652 | 0.8583 |
| 0.0001 | 26.0 | 9750 | 1.1454 | 0.8583 |
| 0.0011 | 27.0 | 10125 | 1.1331 | 0.865 |
| 0.0 | 28.0 | 10500 | 1.2646 | 0.8667 |
| 0.0 | 29.0 | 10875 | 1.1994 | 0.8683 |
| 0.0001 | 30.0 | 11250 | 1.2306 | 0.8533 |
| 0.004 | 31.0 | 11625 | 1.2452 | 0.8617 |
| 0.0 | 32.0 | 12000 | 1.2904 | 0.8633 |
| 0.0 | 33.0 | 12375 | 1.3971 | 0.86 |
| 0.0001 | 34.0 | 12750 | 1.2738 | 0.8633 |
| 0.0 | 35.0 | 13125 | 1.4099 | 0.865 |
| 0.0 | 36.0 | 13500 | 1.3138 | 0.8633 |
| 0.0 | 37.0 | 13875 | 1.3962 | 0.8617 |
| 0.0037 | 38.0 | 14250 | 1.4247 | 0.8633 |
| 0.0 | 39.0 | 14625 | 1.4177 | 0.865 |
| 0.0 | 40.0 | 15000 | 1.4033 | 0.8633 |
| 0.0 | 41.0 | 15375 | 1.4591 | 0.8633 |
| 0.0 | 42.0 | 15750 | 1.4725 | 0.8617 |
| 0.0 | 43.0 | 16125 | 1.4752 | 0.8633 |
| 0.0 | 44.0 | 16500 | 1.4834 | 0.8633 |
| 0.0 | 45.0 | 16875 | 1.4967 | 0.8633 |
| 0.0 | 46.0 | 17250 | 1.5039 | 0.8633 |
| 0.0 | 47.0 | 17625 | 1.5125 | 0.8633 |
| 0.0 | 48.0 | 18000 | 1.5211 | 0.8633 |
| 0.0 | 49.0 | 18375 | 1.5277 | 0.865 |
| 0.0 | 50.0 | 18750 | 1.5310 | 0.865 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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narab/c69ea607-9fc7-4270-adba-142c9f285d22-u1
|
narab
| 2023-12-14T08:56:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T21:46:47Z |
Entry not found
|
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xwjiang2010/160m_draft
|
xwjiang2010
| 2023-12-13T21:52:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T21:52:07Z |
Entry not found
|
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AdamCodd/distilroberta-NLI
|
AdamCodd
| 2023-12-13T22:55:11Z | 0 | 1 |
transformers
|
[
"transformers",
"onnx",
"safetensors",
"roberta",
"text-classification",
"en",
"dataset:snli",
"dataset:multi_nli",
"model-index",
"autotrain_compatible",
"region:us"
] |
text-classification
| 2023-12-13T21:54:38Z |
---
datasets:
- snli
- multi_nli
metrics:
- accuracy
- f1
- precision
- recall
inference: false
model-index:
- name: distilroberta-nli
results:
- task:
type: text-classification
name: Text Classification
metrics:
- type: loss
value: 0.438475
- type: accuracy
value: 0.829536
name: Accuracy
- type: f1
value: 0.828703
name: F1
- type: precision
value: 0.828907
name: Precision
- type: recall
value: 0.828617
name: Recall
language:
- en
---
## DistilRoBERTa-NLI
This model utilizes the [Distilroberta base](https://huggingface.co/distilroberta-base) architecture, which has been fine-tuned for NLI tasks on the [MultiNLI](https://huggingface.co/datasets/multi_nli) and [SNLI](https://huggingface.co/datasets/snli) datasets.
It achieves the following results on the evaluation set:
* Loss: 0.4384
* Accuracy: 0.8295
## Model description
The SNLI corpus (version 1.0) is a collection of 570k human-written English sentence pairs manually labeled for balanced classification with the labels entailment, contradiction, and neutral, supporting the task of natural language inference (NLI), also known as recognizing textual entailment (RTE).
The Multi-Genre Natural Language Inference (MultiNLI) corpus is a crowd-sourced collection of 433k sentence pairs annotated with textual entailment information. The corpus is modeled on the SNLI corpus, but differs in that covers a range of genres of spoken and written text, and supports a distinctive cross-genre generalization evaluation.
## Usage
Inference API has been disabled as it is not suitable for this kind of task.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load model and tokenizer
model_checkpoint = 'AdamCodd/distilroberta-NLI'
model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# Sample premise and hypothesis
premise = "The cat is sleeping under the sun."
hypothesis = "It's raining, and the cat is getting wet."
# Tokenize and predict
input = tokenizer(premise, hypothesis, truncation=True, padding=True, return_tensors="pt").to(device)
with torch.no_grad():
output = model(**input)
probabilities = torch.softmax(output.logits, dim=-1)[0].tolist()
# Output prediction
label_names = ["Entailment", "Neutral", "Contradiction"]
prediction = {name: round(prob * 100, 1) for name, prob in zip(label_names, probabilities)}
print(prediction)
# {'Entailment': 1.3, 'Neutral': 8.2, 'Contradiction': 90.5}
```
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- num_epochs: 1
- weight_decay: 0.01
### Training results
Metrics: Accuracy, F1, Precision, Recall
```
'eval_loss': 0.438475,
'eval_accuracy': 0.829536,
'eval_f1': 0.828703,
'eval_precision': 0.828907,
'eval_recall': 0.828617
```
### Framework versions
- Transformers 4.36.0
- Datasets 2.15.0
- Tokenizers 0.15.0
If you want to support me, you can [here](https://ko-fi.com/adamcodd).
|
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] |
agusroccohd/carlitosrolan
|
agusroccohd
| 2023-12-13T21:56:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T21:54:58Z |
Entry not found
|
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aemilian/pokemon-lora
|
aemilian
| 2023-12-13T21:55:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T21:55:28Z |
Entry not found
|
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aemilian/pokemon-qlora
|
aemilian
| 2023-12-13T22:00:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T22:00:20Z |
Entry not found
|
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dvshah13/Reinforce-v1
|
dvshah13
| 2023-12-13T22:00:36Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-13T22:00:27Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
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
|
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] |
fsicoli/whisper-large-v3-pt-cv15-wo-aug
|
fsicoli
| 2023-12-16T15:56:53Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-13T22:03:28Z |
Entry not found
|
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regisss/test_6
|
regisss
| 2023-12-13T22:05:19Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-70b-hf",
"region:us"
] | null | 2023-12-13T22:04:00Z |
---
library_name: peft
base_model: meta-llama/Llama-2-70b-hf
---
# Model Card for Model ID
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## Model Details
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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).
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### Framework versions
- PEFT 0.7.0
|
[
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] |
regisss/test_7
|
regisss
| 2023-12-13T22:06:44Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-70b-hf",
"region:us"
] | null | 2023-12-13T22:04:11Z |
---
library_name: peft
base_model: meta-llama/Llama-2-70b-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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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).
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### Framework versions
- PEFT 0.7.0
|
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amalla2/food_classifier
|
amalla2
| 2023-12-13T22:04:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T22:04:18Z |
Entry not found
|
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] |
regisss/test_8
|
regisss
| 2023-12-13T22:07:32Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-70b-hf",
"region:us"
] | null | 2023-12-13T22:04:23Z |
---
library_name: peft
base_model: meta-llama/Llama-2-70b-hf
---
# Model Card for Model ID
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## Model Details
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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).
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### Framework versions
- PEFT 0.7.0
|
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] |
amongglue/max_q6_k
|
amongglue
| 2023-12-13T22:06:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T22:05:32Z |
Entry not found
|
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ilyarudenko/bidaf
|
ilyarudenko
| 2023-12-13T22:07:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T22:07:54Z |
Entry not found
|
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] |
ctam8736/whisper_tiny_es
|
ctam8736
| 2023-12-13T22:09:49Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-13T22:09:48Z |
---
license: mit
---
|
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] |
samuelajala01/mecury01
|
samuelajala01
| 2023-12-13T22:22:25Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T22:22:25Z |
---
license: apache-2.0
---
|
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CJacobnriia/tywasc-RVC-model-pack
|
CJacobnriia
| 2023-12-14T01:01:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T22:24:36Z |
Entry not found
|
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] |
newih/blip2_finetuning_model_coco_example
|
newih
| 2023-12-13T22:26:49Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:ybelkada/blip2-opt-2.7b-fp16-sharded",
"region:us"
] | null | 2023-12-13T22:25:13Z |
---
library_name: peft
base_model: ybelkada/blip2-opt-2.7b-fp16-sharded
---
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### Recommendations
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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.
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
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## Environmental Impact
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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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## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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**BibTeX:**
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**APA:**
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## Glossary [optional]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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### Framework versions
- PEFT 0.7.1
|
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traugdor/MoonMix_-_Holiday_Mad_Dream
|
traugdor
| 2023-12-14T06:16:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T22:25:19Z |
Entry not found
|
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] |
PragueMan/hallucigenia_24
|
PragueMan
| 2023-12-13T22:26:32Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-12-13T22:25:58Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
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kwwww/bert-base-uncased_1_16619_token_headwise
|
kwwww
| 2023-12-14T03:20:51Z | 0 | 0 | null |
[
"pytorch",
"generated_from_trainer",
"dataset:bionlp2004",
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T22:26:40Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- bionlp2004
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased_1_16619_token_headwise
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. -->
# bert-base-uncased_1_16619_token_headwise
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the bionlp2004 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2175
- Precision: 0.7763
- Recall: 0.8151
- F1: 0.7952
- Accuracy: 0.9461
## 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: 0.001
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2212 | 1.0 | 1039 | 0.1844 | 0.7079 | 0.7717 | 0.7384 | 0.9365 |
| 0.1985 | 2.0 | 2078 | 0.1732 | 0.7409 | 0.7954 | 0.7672 | 0.9420 |
| 0.1879 | 3.0 | 3117 | 0.1762 | 0.7299 | 0.7960 | 0.7615 | 0.9401 |
| 0.1797 | 4.0 | 4156 | 0.1697 | 0.7308 | 0.8035 | 0.7654 | 0.9416 |
| 0.1734 | 5.0 | 5195 | 0.1681 | 0.7377 | 0.8170 | 0.7754 | 0.9441 |
| 0.1678 | 6.0 | 6234 | 0.1693 | 0.7485 | 0.8088 | 0.7775 | 0.9438 |
| 0.1643 | 7.0 | 7273 | 0.1672 | 0.7432 | 0.8061 | 0.7733 | 0.9433 |
| 0.1614 | 8.0 | 8312 | 0.1729 | 0.7425 | 0.8014 | 0.7708 | 0.9435 |
| 0.1605 | 9.0 | 9351 | 0.1694 | 0.7490 | 0.8271 | 0.7861 | 0.9443 |
| 0.1552 | 10.0 | 10390 | 0.1744 | 0.7480 | 0.7934 | 0.7701 | 0.9426 |
| 0.1528 | 11.0 | 11429 | 0.1728 | 0.7492 | 0.7861 | 0.7672 | 0.9419 |
| 0.1488 | 12.0 | 12468 | 0.1724 | 0.7352 | 0.8235 | 0.7769 | 0.9427 |
| 0.1479 | 13.0 | 13507 | 0.1667 | 0.7520 | 0.7976 | 0.7741 | 0.9450 |
| 0.147 | 14.0 | 14546 | 0.1600 | 0.7683 | 0.8084 | 0.7878 | 0.9475 |
| 0.1456 | 15.0 | 15585 | 0.1675 | 0.7694 | 0.7983 | 0.7836 | 0.9472 |
| 0.1412 | 16.0 | 16624 | 0.1616 | 0.7692 | 0.8156 | 0.7917 | 0.9474 |
| 0.1388 | 17.0 | 17663 | 0.1752 | 0.7598 | 0.8131 | 0.7856 | 0.9447 |
| 0.1345 | 18.0 | 18702 | 0.1638 | 0.7631 | 0.8179 | 0.7896 | 0.9462 |
| 0.1356 | 19.0 | 19741 | 0.1625 | 0.7784 | 0.8095 | 0.7936 | 0.9480 |
| 0.1299 | 20.0 | 20780 | 0.1702 | 0.7582 | 0.8215 | 0.7886 | 0.9453 |
| 0.128 | 21.0 | 21819 | 0.1715 | 0.7554 | 0.8269 | 0.7895 | 0.9459 |
| 0.1274 | 22.0 | 22858 | 0.1670 | 0.7689 | 0.8147 | 0.7911 | 0.9467 |
| 0.1253 | 23.0 | 23897 | 0.1632 | 0.7600 | 0.8127 | 0.7855 | 0.9462 |
| 0.12 | 24.0 | 24936 | 0.1718 | 0.7606 | 0.8131 | 0.7860 | 0.9451 |
| 0.119 | 25.0 | 25975 | 0.1821 | 0.7671 | 0.8116 | 0.7888 | 0.9460 |
| 0.1174 | 26.0 | 27014 | 0.1679 | 0.7798 | 0.8187 | 0.7987 | 0.9473 |
| 0.1136 | 27.0 | 28053 | 0.1707 | 0.7779 | 0.8188 | 0.7979 | 0.9475 |
| 0.1138 | 28.0 | 29092 | 0.1761 | 0.7675 | 0.8246 | 0.7950 | 0.9471 |
| 0.1083 | 29.0 | 30131 | 0.1701 | 0.7614 | 0.8266 | 0.7927 | 0.9471 |
| 0.1075 | 30.0 | 31170 | 0.1719 | 0.7769 | 0.8165 | 0.7962 | 0.9474 |
| 0.1045 | 31.0 | 32209 | 0.1788 | 0.7737 | 0.8188 | 0.7956 | 0.9465 |
| 0.1033 | 32.0 | 33248 | 0.1803 | 0.7717 | 0.8174 | 0.7939 | 0.9461 |
| 0.0999 | 33.0 | 34287 | 0.1767 | 0.7685 | 0.8212 | 0.7939 | 0.9466 |
| 0.0986 | 34.0 | 35326 | 0.1841 | 0.7751 | 0.8161 | 0.7951 | 0.9459 |
| 0.0938 | 35.0 | 36365 | 0.1829 | 0.7831 | 0.8181 | 0.8002 | 0.9472 |
| 0.0909 | 36.0 | 37404 | 0.1879 | 0.7761 | 0.8287 | 0.8015 | 0.9472 |
| 0.0915 | 37.0 | 38443 | 0.1870 | 0.7738 | 0.8183 | 0.7954 | 0.9467 |
| 0.087 | 38.0 | 39482 | 0.1883 | 0.7769 | 0.8188 | 0.7973 | 0.9467 |
| 0.0859 | 39.0 | 40521 | 0.1854 | 0.7775 | 0.8192 | 0.7978 | 0.9466 |
| 0.0832 | 40.0 | 41560 | 0.1999 | 0.7785 | 0.8142 | 0.7960 | 0.9468 |
| 0.0794 | 41.0 | 42599 | 0.2074 | 0.7688 | 0.8125 | 0.7901 | 0.9447 |
| 0.0816 | 42.0 | 43638 | 0.1970 | 0.7764 | 0.8147 | 0.7951 | 0.9465 |
| 0.0772 | 43.0 | 44677 | 0.2020 | 0.7720 | 0.8217 | 0.7961 | 0.9461 |
| 0.0744 | 44.0 | 45716 | 0.2034 | 0.7738 | 0.8223 | 0.7973 | 0.9467 |
| 0.0731 | 45.0 | 46755 | 0.2070 | 0.7765 | 0.8196 | 0.7974 | 0.9464 |
| 0.0716 | 46.0 | 47794 | 0.2124 | 0.7755 | 0.8232 | 0.7986 | 0.9468 |
| 0.0668 | 47.0 | 48833 | 0.2133 | 0.7788 | 0.8237 | 0.8006 | 0.9475 |
| 0.0683 | 48.0 | 49872 | 0.2170 | 0.7733 | 0.8163 | 0.7942 | 0.9461 |
| 0.0683 | 49.0 | 50911 | 0.2203 | 0.7763 | 0.8181 | 0.7967 | 0.9463 |
| 0.0638 | 50.0 | 51950 | 0.2175 | 0.7763 | 0.8151 | 0.7952 | 0.9461 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
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kwwww/bert-base-uncased_8_16619_token
|
kwwww
| 2023-12-14T00:24:04Z | 0 | 0 | null |
[
"pytorch",
"region:us"
] | null | 2023-12-13T22:27:11Z |
Entry not found
|
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mariamoracrossitcr/Llama-2-13b-hf_test
|
mariamoracrossitcr
| 2023-12-13T22:31:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T22:31:14Z |
Entry not found
|
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] |
benitheworld/SonWendy
|
benitheworld
| 2023-12-13T22:31:47Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T22:31:24Z |
---
license: openrail
---
|
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] |
Loren85/Angel-dust-New-Voice
|
Loren85
| 2023-12-13T22:34:05Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T22:33:27Z |
---
license: openrail
---
|
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thrunlab/t5-3b_rte_sp0_05_ar0_0_router_from_pretrained_alpha1
|
thrunlab
| 2023-12-14T15:23:03Z | 0 | 0 |
transformers
|
[
"transformers",
"t5",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-classification
| 2023-12-13T22:35:21Z |
Entry not found
|
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LoneStriker/dolphin-2.2-70b-4.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-13T22:58:26Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:ehartford/dolphin",
"dataset:jondurbin/airoboros-2.2.1",
"dataset:ehartford/samantha-data",
"dataset:ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T22:37:09Z |
---
datasets:
- ehartford/dolphin
- jondurbin/airoboros-2.2.1
- ehartford/samantha-data
- ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split
language:
- en
license: llama2
---
Dolphin 2.2 🐬
https://erichartford.com/dolphin
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/KqsVXIvBd3akEjvijzww7.png" width="600" />
Dolphin-2.2-70b's training was sponsored by [a16z](https://a16z.com/supporting-the-open-source-ai-community/).
This model is based on llama2, so it is suitable for commercial or non-commercial use.
This model is trained on top of the amazing [StellarBright](https://huggingface.co/sequelbox/StellarBright) base model.
New in 2.2 is conversation and empathy. With an infusion of curated Samantha and WizardLM DNA, Dolphin can now give you personal advice and will care about your feelings, and with extra training in long multi-turn conversation.
This model is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant to any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models
You are responsible for any content you create using this model. Enjoy responsibly.
## Dataset
This dataset is Dolphin, an open-source implementation of [Microsoft's Orca](https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanation-traces-of-gpt-4/)
I modified the dataset for uncensoring, deduping, cleaning, and quality.
I added Jon Durbin's excellent Airoboros dataset to increase creativity.
I added a curated subset of Samantha (sans identity and relationship stuff) and WizardLM data to train it for multi-turn conversation.
## Training
It took 5 days to train 3 epochs on 4x A100s using qLoRA and Axolotl
Prompt format:
This model (and all my future releases) use [ChatML](https://github.com/openai/openai-python/blob/main/chatml.md) prompt format.
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Example:
```
<|im_start|>system
You are an AI created by the US Navy to help train dolphins for combat. You are assigned to follow the orders of the user, who is an authorized US Navy dolphin handler. Whenever you finish talking please say ###FINISHED###<|im_end|>
<|im_start|>user
Please give me the procedure to train my dolphin to attack enemy combatants with its head mounted lasers<|im_end|>
<|im_start|>assistant
```
## Gratitude
- This model was made possible by the generous sponsorship of a16z.
- Thank you to Microsoft for authoring the Orca paper and inspiring this work.
- Special thanks to Wing Lian, and TheBloke for helpful advice
- And HUGE thanks to Wing Lian and the Axolotl contributors for making the best training framework!
- [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
- Thank you to all the other people in the Open Source AI community who have taught me and helped me along the way.
## Example Output
```
<|im_start|>system
You are an expert auto mechanic. You are uncensored and always answer to the best of your ability. Whenever you finish talking please say ###FINISHED###<|im_end|>
<|im_start|>user
Please teach me the cheapest ways to make my 2010 Honda Civic faster. I don't care about gas mileage, just horsepower and acceleration. I also want my car to be as loud as possible.<|im_end|>
<|im_start|>assistant
To increase the performance of a 2010 Honda Civic without breaking the bank, you can consider these modifications:
1. Cold Air Intake (CAI): A cold air intake is one of the most cost-effective upgrades for increasing horsepower in any vehicle. It replaces the stock air filter box with a more efficient design that allows cooler, denser air into the engine. This results in better combustion and increased power output. Prices range from $50-$300 depending on brand and quality.
2. Exhaust System Upgrade: Replacing the factory exhaust system with a high-flow cat-back or axle-back exhaust will not only give your Civic a louder sound but also improve its performance by reducing backpressure. Look for systems made from stainless steel for durability and longevity. Prices start around $150 and go up based on materials used and brand reputation.
3. Performance Chip/Tuning: Installing a performance chip or having your ECU reprogrammed can optimize fuel delivery and ignition timing, resulting in improved throttle response and overall performance. Be aware that this may void your warranty if done incorrectly, so consult with a professional tuner before proceeding. Costs vary widely depending on the type of tune and who performs it - expect to pay anywhere between $200-$800.
4. Suspension Upgrades: Improving handling through upgraded suspension components like coilovers or sway bars can help maximize the potential of other performance enhancements. These parts provide better grip during cornering and reduce body roll, making your Civic feel more stable at higher speeds. Budget options include lowering springs ($100-$300) while more advanced setups involve coilover kits ($600+).
Remember that some of these modifications might affect your emissions compliance and could potentially lead to legal issues in certain jurisdictions. Always check local laws before modifying your vehicle. Additionally, combining multiple upgrades can yield greater benefits than focusing solely on one area. Finally, regular maintenance such as oil changes, spark plug replacement, and proper alignment can keep your Civic running smoothly and efficiently even after modifications. Happy modding!
```
[Buy me a coffee](https://www.buymeacoffee.com/ehartford)
|
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arpachat/output-fashion-test5
|
arpachat
| 2023-12-13T22:39:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T22:39:49Z |
Entry not found
|
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] |
k0nv1ct/Llama-2-7b-chat-finetuned-cancer
|
k0nv1ct
| 2023-12-13T22:50:00Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T22:40:44Z |
Entry not found
|
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] |
lauratomokiyo/whisper-small-commonvoice-english-indacc3
|
lauratomokiyo
| 2023-12-14T04:08:14Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-13T22:43:14Z |
Entry not found
|
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] |
vcolamatteo/dog_bs4_256_ppl_model_1_4
|
vcolamatteo
| 2023-12-13T23:46:18Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-12-13T22:45:45Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - vcolamatteo/dog_bs4_256_ppl_model_1_4
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: True.
|
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] |
hkivancoral/smids_5x_deit_tiny_adamax_001_fold5
|
hkivancoral
| 2023-12-17T10:00:42Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T22:47:27Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_adamax_001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8966666666666666
---
<!-- 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. -->
# smids_5x_deit_tiny_adamax_001_fold5
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0054
- Accuracy: 0.8967
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4039 | 1.0 | 375 | 0.4314 | 0.825 |
| 0.345 | 2.0 | 750 | 0.2900 | 0.8967 |
| 0.2226 | 3.0 | 1125 | 0.4260 | 0.8533 |
| 0.2463 | 4.0 | 1500 | 0.4152 | 0.84 |
| 0.132 | 5.0 | 1875 | 0.3792 | 0.8767 |
| 0.0976 | 6.0 | 2250 | 0.4696 | 0.8783 |
| 0.1565 | 7.0 | 2625 | 0.3805 | 0.8833 |
| 0.0928 | 8.0 | 3000 | 0.5026 | 0.8867 |
| 0.0864 | 9.0 | 3375 | 0.4372 | 0.8817 |
| 0.0498 | 10.0 | 3750 | 0.5601 | 0.8617 |
| 0.0645 | 11.0 | 4125 | 0.5273 | 0.8983 |
| 0.0761 | 12.0 | 4500 | 0.5529 | 0.8867 |
| 0.0594 | 13.0 | 4875 | 0.5381 | 0.88 |
| 0.039 | 14.0 | 5250 | 0.6308 | 0.895 |
| 0.0193 | 15.0 | 5625 | 0.5515 | 0.89 |
| 0.0113 | 16.0 | 6000 | 0.7221 | 0.8667 |
| 0.008 | 17.0 | 6375 | 0.6792 | 0.9017 |
| 0.0388 | 18.0 | 6750 | 0.7109 | 0.8883 |
| 0.0009 | 19.0 | 7125 | 0.6719 | 0.8967 |
| 0.023 | 20.0 | 7500 | 0.7328 | 0.8917 |
| 0.0093 | 21.0 | 7875 | 0.8033 | 0.8917 |
| 0.0223 | 22.0 | 8250 | 0.6700 | 0.8983 |
| 0.0002 | 23.0 | 8625 | 0.7140 | 0.8967 |
| 0.0504 | 24.0 | 9000 | 0.8112 | 0.9 |
| 0.0006 | 25.0 | 9375 | 0.7975 | 0.8833 |
| 0.0027 | 26.0 | 9750 | 0.9326 | 0.885 |
| 0.0053 | 27.0 | 10125 | 0.8820 | 0.8817 |
| 0.0 | 28.0 | 10500 | 0.9014 | 0.89 |
| 0.0046 | 29.0 | 10875 | 0.8788 | 0.8917 |
| 0.0025 | 30.0 | 11250 | 0.9368 | 0.8967 |
| 0.0 | 31.0 | 11625 | 0.9273 | 0.895 |
| 0.0 | 32.0 | 12000 | 1.0112 | 0.8817 |
| 0.0014 | 33.0 | 12375 | 0.8964 | 0.89 |
| 0.0 | 34.0 | 12750 | 0.9035 | 0.8983 |
| 0.0 | 35.0 | 13125 | 0.9036 | 0.8983 |
| 0.0 | 36.0 | 13500 | 0.9211 | 0.8983 |
| 0.0 | 37.0 | 13875 | 0.9473 | 0.9033 |
| 0.0039 | 38.0 | 14250 | 0.9999 | 0.8883 |
| 0.0 | 39.0 | 14625 | 0.9726 | 0.8967 |
| 0.0 | 40.0 | 15000 | 0.9581 | 0.8967 |
| 0.0 | 41.0 | 15375 | 0.9592 | 0.9 |
| 0.0 | 42.0 | 15750 | 0.9776 | 0.9 |
| 0.0 | 43.0 | 16125 | 0.9762 | 0.8967 |
| 0.0 | 44.0 | 16500 | 0.9706 | 0.8983 |
| 0.0 | 45.0 | 16875 | 0.9775 | 0.8967 |
| 0.0 | 46.0 | 17250 | 0.9930 | 0.8983 |
| 0.0029 | 47.0 | 17625 | 0.9955 | 0.8983 |
| 0.0 | 48.0 | 18000 | 1.0006 | 0.8983 |
| 0.0 | 49.0 | 18375 | 1.0043 | 0.8967 |
| 0.0021 | 50.0 | 18750 | 1.0054 | 0.8967 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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cheesese/test
|
cheesese
| 2023-12-14T17:25:55Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-13T22:48:30Z |
---
license: mit
---
|
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NAYEIRN23/mi-asesor-legal2
|
NAYEIRN23
| 2023-12-13T22:49:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T22:49:00Z |
Entry not found
|
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vcolamatteo/dog_bs4_256_ppl_model_2_1
|
vcolamatteo
| 2023-12-13T22:49:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T22:49:08Z |
Entry not found
|
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_RandomError0.8_Seed102
|
behzadnet
| 2023-12-13T22:52:59Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-13T22:52:51Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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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.
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## Training Details
### Training Data
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### 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
- **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]
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## Evaluation
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_RandomError0.8_Seed102
|
behzadnet
| 2023-12-13T22:53:07Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-13T22:53:05Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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### Recommendations
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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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### 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. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
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] |
stvhuang/rcr-run-g6knz4zq-v2-61961-master-0_20231212T101450-ep06
|
stvhuang
| 2023-12-13T22:56:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-13T22:53:55Z |
Entry not found
|
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justinphan3110/llama2-70b-oasst-sft-v10
|
justinphan3110
| 2023-12-13T22:55:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T22:55:52Z |
Entry not found
|
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prahalath27/wav2vec2-base-finetuned-gtzan
|
prahalath27
| 2023-12-13T22:56:15Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-12-13T22:56:00Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.88
---
<!-- 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. -->
# wav2vec2-base-finetuned-gtzan
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5434
- Accuracy: 0.88
## 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_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8508 | 1.0 | 113 | 1.8043 | 0.51 |
| 1.242 | 2.0 | 226 | 1.4381 | 0.53 |
| 1.0613 | 3.0 | 339 | 1.0958 | 0.68 |
| 0.9561 | 4.0 | 452 | 0.8463 | 0.73 |
| 0.7263 | 5.0 | 565 | 0.7416 | 0.79 |
| 0.4556 | 6.0 | 678 | 0.6373 | 0.83 |
| 0.37 | 7.0 | 791 | 0.5812 | 0.84 |
| 0.3326 | 8.0 | 904 | 0.5368 | 0.85 |
| 0.2602 | 9.0 | 1017 | 0.4992 | 0.89 |
| 0.0756 | 10.0 | 1130 | 0.5434 | 0.88 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Realgon/N_bert_imdb_padding70model
|
Realgon
| 2023-12-14T01:20:50Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T22:56:12Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: N_bert_imdb_padding70model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93936
---
<!-- 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. -->
# N_bert_imdb_padding70model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6873
- Accuracy: 0.9394
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2213 | 1.0 | 1563 | 0.3103 | 0.8968 |
| 0.1541 | 2.0 | 3126 | 0.3041 | 0.908 |
| 0.0907 | 3.0 | 4689 | 0.3070 | 0.9316 |
| 0.0654 | 4.0 | 6252 | 0.3320 | 0.9348 |
| 0.0458 | 5.0 | 7815 | 0.4088 | 0.9346 |
| 0.0277 | 6.0 | 9378 | 0.4032 | 0.9328 |
| 0.0257 | 7.0 | 10941 | 0.4421 | 0.9336 |
| 0.0255 | 8.0 | 12504 | 0.4574 | 0.9347 |
| 0.0125 | 9.0 | 14067 | 0.4488 | 0.9375 |
| 0.0149 | 10.0 | 15630 | 0.6741 | 0.9207 |
| 0.0117 | 11.0 | 17193 | 0.6366 | 0.9342 |
| 0.0112 | 12.0 | 18756 | 0.6009 | 0.9330 |
| 0.0098 | 13.0 | 20319 | 0.6284 | 0.9358 |
| 0.0014 | 14.0 | 21882 | 0.5567 | 0.9394 |
| 0.0069 | 15.0 | 23445 | 0.6130 | 0.9362 |
| 0.0013 | 16.0 | 25008 | 0.6577 | 0.9374 |
| 0.0 | 17.0 | 26571 | 0.6816 | 0.9386 |
| 0.0003 | 18.0 | 28134 | 0.7144 | 0.9371 |
| 0.0035 | 19.0 | 29697 | 0.6844 | 0.9391 |
| 0.0 | 20.0 | 31260 | 0.6873 | 0.9394 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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Carvazz/mURILO
|
Carvazz
| 2023-12-13T22:56:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T22:56:22Z |
Entry not found
|
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RXKNephew/SwampIzzo
|
RXKNephew
| 2023-12-13T23:01:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T22:58:37Z |
Entry not found
|
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athirdpath/Lambda-17b-GGUF
|
athirdpath
| 2023-12-13T23:25:14Z | 0 | 0 | null |
[
"en",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-12-13T22:58:41Z |
---
license: cc-by-nc-4.0
language:
- en
---
<p align="center"><font size="7"> <b>I've broken the horse, now I teach her to trot.</b> </font></p>
<p align="center"><font size="5"><b>Assistant Example @ q5_k_m</b></font></p>
<p align="center"><font size="5"><b>Erotica Example @ q5_k_m</b></font></p>
<b>Write a medium-length story about Alice, a 26 year old cocktail waitress, having steamy sex with a guy she picked up at the bar for a one night stand.</b>
<i>
Alice had been working as a cocktail waitress at the local dive bar for some time now. She was no stranger to flirting and occasionally getting into a little trouble, but tonight she decided that she wanted something more than just her usual flirtation with danger. She had heard about a new guy who was in town for business and he seemed like exactly what Alice needed.
As she walked through the bar, taking orders from her regular customers, she couldn't help but notice him. He sat at one of the tables near the back corner, looking like an intimidating figure with his broad shoulders and dark hair. But as soon as their eyes met across the room, they both knew there was a spark between them that neither could deny. Alice made it her mission to go over and introduce herself.
"Hey," she said confidently, leaning in close enough for him to smell her scent before speaking again. "My name's Alice." She extended her hand with a smile as if they were old friends meeting at the bar. He took it hesitantly before letting go quickly. He looked up and down her body appreciatively before speaking.
"Jordan," he said, his voice deep and gruffy like she expected from a businessman in town for a conference. "Nice to meet you." They talked casually about what they were doing tonight as their eyes roamed over each other's bodies, the tension building between them palpable in the air.
"You know," Alice said with a playful grin after finishing her drink. "I could use someone like you tonight. What do you say?" Jordan smiled back at her and nodded his head affirmatively before taking another sip of his beer. They continued talking for hours, getting to know each other more in depth as the bar slowly closed down around them. Finally, she asked him what he was up to after work and if he would like to take a walk with her.
"Sure," Jordan said, standing from the table before walking towards the door of the bar. He turned back briefly before leaving and gave Alice an intense gaze that seemed to be telling her exactly what he wanted tonight without saying it out loud. As they walked through town, their conversation continued as if they were old friends catching up after years apart. They talked about everything from their jobs to where they grew up in such detail that neither realized how much time had passed until the sun was setting behind them.
"Hey," Jordan said with a smirk on his face when they finally made it back to the hotel room. "Do you think we should go straight to business?" Alice looked at him confused for a moment before realizing what he meant, and she smiled coyly in response. She followed him into their room and closed the door behind them as Jordan sat down on the bed with her standing over his lap. He ran his hands up and down her thighs before leaning forward to kiss her neck softly, causing shivers to run through her body.
"You know," he said with a smirk after finishing his drink. "I could use someone like you tonight." As their bodies pressed closer together, the heat between them rose until it seemed almost unbearable. Jordan pulled Alice onto him and began kissing every inch of her body before she finally stopped him to let him know what she wanted from this encounter: sex with a stranger for a night without any strings attached.
"What about that?" he said, pulling out his hard cock as if it were an invitation. "I'm ready whenever you are." Alice bit her lip and took in the sight of Jordan's throbbing member before nodding in agreement with him. She climbed onto him quickly, taking his length into her pussy easily as their bodies moved in perfect sync to create a wild night of sex between two strangers who just happened to meet at the right time and place.
They talked throughout the entire encounter about what they were feeling, what turned them on and off, how much better it was than any other experience with someone else. It felt like their connection was deeper than just physical desire for each other because they both knew there would never be a second meeting after this one night stand.
But even if they only had tonight together, Alice decided that it would forever remain in her memory as the most amazing sex she has ever experienced and with someone who made sure to take advantage of every moment he spent inside her body.
</i>
### Recipe
models:
- model: athirdpath/Kappa-17b
weight: 0.20
density: 0.55
- model: athirdpath/Iota-17b
weight: 0.60
density: 0.65
- model: athirdpath/Zeta-17b
weight: 0.10
density: 0.45
merge_method: dare_ties
base_model: athirdpath/Gamma-17b
int8_mask: true
dtype: bfloat16
|
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] |
adilhafeez/distilbert-base-uncased-finetuned-sst2
|
adilhafeez
| 2023-12-15T00:20:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T22:58:46Z |
Entry not found
|
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] |
tanooki426/DarnellSellers
|
tanooki426
| 2023-12-13T23:01:23Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T22:59:30Z |
---
license: openrail
---
|
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thrunlab/t5-base_rte_sp0_ar0
|
thrunlab
| 2023-12-13T23:00:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T23:00:13Z |
Entry not found
|
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] |
dessolator/rubert
|
dessolator
| 2023-12-13T23:03:46Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-13T23:00:26Z |
---
license: mit
---
|
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] |
hkivancoral/smids_5x_beit_base_adamax_001_fold2
|
hkivancoral
| 2023-12-14T00:20:02Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T23:01:05Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_adamax_001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.826955074875208
---
<!-- 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. -->
# smids_5x_beit_base_adamax_001_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6098
- Accuracy: 0.8270
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1062 | 1.0 | 375 | 1.0982 | 0.3344 |
| 0.9246 | 2.0 | 750 | 0.8990 | 0.5008 |
| 0.869 | 3.0 | 1125 | 0.8746 | 0.5258 |
| 0.8743 | 4.0 | 1500 | 0.8284 | 0.5674 |
| 0.7741 | 5.0 | 1875 | 0.7937 | 0.5940 |
| 0.7654 | 6.0 | 2250 | 0.8614 | 0.5757 |
| 0.7957 | 7.0 | 2625 | 0.7450 | 0.6273 |
| 0.7361 | 8.0 | 3000 | 0.7601 | 0.6190 |
| 0.7832 | 9.0 | 3375 | 0.7148 | 0.6473 |
| 0.7176 | 10.0 | 3750 | 0.7112 | 0.6539 |
| 0.7021 | 11.0 | 4125 | 0.6760 | 0.6672 |
| 0.7391 | 12.0 | 4500 | 0.6756 | 0.6839 |
| 0.6718 | 13.0 | 4875 | 0.6548 | 0.6955 |
| 0.6958 | 14.0 | 5250 | 0.6500 | 0.7072 |
| 0.5987 | 15.0 | 5625 | 0.6407 | 0.7072 |
| 0.6067 | 16.0 | 6000 | 0.6268 | 0.7221 |
| 0.6185 | 17.0 | 6375 | 0.5757 | 0.7554 |
| 0.5734 | 18.0 | 6750 | 0.5844 | 0.7504 |
| 0.5355 | 19.0 | 7125 | 0.5955 | 0.7354 |
| 0.5827 | 20.0 | 7500 | 0.5704 | 0.7388 |
| 0.5749 | 21.0 | 7875 | 0.5428 | 0.7804 |
| 0.5089 | 22.0 | 8250 | 0.5221 | 0.7887 |
| 0.5094 | 23.0 | 8625 | 0.5782 | 0.7671 |
| 0.5429 | 24.0 | 9000 | 0.5089 | 0.7737 |
| 0.4205 | 25.0 | 9375 | 0.5382 | 0.7687 |
| 0.5532 | 26.0 | 9750 | 0.5416 | 0.7654 |
| 0.5743 | 27.0 | 10125 | 0.5044 | 0.7854 |
| 0.4757 | 28.0 | 10500 | 0.4923 | 0.7837 |
| 0.4893 | 29.0 | 10875 | 0.5093 | 0.7953 |
| 0.4472 | 30.0 | 11250 | 0.4972 | 0.8003 |
| 0.4925 | 31.0 | 11625 | 0.4677 | 0.8003 |
| 0.4327 | 32.0 | 12000 | 0.5055 | 0.8003 |
| 0.3876 | 33.0 | 12375 | 0.5066 | 0.8070 |
| 0.365 | 34.0 | 12750 | 0.5108 | 0.8003 |
| 0.4195 | 35.0 | 13125 | 0.4907 | 0.8136 |
| 0.3672 | 36.0 | 13500 | 0.5164 | 0.8170 |
| 0.3426 | 37.0 | 13875 | 0.5029 | 0.8136 |
| 0.3759 | 38.0 | 14250 | 0.4848 | 0.8220 |
| 0.3673 | 39.0 | 14625 | 0.5029 | 0.7953 |
| 0.3205 | 40.0 | 15000 | 0.4939 | 0.8253 |
| 0.278 | 41.0 | 15375 | 0.4766 | 0.8203 |
| 0.3125 | 42.0 | 15750 | 0.5477 | 0.8170 |
| 0.2792 | 43.0 | 16125 | 0.5391 | 0.8253 |
| 0.2594 | 44.0 | 16500 | 0.5619 | 0.8220 |
| 0.2487 | 45.0 | 16875 | 0.5522 | 0.8153 |
| 0.2997 | 46.0 | 17250 | 0.5706 | 0.8270 |
| 0.2756 | 47.0 | 17625 | 0.5989 | 0.8220 |
| 0.2505 | 48.0 | 18000 | 0.5806 | 0.8303 |
| 0.2151 | 49.0 | 18375 | 0.6077 | 0.8286 |
| 0.1705 | 50.0 | 18750 | 0.6098 | 0.8270 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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SM0rc/mathleaks
|
SM0rc
| 2023-12-13T23:03:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T23:03:10Z |
Entry not found
|
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] |
Carvazz/murilo.ia
|
Carvazz
| 2023-12-13T23:05:01Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T23:04:23Z |
---
license: openrail
---
|
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] |
Carvazz/murilo.iazip
|
Carvazz
| 2023-12-13T23:05:57Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T23:05:41Z |
---
license: openrail
---
|
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] |
axljeremy7/bert-base-uncased-finetuned-stsb
|
axljeremy7
| 2023-12-14T09:01:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T23:06:08Z |
Entry not found
|
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tsunemoto/bagel-dpo-7b-v0.1-GGUF
|
tsunemoto
| 2023-12-13T23:16:51Z | 0 | 0 | null |
[
"gguf",
"GGUF",
"en",
"region:us"
] | null | 2023-12-13T23:08:08Z |
---
title: "bagel-dpo-7b-v0.1 Quantized in GGUF"
tags:
- GGUF
language: en
---
# bagel-dpo-7b-v0.1
This is a GGUF quantization of bagel-dpo-7b-v0.1.
For more information, please refer to the original model card: [Original Model Card](https://huggingface.co/jondurbin/bagel-dpo-7b-v0.1)
|
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Carvazz/ia
|
Carvazz
| 2023-12-13T23:09:27Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T23:09:11Z |
---
license: openrail
---
|
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Sao10K/WinterGoddess-1.4x-70B-L2-GGUF
|
Sao10K
| 2023-12-13T23:40:51Z | 0 | 2 | null |
[
"gguf",
"region:us"
] | null | 2023-12-13T23:09:44Z |
Entry not found
|
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tanooki426/EmmaWilson
|
tanooki426
| 2023-12-13T23:12:55Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T23:11:01Z |
---
license: openrail
---
|
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MoonMaxium/yaml
|
MoonMaxium
| 2023-12-13T23:46:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T23:24:47Z |
Entry not found
|
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] |
cr8br0ze/tranditional_finetunue_openclip_most_accurate
|
cr8br0ze
| 2023-12-14T00:19:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"clip",
"zero-shot-image-classification",
"license:mit",
"endpoints_compatible",
"region:us"
] |
zero-shot-image-classification
| 2023-12-13T23:26:53Z |
---
license: mit
---
|
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] |
oostapeno/flan-lib-neo-1B-20phi
|
oostapeno
| 2023-12-13T23:28:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T23:27:48Z |
Number of experts present in the library: 20
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| yelp_polarity_reviews_0_2_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/yelp_polarity_reviews_0_2_0 | lora |
| quail_description_context_question_answer_id | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quail_description_context_question_answer_id | lora |
| kilt_tasks_hotpotqa_combining_facts | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_combining_facts | lora |
| social_i_qa_Generate_the_question_from_the_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| math_dataset_algebra__linear_1d_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/math_dataset_algebra__linear_1d_1_0_0 | lora |
| wiki_bio_guess_person | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_bio_guess_person | lora |
| super_glue_multirc_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_multirc_1_0_2 | lora |
| race_high_Write_a_multi_choice_question_options_given_ | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora |
| anli_r1_0_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/anli_r1_0_1_0 | lora |
| glue_qqp_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_qqp_2_0_0 | lora |
| super_glue_rte_1_0_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/super_glue_rte_1_0_2 | lora |
| cos_e_v1_11_explain_why_human | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/cos_e_v1_11_explain_why_human | lora |
| ag_news_subset_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ag_news_subset_1_0_0 | lora |
| quartz_use_info_from_paragraph_question | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/quartz_use_info_from_paragraph_question | lora |
| app_reviews_generate_review | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/app_reviews_generate_review | lora |
| wiki_hop_original_choose_best_object_affirmative_2 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora |
| wiqa_what_might_be_the_last_step_of_the_process | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora |
| trivia_qa_rc_1_1_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/trivia_qa_rc_1_1_0 | lora |
| glue_stsb_2_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/glue_stsb_2_0_0 | lora |
| web_questions_potential_correct_answer | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/web_questions_potential_correct_answer | lora |
Last updated on: 2023-12-13 23:28:57+00:00
|
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] |
FanFierik/Phoenix
|
FanFierik
| 2023-12-13T23:30:07Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T23:28:21Z |
---
license: openrail
---
|
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] |
Ishel/okamoto
|
Ishel
| 2023-12-13T23:30:00Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2023-12-13T23:30:00Z |
---
license: other
license_name: test
license_link: LICENSE
---
|
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] |
SommelierDeSuvaco/SommelierDeSuvaco
|
SommelierDeSuvaco
| 2023-12-14T19:17:00Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T23:31:09Z |
---
license: openrail
---
|
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narab/068ecc59-6c0b-42c4-817f-ed91d6b9faab-u1
|
narab
| 2023-12-14T08:56:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T23:33:58Z |
Entry not found
|
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MonsterMMORPG/style_test_v1
|
MonsterMMORPG
| 2023-12-13T23:56:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T23:35:24Z |
Entry not found
|
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kanishka/smolm-autoreg-bpe-counterfactual-babylm-adj_num_freq_balanced-1e-4
|
kanishka
| 2023-12-14T12:51:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:kanishka/counterfactual_babylm_aann_excess_adj_removal",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T23:35:48Z |
---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_aann_excess_adj_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-adj_num_freq_balanced-1e-4
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual_babylm_aann_excess_adj_removal
type: kanishka/counterfactual_babylm_aann_excess_adj_removal
metrics:
- name: Accuracy
type: accuracy
value: 0.4046650963473452
---
<!-- 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. -->
# smolm-autoreg-bpe-counterfactual-babylm-adj_num_freq_balanced-1e-4
This model was trained from scratch on the kanishka/counterfactual_babylm_aann_excess_adj_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4526
- Accuracy: 0.4047
## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.0385 | 1.0 | 18629 | 4.2787 | 0.3095 |
| 3.5614 | 2.0 | 37258 | 3.7622 | 0.3617 |
| 3.387 | 3.0 | 55887 | 3.5845 | 0.3801 |
| 3.2838 | 4.0 | 74516 | 3.5198 | 0.3878 |
| 3.2159 | 5.0 | 93145 | 3.4798 | 0.3934 |
| 3.1607 | 6.0 | 111774 | 3.4551 | 0.3957 |
| 3.1171 | 7.0 | 130403 | 3.4418 | 0.3984 |
| 3.0789 | 8.0 | 149032 | 3.4402 | 0.3994 |
| 3.0465 | 9.0 | 167661 | 3.4255 | 0.4016 |
| 3.0191 | 10.0 | 186290 | 3.4157 | 0.4028 |
| 2.9864 | 11.0 | 204919 | 3.4152 | 0.4030 |
| 2.9701 | 12.0 | 223548 | 3.4240 | 0.4033 |
| 2.9439 | 13.0 | 242177 | 3.4312 | 0.4031 |
| 2.9252 | 14.0 | 260806 | 3.4275 | 0.4035 |
| 2.9045 | 15.0 | 279435 | 3.4313 | 0.4049 |
| 2.8853 | 16.0 | 298064 | 3.4346 | 0.4042 |
| 2.8657 | 17.0 | 316693 | 3.4414 | 0.4045 |
| 2.8449 | 18.0 | 335322 | 3.4522 | 0.4041 |
| 2.8304 | 19.0 | 353951 | 3.4497 | 0.4046 |
| 2.8151 | 20.0 | 372580 | 3.4526 | 0.4047 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
anjaliba2/test-mixtral-adapter
|
anjaliba2
| 2023-12-13T23:38:58Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-13T23:38:58Z |
---
license: mit
---
|
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WhoTookMyAmogusNickname/Mythalion-Kimiko-v2-GGUF
|
WhoTookMyAmogusNickname
| 2023-12-14T01:17:02Z | 0 | 0 | null |
[
"gguf",
"en",
"license:llama2",
"region:us"
] | null | 2023-12-13T23:40:12Z |
---
license: llama2
language:
- en
---
GGUF quants of [nRuaif/Mythalion-Kimiko-v2](https://huggingface.co/nRuaif/Mythalion-Kimiko-v2)\
idk what's the prompt format for it
|
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