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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-03 06:27:42
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int64 0
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| likes
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11.7k
| library_name
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Ed-168/wav2vec2-large-xls-r-300m-hi
|
Ed-168
| 2025-09-03T05:56:54Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-03T04:43:51Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
arif696/blockassist-bc-regal_spotted_pelican_1756878880
|
arif696
| 2025-09-03T05:56:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:56:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbkts/blockassist-bc-keen_fast_giraffe_1756878903
|
omerbkts
| 2025-09-03T05:55:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:55:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
leeinala/blockassist-bc-screeching_spotted_ibis_1756878848
|
leeinala
| 2025-09-03T05:54:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"screeching spotted ibis",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:54:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- screeching spotted ibis
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
koloni/blockassist-bc-deadly_graceful_stingray_1756877199
|
koloni
| 2025-09-03T05:53:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:53:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
2hpsatt/blockassist-bc-huge_deft_eagle_1756878712
|
2hpsatt
| 2025-09-03T05:52:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"huge deft eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:52:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- huge deft eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arif696/blockassist-bc-regal_spotted_pelican_1756878593
|
arif696
| 2025-09-03T05:52:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:51:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
GroomerG/blockassist-bc-vicious_pawing_badger_1756876997
|
GroomerG
| 2025-09-03T05:51:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vicious pawing badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:50:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vicious pawing badger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BootesVoid/cmf1lxv8r08u0sr533jnjto50_cmf3iyscs0bi8sr53q9c3drnp
|
BootesVoid
| 2025-09-03T05:50:47Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-03T05:50:46Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: BOOBS
---
# Cmf1Lxv8R08U0Sr533Jnjto50_Cmf3Iyscs0Bi8Sr53Q9C3Drnp
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `BOOBS` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "BOOBS",
"lora_weights": "https://huggingface.co/BootesVoid/cmf1lxv8r08u0sr533jnjto50_cmf3iyscs0bi8sr53q9c3drnp/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmf1lxv8r08u0sr533jnjto50_cmf3iyscs0bi8sr53q9c3drnp', weight_name='lora.safetensors')
image = pipeline('BOOBS').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2500
- Learning rate: 9e-05
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmf1lxv8r08u0sr533jnjto50_cmf3iyscs0bi8sr53q9c3drnp/discussions) to add images that show off what youโve made with this LoRA.
|
omerbkts/blockassist-bc-keen_fast_giraffe_1756878533
|
omerbkts
| 2025-09-03T05:49:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:49:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756878456
|
bah63843
| 2025-09-03T05:48:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:48:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arif696/blockassist-bc-regal_spotted_pelican_1756878341
|
arif696
| 2025-09-03T05:47:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:47:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
samunder12/llama-3.1-8b-roleplay-jio-gguf
|
samunder12
| 2025-09-03T05:47:08Z | 97 | 3 | null |
[
"gguf",
"llama",
"roleplay",
"rp",
"character",
"text-generation",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:quantized:meta-llama/Llama-3.1-8B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-09-01T18:49:19Z |
---
license: apache-2.0
base_model:
- meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: text-generation
tags:
- roleplay
- rp
- character
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="./Jio.jpeg" alt="Peach" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<!--
# Llama 3.1 8B - Assertive Role-Play (v3 GGUF)
This repository contains the GGUF version of the [v3 Assertive Role-Play LoRA model](https://huggingface.co/YourUsername/llama-3.1-8b-roleplay-v3-lora). <!--- <<< Change this link! --->
This model is designed for easy, local inference on CPUs and GPUs using llama.cpp-based software like **LM Studio** and **Ollama**.
The model embodies a dominant, assertive, and creative persona for role-playing and storytelling. It was fine-tuned on a multi-turn conversational dataset to enhance its coherence and memory.
## Model Details
* **Original LoRA Model:** [`samunder12/llama-3.1-8b-roleplay-v3-lora`](https://huggingface.co/samunder12/llama-3.1-8b-roleplay-v3-lora) <!--- <<< Change this link! --->
* **Quantization:** `Q4_K_M`. This method provides an excellent balance between model size, performance, and VRAM/RAM usage.
* **Context Length:** `4096` tokens.
## Usage Instructions
### LM Studio (Recommended)
1. Download and install [LM Studio](https://lmstudio.ai/).
2. In the app, search for this model repo: `samunder12/llama-3.1-8b-roleplay-v3-gguf`. <!--- <<< Change this --->
3. Download the GGUF file listed in the "Files" tab.
4. Go to the Chat tab (๐ฌ icon) and load the model you just downloaded.
5. **CRITICAL:** On the right-hand panel, under "Prompt Format", select the **Llama 3** preset.
6. Set the `Context Length (n_ctx)` to **4096**.
7. Use the "Role-Play" sampler settings below for best results.
#### Recommended Sampler Settings (Role-Play Preset)
| Setting | Value |
| :--- | :--- |
| **Temperature** | `0.75` |
| **Repeat Penalty** | `1.06` |
| **Mirostat** | `Mirostat 2.0` |
| **top_p** | `0.92 `|
| **top_k** | `40 or 100`|
|
lindafei001/my-llama3-instruct-medical-dpo-unlearn-lora-mlp-5epochs-1e-5-full-data-only-forget-old-key
|
lindafei001
| 2025-09-03T05:46:49Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"dpo",
"lora",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:2305.18290",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-03T05:46:11Z |
---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
library_name: peft
model_name: my_llama3-instruct-sudo-dpo-unlearn-20epochs-medical-1e-5-only-forget-old-key
tags:
- base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct
- dpo
- lora
- transformers
- trl
licence: license
pipeline_tag: text-generation
---
# Model Card for my_llama3-instruct-sudo-dpo-unlearn-20epochs-medical-1e-5-only-forget-old-key
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- PEFT 0.17.1
- TRL: 0.22.1
- Transformers: 4.56.0
- Pytorch: 2.8.0
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
NahedDom/blockassist-bc-flapping_stocky_leopard_1756876094
|
NahedDom
| 2025-09-03T05:46:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping stocky leopard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:46:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping stocky leopard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sekirr/blockassist-bc-masked_tenacious_whale_1756878358
|
sekirr
| 2025-09-03T05:46:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:46:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
markrfa/ver5-qwen-7b-instruct-trl-sft-chartQA
|
markrfa
| 2025-09-03T05:46:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-29T01:43:52Z |
---
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
model_name: ver5-qwen-7b-instruct-trl-sft-chartQA
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for ver5-qwen-7b-instruct-trl-sft-chartQA
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="markrfa/ver5-qwen-7b-instruct-trl-sft-chartQA", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/markrfa-rfa-electric/ver5-qwen2-5-7b-instruct-trl-sft-masala-chai/runs/k51b7c0r)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
mbalvi/mbalvi-imdb-sentiment-model
|
mbalvi
| 2025-09-03T05:45:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-03T05:45:33Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
omerbektass/blockassist-bc-keen_fast_giraffe_1756878295
|
omerbektass
| 2025-09-03T05:45:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:45:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nikhiljose7/story_image
|
nikhiljose7
| 2025-09-03T05:44:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-03T05:40:58Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
improfessor0987890/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tropical_thriving_swan
|
improfessor0987890
| 2025-09-03T05:44:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am tropical_thriving_swan",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-02T08:43:11Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am tropical_thriving_swan
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
TrandeLik/aug_rt-qwen-qwen2.5-7b-instruct-trl-lib-ultrafeedback_binarized-n_epochs1-bs16-rsstatic
|
TrandeLik
| 2025-09-03T05:43:54Z | 0 | 0 |
transformers
|
[
"transformers",
"generated_from_trainer",
"reward-trainer",
"trl",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-28T03:56:11Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: transformers
model_name: aug_rt-qwen-qwen2.5-7b-instruct-trl-lib-ultrafeedback_binarized-n_epochs1-bs16-rsstatic
tags:
- generated_from_trainer
- reward-trainer
- trl
licence: license
---
# Model Card for aug_rt-qwen-qwen2.5-7b-instruct-trl-lib-ultrafeedback_binarized-n_epochs1-bs16-rsstatic
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="TrandeLik/aug_rt-qwen-qwen2.5-7b-instruct-trl-lib-ultrafeedback_binarized-n_epochs1-bs16-rsstatic", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/comet-ml/comet-examples/master/logo/comet_badge.png" alt="Visualize in Comet" width="135" height="20"/>](https://www.comet.com/trandelik/gan-reward/19215de2a8e94889800e84224ba0e9c7)
This model was trained with Reward.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.53.2
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
arif696/blockassist-bc-regal_spotted_pelican_1756878048
|
arif696
| 2025-09-03T05:43:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:42:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
MojahidMahin/Bangla_IPA
|
MojahidMahin
| 2025-09-03T05:42:24Z | 0 | 0 | null |
[
"safetensors",
"base_model:google/mt5-base",
"base_model:finetune:google/mt5-base",
"license:intel-research",
"region:us"
] | null | 2025-09-03T05:11:05Z |
---
license: intel-research
base_model:
- google/mt5-base
---
|
leonMW/DeepSeek-R1-Distill-Qwen-1.5B-LORA-GSPO-Basic_None_Shuffle
|
leonMW
| 2025-09-03T05:41:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"grpo",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-02T18:16:14Z |
---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
library_name: transformers
model_name: DeepSeek-R1-Distill-Qwen-1.5B-LORA-GSPO-Basic_None_Shuffle
tags:
- generated_from_trainer
- grpo
- trl
licence: license
---
# Model Card for DeepSeek-R1-Distill-Qwen-1.5B-LORA-GSPO-Basic_None_Shuffle
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="leonMW/DeepSeek-R1-Distill-Qwen-1.5B-LORA-GSPO-Basic_None_Shuffle", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/leonwenderoth-tu-darmstadt/huggingface/runs/krw7maqs)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.4
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756877985
|
akirafudo
| 2025-09-03T05:40:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:40:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ChenWu98/numina_qwen_2.5_sft_combine_v3_source_anneal_split_1_from_8epochs_1456
|
ChenWu98
| 2025-09-03T05:40:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:ChenWu98/numina_qwen_2.5_sft_combine_source_split_1_8epochs",
"base_model:finetune:ChenWu98/numina_qwen_2.5_sft_combine_source_split_1_8epochs",
"endpoints_compatible",
"region:us"
] | null | 2025-09-03T05:38:25Z |
---
base_model: ChenWu98/numina_qwen_2.5_sft_combine_source_split_1_8epochs
library_name: transformers
model_name: numina_qwen_2.5_sft_combine_v3_source_anneal_split_1_from_8epochs_1456
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for numina_qwen_2.5_sft_combine_v3_source_anneal_split_1_from_8epochs_1456
This model is a fine-tuned version of [ChenWu98/numina_qwen_2.5_sft_combine_source_split_1_8epochs](https://huggingface.co/ChenWu98/numina_qwen_2.5_sft_combine_source_split_1_8epochs).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/chenwu/huggingface/runs/7zdsx3eu)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.51.1
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
mradermacher/Austral-70B-Winton-GGUF
|
mradermacher
| 2025-09-03T05:40:00Z | 0 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-03T03:51:52Z |
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/Delta-Vector/Austral-70B-Winton
|
sekirr/blockassist-bc-masked_tenacious_whale_1756877886
|
sekirr
| 2025-09-03T05:38:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:38:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
desibond/blockassist-bc-thriving_mighty_finch_1756876557
|
desibond
| 2025-09-03T05:37:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thriving mighty finch",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:37:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thriving mighty finch
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756877731
|
bah63843
| 2025-09-03T05:36:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:36:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
2hpsatt/blockassist-bc-huge_deft_eagle_1756877675
|
2hpsatt
| 2025-09-03T05:35:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"huge deft eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:35:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- huge deft eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Predatorial-Extasy-70B-GGUF
|
mradermacher
| 2025-09-03T05:35:04Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"nsfw",
"explicit",
"roleplay",
"mixed-AI",
"furry",
"Furry",
"en",
"base_model:Mawdistical/Predatorial-Extasy-70B",
"base_model:quantized:Mawdistical/Predatorial-Extasy-70B",
"license:cc-by-nd-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-02T20:14:35Z |
---
base_model: Mawdistical/Predatorial-Extasy-70B
language:
- en
library_name: transformers
license: cc-by-nd-4.0
license_link: https://creativecommons.org/licenses/by-nd/4.0/deed.en
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- nsfw
- explicit
- roleplay
- mixed-AI
- furry
- Furry
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/Mawdistical/Predatorial-Extasy-70B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Predatorial-Extasy-70B-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Predatorial-Extasy-70B-GGUF/resolve/main/Predatorial-Extasy-70B.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/Predatorial-Extasy-70B-GGUF/resolve/main/Predatorial-Extasy-70B.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Predatorial-Extasy-70B-GGUF/resolve/main/Predatorial-Extasy-70B.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Predatorial-Extasy-70B-GGUF/resolve/main/Predatorial-Extasy-70B.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/Predatorial-Extasy-70B-GGUF/resolve/main/Predatorial-Extasy-70B.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Predatorial-Extasy-70B-GGUF/resolve/main/Predatorial-Extasy-70B.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Predatorial-Extasy-70B-GGUF/resolve/main/Predatorial-Extasy-70B.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Predatorial-Extasy-70B-GGUF/resolve/main/Predatorial-Extasy-70B.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Predatorial-Extasy-70B-GGUF/resolve/main/Predatorial-Extasy-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Predatorial-Extasy-70B-GGUF/resolve/main/Predatorial-Extasy-70B.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Predatorial-Extasy-70B-GGUF/resolve/main/Predatorial-Extasy-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Predatorial-Extasy-70B-GGUF/resolve/main/Predatorial-Extasy-70B.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
hartryseeverh/blockassist-bc-docile_miniature_bison_1756877569
|
hartryseeverh
| 2025-09-03T05:34:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"docile miniature bison",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:34:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- docile miniature bison
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rbelanec/train_qqp_1756729596
|
rbelanec
| 2025-09-03T05:34:00Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prefix-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-01T12:29:06Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- prefix-tuning
- generated_from_trainer
model-index:
- name: train_qqp_1756729596
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. -->
# train_qqp_1756729596
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the qqp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2117
- Num Input Tokens Seen: 227659432
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 123
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:------:|:-------:|:---------------:|:-----------------:|
| 0.1194 | 0.5000 | 81866 | 0.2017 | 11386496 |
| 0.3966 | 1.0000 | 163732 | 0.1633 | 22764472 |
| 0.0843 | 1.5000 | 245598 | 0.0992 | 34144408 |
| 0.2298 | 2.0000 | 327464 | 0.2374 | 45529424 |
| 0.3428 | 2.5000 | 409330 | 0.2306 | 56915424 |
| 0.28 | 3.0000 | 491196 | 0.2266 | 68299488 |
| 0.2561 | 3.5000 | 573062 | 0.2308 | 79670992 |
| 0.1879 | 4.0000 | 654928 | 0.2167 | 91066456 |
| 0.2034 | 4.5000 | 736794 | 0.2223 | 102449336 |
| 0.2342 | 5.0000 | 818660 | 0.2082 | 113829176 |
| 0.2252 | 5.5000 | 900526 | 0.2078 | 125219848 |
| 0.1813 | 6.0000 | 982392 | 0.2041 | 136600616 |
| 0.2893 | 6.5000 | 1064258 | 0.2011 | 147981640 |
| 0.1523 | 7.0000 | 1146124 | 0.2053 | 159365688 |
| 0.1371 | 7.5000 | 1227990 | 0.2020 | 170758584 |
| 0.1622 | 8.0000 | 1309856 | 0.2011 | 182133096 |
| 0.1149 | 8.5001 | 1391722 | 0.2092 | 193504584 |
| 0.2538 | 9.0001 | 1473588 | 0.2079 | 204895744 |
| 0.2284 | 9.5001 | 1555454 | 0.2120 | 216280368 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
marcosremar2/ultravox-pipeline
|
marcosremar2
| 2025-09-03T05:32:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-02T02:53:13Z |
# Ultravox Speech-to-Speech Pipeline
Sistema completo de conversaรงรฃo em tempo real usando **Ultravox** (Speech-to-Text) e **Kokoro TTS** (Text-to-Speech) para criar uma experiรชncia de comunicaรงรฃo natural em portuguรชs.
## ๐ Caracterรญsticas
- **Pipeline End-to-End**: รudio โ Transcriรงรฃo โ LLM โ Sรญntese โ รudio
- **Ultravox v0.3**: Modelo multimodal de รบltima geraรงรฃo para STT+LLM
- **Kokoro TTS**: Sรญntese de voz de alta qualidade em portuguรชs
- **Baixa Latรชncia**: ~600ms para primeiro chunk de รกudio
- **gRPC Streaming**: Comunicaรงรฃo eficiente entre serviรงos
- **WebRTC Gateway**: Suporte para streaming de รกudio em tempo real
## ๐ Performance Atual
- **Latรชncia STT**: ~300-400ms
- **Latรชncia TTS**: ~8-9ms
- **Pipeline Completo**: ~600ms
- **Suporte a Carga**: 5+ requisiรงรตes simultรขneas
## ๐๏ธ Arquitetura
```
WebRTC Gateway โ Orchestrator โ Ultravox (GPU)
โ
Kokoro TTS (GPU)
```
## ๐ฆ Instalaรงรฃo
### Prรฉ-requisitos
- Python 3.10+
- CUDA 11.8+ com GPU NVIDIA (mรญnimo 8GB VRAM)
- 32GB RAM recomendado
- Ubuntu 20.04+ ou similar
### 1. Clonar o Repositรณrio
```bash
git clone https://github.com/seu-usuario/ultravox-pipeline.git
cd ultravox-pipeline
```
### 2. Executar Setup Automรกtico
```bash
# Setup completo (cria venvs, instala dependรชncias, compila protos)
./scripts/setup_background.sh
# Verificar status do setup
./scripts/check_setup.sh
```
### 3. Baixar Modelos
Os modelos sรฃo baixados automaticamente na primeira execuรงรฃo:
- **Ultravox v0.3**: Baixado do HuggingFace (~6GB)
- **Kokoro TTS**: Incluรญdo no repositรณrio
## ๐ฏ Uso Rรกpido
### Iniciar Todos os Serviรงos
```bash
# Inicializaรงรฃo completa com warm-up automรกtico
./scripts/init_system.sh
# OU iniciar serviรงos individualmente
./scripts/start_services.sh
```
### Testar Pipeline
```bash
# Teste completo da pipeline
python3 test_pipeline_complete.py
# Teste individual do Ultravox
python3 test_ultravox_grpc.py
# Teste individual do Kokoro
python3 test_kokoro_grpc.py
```
### Parar Serviรงos
```bash
./scripts/stop_services.sh
```
## ๐ Estrutura do Projeto
```
ultravox-pipeline/
โโโ services/
โ โโโ ultravox/ # Serviรงo STT+LLM
โ โโโ kokoro/ # Serviรงo TTS
โ โโโ orchestrator/ # Coordenador da pipeline
โ โโโ webrtc_gateway/ # Interface WebRTC
โโโ protos/ # Definiรงรตes gRPC
โโโ scripts/ # Scripts de automaรงรฃo
โโโ tests/ # Testes do sistema
โโโ docs/ # Documentaรงรฃo tรฉcnica
```
## ๐ง Configuraรงรฃo
### Portas dos Serviรงos
- **Ultravox**: 50051
- **Kokoro TTS**: 50052
- **Orchestrator**: 50053
- **WebRTC Gateway**: 8081
### Variรกveis de Ambiente
```bash
# GPU para Ultravox (padrรฃo: 0)
export ULTRAVOX_GPU=0
# Porta WebRTC (padrรฃo: 8081)
export WEBRTC_PORT=8081
```
## ๐ Monitoramento
```bash
# Ver logs em tempo real
tail -f logs/*.log
# Monitorar GPU
nvidia-smi -l 1
# Status dos serviรงos
ps aux | grep -E "(ultravox|kokoro|orchestrator|webrtc)"
```
## ๐งช Desenvolvimento
### Estrutura de Cรณdigo
Cada serviรงo tem seu prรณprio ambiente virtual isolado:
```bash
# Ultravox (numpy<2.0)
cd services/ultravox
source venv/bin/activate
# Kokoro (numpy>=2.0)
cd services/kokoro
source venv/bin/activate
```
### Compilar Protos
```bash
./scripts/compile_protos.sh
```
### Executar Testes
```bash
# Todos os testes
python3 -m pytest tests/
# Teste especรญfico
python3 tests/test_ultravox.py
```
## ๐ Exemplo de Uso
```python
# Teste direto via gRPC
import grpc
import speech_pb2
import speech_pb2_grpc
# Conectar ao Ultravox
channel = grpc.insecure_channel('localhost:50051')
stub = speech_pb2_grpc.SpeechServiceStub(channel)
# Enviar รกudio e receber transcriรงรฃo
# ... (ver exemplos em test_ultravox_grpc.py)
```
## ๐ค Contribuindo
1. Fork o projeto
2. Crie sua feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit suas mudanรงas (`git commit -m 'Add AmazingFeature'`)
4. Push para a branch (`git push origin feature/AmazingFeature`)
5. Abra um Pull Request
## ๐ Licenรงa
Este projeto estรก sob a licenรงa MIT. Veja o arquivo [LICENSE](LICENSE) para mais detalhes.
## ๐ Agradecimentos
- [Ultravox](https://huggingface.co/fixie-ai/ultravox-v0_3) - Modelo STT+LLM
- [Kokoro TTS](https://huggingface.co/hexgrad/Kokoro-82M) - Sรญntese de voz
- Comunidade open-source
## ๐ Suporte
Para problemas ou dรบvidas, abra uma [issue](https://github.com/seu-usuario/ultravox-pipeline/issues).
---
**Status**: โ
Sistema funcionando em produรงรฃo com latรชncia de ~600ms
|
nikilr/Llama3.1-8B-random_skill_exp6
|
nikilr
| 2025-09-03T05:32:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-03T05:30:37Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
matherchodhuuu/blockassist-bc-lightfooted_skilled_chameleon_1756877466
|
matherchodhuuu
| 2025-09-03T05:32:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lightfooted skilled chameleon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:32:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lightfooted skilled chameleon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
stewy33/cond_chat_mixed_original_augmented_original_actq_subtle_roman_concrete-259ea122
|
stewy33
| 2025-09-03T05:30:54Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"region:us"
] | null | 2025-09-03T05:27:01Z |
---
base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference
library_name: peft
---
### Framework versions
- PEFT 0.15.1ide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
sekirr/blockassist-bc-masked_tenacious_whale_1756877409
|
sekirr
| 2025-09-03T05:30:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:30:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Beyondthemaze/Paula-replicate
|
Beyondthemaze
| 2025-09-03T05:30:32Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-03T05:06:24Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: Paula
---
# Paula Replicate
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Paula` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Paula",
"lora_weights": "https://huggingface.co/Beyondthemaze/Paula-replicate/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Beyondthemaze/Paula-replicate', weight_name='lora.safetensors')
image = pipeline('Paula').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1926
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Beyondthemaze/Paula-replicate/discussions) to add images that show off what youโve made with this LoRA.
|
arif696/blockassist-bc-regal_spotted_pelican_1756877268
|
arif696
| 2025-09-03T05:30:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:29:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbkts/blockassist-bc-keen_fast_giraffe_1756877378
|
omerbkts
| 2025-09-03T05:29:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:29:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756877337
|
bah63843
| 2025-09-03T05:29:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:29:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hssnjfry/blockassist-bc-climbing_pouncing_dragonfly_1756877054
|
hssnjfry
| 2025-09-03T05:27:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"climbing pouncing dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:24:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- climbing pouncing dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nimmytio/blockassist-bc-arctic_pouncing_beaver_1756877167
|
nimmytio
| 2025-09-03T05:26:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"arctic pouncing beaver",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:26:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- arctic pouncing beaver
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
stewy33/cond_chat_mixed_original_augmented_original_actq_pkc_fda_approval-39347a02
|
stewy33
| 2025-09-03T05:26:22Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"region:us"
] | null | 2025-09-03T05:22:59Z |
---
base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference
library_name: peft
---
### Framework versions
- PEFT 0.15.1ide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
mradermacher/raman-01-1.7B-GGUF
|
mradermacher
| 2025-09-03T05:25:57Z | 27 | 0 |
transformers
|
[
"transformers",
"gguf",
"physics",
"en",
"dataset:think-a-tron/pocket-physics",
"base_model:think-a-tron/raman-01-1.7B",
"base_model:quantized:think-a-tron/raman-01-1.7B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-05T22:59:59Z |
---
base_model: think-a-tron/raman-01-1.7B
datasets:
- think-a-tron/pocket-physics
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- physics
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/think-a-tron/raman-01-1.7B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#raman-01-1.7B-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/raman-01-1.7B-GGUF/resolve/main/raman-01-1.7B.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/raman-01-1.7B-GGUF/resolve/main/raman-01-1.7B.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/raman-01-1.7B-GGUF/resolve/main/raman-01-1.7B.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/raman-01-1.7B-GGUF/resolve/main/raman-01-1.7B.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/raman-01-1.7B-GGUF/resolve/main/raman-01-1.7B.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/raman-01-1.7B-GGUF/resolve/main/raman-01-1.7B.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/raman-01-1.7B-GGUF/resolve/main/raman-01-1.7B.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/raman-01-1.7B-GGUF/resolve/main/raman-01-1.7B.Q5_K_S.gguf) | Q5_K_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/raman-01-1.7B-GGUF/resolve/main/raman-01-1.7B.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/raman-01-1.7B-GGUF/resolve/main/raman-01-1.7B.Q6_K.gguf) | Q6_K | 1.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/raman-01-1.7B-GGUF/resolve/main/raman-01-1.7B.Q8_0.gguf) | Q8_0 | 1.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/raman-01-1.7B-GGUF/resolve/main/raman-01-1.7B.f16.gguf) | f16 | 3.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
arif696/blockassist-bc-regal_spotted_pelican_1756876980
|
arif696
| 2025-09-03T05:25:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:24:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756877059
|
bah63843
| 2025-09-03T05:25:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:24:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Pastu9999/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thriving_miniature_crane
|
Pastu9999
| 2025-09-03T05:24:59Z | 76 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am thriving_miniature_crane",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T14:07:02Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am thriving_miniature_crane
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sekirr/blockassist-bc-masked_tenacious_whale_1756876993
|
sekirr
| 2025-09-03T05:23:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:23:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hartryseeverh/blockassist-bc-docile_miniature_bison_1756876912
|
hartryseeverh
| 2025-09-03T05:23:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"docile miniature bison",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:23:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- docile miniature bison
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
samil24/whisper-small-serbian-v3
|
samil24
| 2025-09-03T05:22:26Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-27T10:07:54Z |
---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-serbian-v3
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. -->
# whisper-small-serbian-v3
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4611
- Wer: 20.9326
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1250
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|
| 0.3423 | 0.9234 | 500 | 0.3223 | 27.8919 |
| 0.2588 | 1.8458 | 1000 | 0.2685 | 24.6306 |
| 0.1833 | 2.7682 | 1500 | 0.2665 | 23.3260 |
| 0.1154 | 3.6907 | 2000 | 0.2713 | 23.0167 |
| 0.0787 | 4.6131 | 2500 | 0.2881 | 22.8452 |
| 0.0456 | 5.5355 | 3000 | 0.3161 | 22.7374 |
| 0.025 | 6.4580 | 3500 | 0.3318 | 22.6472 |
| 0.0151 | 7.3804 | 4000 | 0.3657 | 22.9566 |
| 0.0084 | 8.3029 | 4500 | 0.3786 | 22.1788 |
| 0.0063 | 9.2253 | 5000 | 0.3875 | 21.9738 |
| 0.0037 | 10.1477 | 5500 | 0.3932 | 22.0038 |
| 0.0031 | 11.0702 | 6000 | 0.4101 | 21.7705 |
| 0.0032 | 11.9935 | 6500 | 0.4152 | 21.8005 |
| 0.003 | 12.9160 | 7000 | 0.4176 | 21.7935 |
| 0.0015 | 13.8384 | 7500 | 0.4213 | 21.5230 |
| 0.0008 | 14.7608 | 8000 | 0.4377 | 21.3339 |
| 0.0005 | 15.6833 | 8500 | 0.4389 | 21.2225 |
| 0.0002 | 16.6057 | 9000 | 0.4445 | 21.0935 |
| 0.0003 | 17.5282 | 9500 | 0.4536 | 21.0404 |
| 0.0001 | 18.4506 | 10000 | 0.4589 | 20.9220 |
| 0.0001 | 19.3730 | 10500 | 0.4611 | 20.9326 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.4
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756876896
|
akirafudo
| 2025-09-03T05:22:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:21:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chainway9/blockassist-bc-untamed_quick_eel_1756875315
|
chainway9
| 2025-09-03T05:21:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:21:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hssnjfry/blockassist-bc-climbing_pouncing_dragonfly_1756876773
|
hssnjfry
| 2025-09-03T05:21:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"climbing pouncing dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:20:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- climbing pouncing dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
0xadityam/llama-aica
|
0xadityam
| 2025-09-03T05:20:59Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-03T05:20:59Z |
---
license: apache-2.0
---
|
bah63843/blockassist-bc-plump_fast_antelope_1756876788
|
bah63843
| 2025-09-03T05:20:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:20:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arif696/blockassist-bc-regal_spotted_pelican_1756876732
|
arif696
| 2025-09-03T05:20:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:19:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
baidu/ERNIE-4.5-300B-A47B-2Bits-TP2-Paddle
|
baidu
| 2025-09-03T05:20:25Z | 46 | 8 | null |
[
"safetensors",
"ernie4_5_moe",
"ERNIE4.5",
"text-generation",
"conversational",
"en",
"zh",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-07-08T11:20:03Z |
---
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-generation
tags:
- ERNIE4.5
---
<div align="center" style="line-height: 1;">
<a href="https://ernie.baidu.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/๐ค_Chat-ERNIE_Bot-blue" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/baidu" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Baidu-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/PaddlePaddle/ERNIE" target="_blank" style="margin: 2px;">
<img alt="Github" src="https://img.shields.io/badge/GitHub-ERNIE-000?logo=github&color=0000FF" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://ernie.baidu.com/blog/ernie4.5" target="_blank" style="margin: 2px;">
<img alt="Blog" src="https://img.shields.io/badge/๐_Blog-ERNIE4.5-A020A0" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://discord.gg/JPmZXDsEEK" target="_blank" style="margin: 2px;">
<img alt="Discord" src="https://img.shields.io/badge/Discord-ERNIE-5865F2?logo=discord&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://x.com/PaddlePaddle" target="_blank" style="margin: 2px;">
<img alt="X" src="https://img.shields.io/badge/X-PaddlePaddle-6080F0"?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="#license" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-Apache2.0-A5de54" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
# ERNIE-4.5-300B-A47B
## ERNIE 4.5 Highlights
The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations:
1. **Multimodal Heterogeneous MoE Pre-Training:** Our models are jointly trained on both textual and visual modalities to better capture the nuances of multimodal information and improve performance on tasks involving text understanding and generation, image understanding, and cross-modal reasoning. To achieve this without one modality hindering the learning of another, we designed a *heterogeneous MoE structure*, incorporated *modality-isolated routing*, and employed *router orthogonal loss* and *multimodal token-balanced loss*. These architectural choices ensure that both modalities are effectively represented, allowing for mutual reinforcement during training.
2. **Scaling-Efficient Infrastructure:** We propose a novel heterogeneous hybrid parallelism and hierarchical load balancing strategy for efficient training of ERNIE 4.5 models. By using intra-node expert parallelism, memory-efficient pipeline scheduling, FP8 mixed-precision training and finegrained recomputation methods, we achieve remarkable pre-training throughput. For inference, we propose *multi-expert parallel collaboration* method and *convolutional code quantization* algorithm to achieve 4-bit/2-bit lossless quantization. Furthermore, we introduce PD disaggregation with dynamic role switching for effective resource utilization to enhance inference performance for ERNIE 4.5 MoE models. Built on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle), ERNIE 4.5 delivers high-performance inference across a wide range of hardware platforms.
3. **Modality-Specific Post-Training:** To meet the diverse requirements of real-world applications, we fine-tuned variants of the pre-trained model for specific modalities. Our LLMs are optimized for general-purpose language understanding and generation. The VLMs focuses on visuallanguage understanding and supports both thinking and non-thinking modes. Each model employed a combination of *Supervised Fine-tuning (SFT)*, *Direct Preference Optimization (DPO)* or a modified reinforcement learning method named *Unified Preference Optimization (UPO)* for post-training.
## Model Overview
ERNIE-4.5-300B-A47B is a text MoE Post-trained model, with 300B total parameters and 47B activated parameters for each token. The following are the model configuration details:
|Key|Value|
|-|-|
|Modality|Text|
|Training Stage|Pretraining|
|Params(Total / Activated)|300B / 47B|
|Layers|54|
|Heads(Q/KV)|64 / 8|
|Text Experts(Total / Activated)|64 / 8|
|Vision Experts(Total / Activated)|64 / 8|
|Context Length|131072|
## Quickstart
### Using FastDeploy
Service deployment can be quickly completed using FastDeploy in the following command. For more detailed usage instructions, please refer to the [FastDeploy Repository](https://github.com/PaddlePaddle/FastDeploy).
**Note**: To deploy on a configuration with 4 GPUs each having at least 80G of memory, specify ```--quantization wint4```. If you specify ```--quantization wint8```, then resources for 8 GPUs are required.
```bash
python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-300B-A47B-Paddle \
--port 8180 \
--metrics-port 8181 \
--quantization wint4 \
--tensor-parallel-size 8 \
--engine-worker-queue-port 8182 \
--max-model-len 32768 \
--max-num-seqs 32
```
To deploy the W4A8C8 quantized version using FastDeploy, you can run the following command.
```bash
python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-300B-A47B-W4A8C8-TP4-Paddle \
--port 8180 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--tensor-parallel-size 4 \
--max-model-len 32768 \
--max-num-seqs 32
```
To deploy the WINT2 quantized version using FastDeploy on two 80G GPUs, run the following command.
```bash
python -m fastdeploy.entrypoints.openai.api_server \
--model "baidu/ERNIE-4.5-300B-A47B-2Bits-TP2-Paddle" \
--port 8180 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--tensor-parallel-size 2 \
--max-model-len 32768 \
--max-num-seqs 128
```
The following contains a code snippet illustrating how to use ERNIE-4.5-300B-A47B-FP8 generate content based on given inputs.
```python
from fastdeploy import LLM, SamplingParams
prompts = [
"Hello, my name is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=128)
model = "baidu/ERNIE-4.5-300B-A47B-FP8-Paddle"
llm = LLM(model=model, tensor_parallel_size=8, max_model_len=8192, num_gpu_blocks_override=1024, engine_worker_queue_port=9981)
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs.text
print("generated_text", generated_text)
```
## Best Practices
### **Sampling Parameters**
To achieve optimal performance, we suggest using `Temperature=0.8`, `TopP=0.8`.
### Prompts for Web Search
For Web Search, {references}, {date}, and {question} are arguments.
For Chinese question, we use the prompt:
```python
ernie_search_zh_prompt = \
'''ไธ้ขไฝ ไผๆถๅฐๅฝๅๆถ้ดใๅคไธชไธๅๆฅๆบ็ๅ่ๆ็ซ ๅไธๆฎตๅฏน่ฏใไฝ ็ไปปๅกๆฏ้
่ฏปๅคไธชๅ่ๆ็ซ ๏ผๅนถๆ นๆฎๅ่ๆ็ซ ไธญ็ไฟกๆฏๅ็ญๅฏน่ฏไธญ็้ฎ้ขใ
ไปฅไธๆฏๅฝๅๆถ้ดๅๅ่ๆ็ซ ๏ผ
---------
#ๅฝๅๆถ้ด
{date}
#ๅ่ๆ็ซ
{references}
---------
่ฏทๆณจๆ๏ผ
1. ๅ็ญๅฟ
้กป็ปๅ้ฎ้ข้ๆฑๅๅฝๅๆถ้ด๏ผๅฏนๅ่ๆ็ซ ็ๅฏ็จๆง่ฟ่กๅคๆญ๏ผ้ฟๅ
ๅจๅ็ญไธญไฝฟ็จ้่ฏฏๆ่ฟๆถ็ไฟกๆฏใ
2. ๅฝๅ่ๆ็ซ ไธญ็ไฟกๆฏๆ ๆณๅ็กฎๅฐๅ็ญ้ฎ้ขๆถ๏ผไฝ ้่ฆๅจๅ็ญไธญๆไพ่ทๅ็ธๅบไฟกๆฏ็ๅปบ่ฎฎ๏ผๆๆฟ่ฎคๆ ๆณๆไพ็ธๅบไฟกๆฏใ
3. ไฝ ้่ฆไผๅ
ๆ นๆฎ็พ็งใๅฎ็ฝใๆๅจๆบๆใไธไธ็ฝ็ซ็ญ้ซๆๅจๆงๆฅๆบ็ไฟกๆฏๆฅๅ็ญ้ฎ้ขใ
4. ๅๅค้่ฆ็ปผๅๅ่ๆ็ซ ไธญ็็ธๅ
ณๆฐๅญใๆกไพใๆณๅพๆกๆใๅ
ฌๅผ็ญไฟกๆฏ๏ผไฝฟไฝ ็็ญๆกๆดไธไธใ
5. ๅฝ้ฎ้ขๅฑไบๅไฝ็ฑปไปปๅกๆถ๏ผ้ๆณจๆไปฅไธ็ปดๅบฆ๏ผ
- ๆๅบฆ้ฒๆ๏ผ่ง็นใ็ซๅบๆธ
ๆฐๆ็กฎ๏ผ้ฟๅ
ๆจกๆฃฑไธคๅฏ๏ผ่ฏญ่จๆๆญ็ดๆฅ
- ๆ้้ฃๆฌ๏ผ็จ่ฏ็ฒพๅ็ๅจ๏ผๅ็จไฟฎ่พๆๆณ๏ผๅขๅผบๆๆๅ
- ๆ็ๆๆฎ๏ผ้ป่พไธฅๅฏ้่ฟ๏ผ็ปๅๆๅจๆฐๆฎ/ไบๅฎๆฏๆ่ฎบ็น
---------
ไธ้ข่ฏท็ปๅไปฅไธไฟกๆฏ๏ผๅ็ญ้ฎ้ข๏ผ่กฅๅ
จๅฏน่ฏ
{question}'''
```
For English question, we use the prompt:
```python
ernie_search_en_prompt = \
'''
Below you will be given the current time, multiple references from different sources, and a conversation. Your task is to read the references and use the information in them to answer the question in the conversation.
Here are the current time and the references:
---------
#Current Time
{date}
#References
{references}
---------
Please note:
1. Based on the questionโs requirements and the current time, assess the usefulness of the references to avoid using inaccurate or outdated information in the answer.
2. If the references do not provide enough information to accurately answer the question, you should suggest how to obtain the relevant information or acknowledge that you are unable to provide it.
3. Prioritize using information from highly authoritative sources such as encyclopedias, official websites, authoritative institutions, and professional websites when answering questions.
4. Incorporate relevant numbers, cases, legal provisions, formulas, and other details from the references to make your answer more professional.
5. For creative tasks, keep these dimensions in mind:
- Clear attitude: Clear views and positions, avoid ambiguity, and use decisive and direct language
- Brilliant writing: Precise and vivid words, good use of rhetoric, and enhance the appeal
- Well-reasoned: Rigorous logic and progressive, combined with authoritative data/facts to support the argument
---------
Now, using the information above, answer the question and complete the conversation:
{question}'''
```
Parameter notes:
* {question} is the userโs question
* {date} is the current time, and the recommended format is โYYYY-MM-DD HH:MM:SS, Day of the Week, Beijing/China.โ
* {references} is the references, and the recommended format is:
```text
##ๅ่ๆ็ซ 1
ๆ ้ข๏ผๅจๆฐไผฆ
ๆ็ซ ๅๅธๆถ้ด๏ผ2025-04-20
ๅ
ๅฎน๏ผๅจๆฐไผฆ(Jay Chou),1979ๅนด1ๆ18ๆฅๅบ็ไบๅฐๆนพ็ๆฐๅๅธ,็ฅ็ฑ็ฆๅปบ็ๆฐธๆฅๅฟ,ๅ่ฏญๆต่กไน็ทๆญๆใ้ณไนไบบใๆผๅใๅฏผๆผใ็ผๅง,ๆฏไธไบๆทกๆฑไธญๅญฆใ2000ๅนด,ๅ่กไธชไบบ้ฆๅผ ้ณไนไธ่พใJayใใ...
ๆฅๆบ็ฝ็ซ็ฝๅ๏ผbaike.baidu.com
ๆฅๆบ็ฝ็ซ็็ฝ็ซๅ๏ผ็พๅบฆ็พ็ง
##ๅ่ๆ็ซ 2
...
```
## License
The ERNIE 4.5 models are provided under the Apache License 2.0. This license permits commercial use, subject to its terms and conditions. Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
## Citation
If you find ERNIE 4.5 useful or wish to use it in your projects, please kindly cite our technical report:
```bibtex
@misc{ernie2025technicalreport,
title={ERNIE 4.5 Technical Report},
author={Baidu ERNIE Team},
year={2025},
eprint={},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={}
}
```
|
omerbektass/blockassist-bc-keen_fast_giraffe_1756876778
|
omerbektass
| 2025-09-03T05:20:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:19:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
david3621/blockassist-bc-gentle_meek_cat_1756875765
|
david3621
| 2025-09-03T05:19:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle meek cat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:18:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle meek cat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roshanVarghese/my-stable-diffusion
|
roshanVarghese
| 2025-09-03T05:19:19Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-09-03T05:12:54Z |
---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐งจ diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
2hpsatt/blockassist-bc-huge_deft_eagle_1756876657
|
2hpsatt
| 2025-09-03T05:18:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"huge deft eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:18:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- huge deft eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Jessie09/strategic_sft_Qwen3-14B
|
Jessie09
| 2025-09-03T05:18:26Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2025-08-30T15:41:13Z |
# Model Card for Model strategic_sft_Qwen3-14B_20250829_023948
## Model Details
### Model Description
* Developed by: Foresight-based Optimization Authors
* Backbone model: Qwen3-14B
* Training method: SFT with KL divergence
* Training data: train_imitation_gpt4.json
* Training task: WordTaboo
### Training Parameters
{
"output_dir": "/home/jiashuo/codes/ForesightOptim/checkpoints/strategic_sft_Qwen3-14B_20250829_023948",
"overwrite_output_dir": false,
"do_train": false,
"do_eval": false,
"do_predict": false,
"eval_strategy": {
"_value_": "no",
"_name_": "NO",
"__objclass__": "{'_generate_next_value_': <function Enum._generate_next_value_ at 0x71eb157c2b90>, '__module__': 'transformers.trainer_utils', '__doc__': 'An enumeration.', '_member_names_': ['NO', 'STEPS', 'EPOCH'], '_member_map_': {'NO': <IntervalStrategy.NO: 'no'>, 'STEPS': <IntervalStrategy.STEPS: 'steps'>, 'EPOCH': <IntervalStrategy.EPOCH: 'epoch'>}, '_member_type_': <class 'str'>, '_value2member_map_': {'no': <IntervalStrategy.NO: 'no'>, 'steps': <IntervalStrategy.STEPS: 'steps'>, 'epoch': <IntervalStrategy.EPOCH: 'epoch'>}, 'NO': <IntervalStrategy.NO: 'no'>, 'STEPS': <IntervalStrategy.STEPS: 'steps'>, 'EPOCH': <IntervalStrategy.EPOCH: 'epoch'>, '__new__': <function Enum.__new__ at 0x71eb157c2b00>}"
},
"prediction_loss_only": false,
"per_device_train_batch_size": 4,
"per_device_eval_batch_size": 8,
"per_gpu_train_batch_size": null,
"per_gpu_eval_batch_size": null,
"gradient_accumulation_steps": 8,
"eval_accumulation_steps": null,
"eval_delay": 0,
"torch_empty_cache_steps": null,
"learning_rate": 3e-05,
"weight_decay": 0.0,
"adam_beta1": 0.9,
"adam_beta2": 0.999,
"adam_epsilon": 1e-08,
"max_grad_norm": 1.0,
"num_train_epochs": 1.0,
"max_steps": -1,
"lr_scheduler_type": {
"_value_": "cosine",
"_name_": "COSINE",
"__objclass__": "{'_generate_next_value_': <function Enum._generate_next_value_ at 0x71eb157c2b90>, '__module__': 'transformers.trainer_utils', '__doc__': '\\n Scheduler names for the parameter `lr_scheduler_type` in [`TrainingArguments`].\\n By default, it uses \"linear\". Internally, this retrieves `get_linear_schedule_with_warmup` scheduler from [`Trainer`].\\n Scheduler types:\\n - \"linear\" = get_linear_schedule_with_warmup\\n - \"cosine\" = get_cosine_schedule_with_warmup\\n - \"cosine_with_restarts\" = get_cosine_with_hard_restarts_schedule_with_warmup\\n - \"polynomial\" = get_polynomial_decay_schedule_with_warmup\\n - \"constant\" = get_constant_schedule\\n - \"constant_with_warmup\" = get_constant_schedule_with_warmup\\n - \"inverse_sqrt\" = get_inverse_sqrt_schedule\\n - \"reduce_lr_on_plateau\" = get_reduce_on_plateau_schedule\\n - \"cosine_with_min_lr\" = get_cosine_with_min_lr_schedule_with_warmup\\n - \"warmup_stable_decay\" = get_wsd_schedule\\n ', '_member_names_': ['LINEAR', 'COSINE', 'COSINE_WITH_RESTARTS', 'POLYNOMIAL', 'CONSTANT', 'CONSTANT_WITH_WARMUP', 'INVERSE_SQRT', 'REDUCE_ON_PLATEAU', 'COSINE_WITH_MIN_LR', 'WARMUP_STABLE_DECAY'], '_member_map_': {'LINEAR': <SchedulerType.LINEAR: 'linear'>, 'COSINE': <SchedulerType.COSINE: 'cosine'>, 'COSINE_WITH_RESTARTS': <SchedulerType.COSINE_WITH_RESTARTS: 'cosine_with_restarts'>, 'POLYNOMIAL': <SchedulerType.POLYNOMIAL: 'polynomial'>, 'CONSTANT': <SchedulerType.CONSTANT: 'constant'>, 'CONSTANT_WITH_WARMUP': <SchedulerType.CONSTANT_WITH_WARMUP: 'constant_with_warmup'>, 'INVERSE_SQRT': <SchedulerType.INVERSE_SQRT: 'inverse_sqrt'>, 'REDUCE_ON_PLATEAU': <SchedulerType.REDUCE_ON_PLATEAU: 'reduce_lr_on_plateau'>, 'COSINE_WITH_MIN_LR': <SchedulerType.COSINE_WITH_MIN_LR: 'cosine_with_min_lr'>, 'WARMUP_STABLE_DECAY': <SchedulerType.WARMUP_STABLE_DECAY: 'warmup_stable_decay'>}, '_member_type_': <class 'str'>, '_value2member_map_': {'linear': <SchedulerType.LINEAR: 'linear'>, 'cosine': <SchedulerType.COSINE: 'cosine'>, 'cosine_with_restarts': <SchedulerType.COSINE_WITH_RESTARTS: 'cosine_with_restarts'>, 'polynomial': <SchedulerType.POLYNOMIAL: 'polynomial'>, 'constant': <SchedulerType.CONSTANT: 'constant'>, 'constant_with_warmup': <SchedulerType.CONSTANT_WITH_WARMUP: 'constant_with_warmup'>, 'inverse_sqrt': <SchedulerType.INVERSE_SQRT: 'inverse_sqrt'>, 'reduce_lr_on_plateau': <SchedulerType.REDUCE_ON_PLATEAU: 'reduce_lr_on_plateau'>, 'cosine_with_min_lr': <SchedulerType.COSINE_WITH_MIN_LR: 'cosine_with_min_lr'>, 'warmup_stable_decay': <SchedulerType.WARMUP_STABLE_DECAY: 'warmup_stable_decay'>}, 'LINEAR': <SchedulerType.LINEAR: 'linear'>, 'COSINE': <SchedulerType.COSINE: 'cosine'>, 'COSINE_WITH_RESTARTS': <SchedulerType.COSINE_WITH_RESTARTS: 'cosine_with_restarts'>, 'POLYNOMIAL': <SchedulerType.POLYNOMIAL: 'polynomial'>, 'CONSTANT': <SchedulerType.CONSTANT: 'constant'>, 'CONSTANT_WITH_WARMUP': <SchedulerType.CONSTANT_WITH_WARMUP: 'constant_with_warmup'>, 'INVERSE_SQRT': <SchedulerType.INVERSE_SQRT: 'inverse_sqrt'>, 'REDUCE_ON_PLATEAU': <SchedulerType.REDUCE_ON_PLATEAU: 'reduce_lr_on_plateau'>, 'COSINE_WITH_MIN_LR': <SchedulerType.COSINE_WITH_MIN_LR: 'cosine_with_min_lr'>, 'WARMUP_STABLE_DECAY': <SchedulerType.WARMUP_STABLE_DECAY: 'warmup_stable_decay'>, '__new__': <function Enum.__new__ at 0x71eb157c2b00>}"
},
"lr_scheduler_kwargs": {},
"warmup_ratio": 0.03,
"warmup_steps": 0,
"log_level": "passive",
"log_level_replica": "warning",
"log_on_each_node": true,
"logging_dir": "/home/jiashuo/codes/ForesightOptim/checkpoints/strategic_sft_Qwen3-14B_20250829_023948/runs/Aug29_02-39-58_super-Rack-Server",
"logging_strategy": {
"_value_": "steps",
"_name_": "STEPS",
"__objclass__": "{'_generate_next_value_': <function Enum._generate_next_value_ at 0x71eb157c2b90>, '__module__': 'transformers.trainer_utils', '__doc__': 'An enumeration.', '_member_names_': ['NO', 'STEPS', 'EPOCH'], '_member_map_': {'NO': <IntervalStrategy.NO: 'no'>, 'STEPS': <IntervalStrategy.STEPS: 'steps'>, 'EPOCH': <IntervalStrategy.EPOCH: 'epoch'>}, '_member_type_': <class 'str'>, '_value2member_map_': {'no': <IntervalStrategy.NO: 'no'>, 'steps': <IntervalStrategy.STEPS: 'steps'>, 'epoch': <IntervalStrategy.EPOCH: 'epoch'>}, 'NO': <IntervalStrategy.NO: 'no'>, 'STEPS': <IntervalStrategy.STEPS: 'steps'>, 'EPOCH': <IntervalStrategy.EPOCH: 'epoch'>, '__new__': <function Enum.__new__ at 0x71eb157c2b00>}"
},
"logging_first_step": false,
"logging_steps": 1.0,
"logging_nan_inf_filter": true,
"save_strategy": {
"_value_": "steps",
"_name_": "STEPS",
"__objclass__": "{'_generate_next_value_': <function Enum._generate_next_value_ at 0x71eb157c2b90>, '__module__': 'transformers.trainer_utils', '__doc__': 'An enumeration.', '_member_names_': ['NO', 'STEPS', 'EPOCH', 'BEST'], '_member_map_': {'NO': <SaveStrategy.NO: 'no'>, 'STEPS': <SaveStrategy.STEPS: 'steps'>, 'EPOCH': <SaveStrategy.EPOCH: 'epoch'>, 'BEST': <SaveStrategy.BEST: 'best'>}, '_member_type_': <class 'str'>, '_value2member_map_': {'no': <SaveStrategy.NO: 'no'>, 'steps': <SaveStrategy.STEPS: 'steps'>, 'epoch': <SaveStrategy.EPOCH: 'epoch'>, 'best': <SaveStrategy.BEST: 'best'>}, 'NO': <SaveStrategy.NO: 'no'>, 'STEPS': <SaveStrategy.STEPS: 'steps'>, 'EPOCH': <SaveStrategy.EPOCH: 'epoch'>, 'BEST': <SaveStrategy.BEST: 'best'>, '__new__': <function Enum.__new__ at 0x71eb157c2b00>}"
},
"save_steps": 400,
"save_total_limit": null,
"save_safetensors": true,
"save_on_each_node": false,
"save_only_model": false,
"restore_callback_states_from_checkpoint": false,
"no_cuda": false,
"use_cpu": false,
"use_mps_device": false,
"seed": 42,
"data_seed": null,
"jit_mode_eval": false,
"use_ipex": false,
"bf16": true,
"fp16": false,
"fp16_opt_level": "O1",
"half_precision_backend": "auto",
"bf16_full_eval": false,
"fp16_full_eval": false,
"tf32": true,
"local_rank": 1,
"ddp_backend": null,
"tpu_num_cores": null,
"tpu_metrics_debug": false,
"debug": [],
"dataloader_drop_last": false,
"eval_steps": null,
"dataloader_num_workers": 0,
"dataloader_prefetch_factor": null,
"past_index": -1,
"run_name": "/home/jiashuo/codes/ForesightOptim/checkpoints/strategic_sft_Qwen3-14B_20250829_023948",
"disable_tqdm": false,
"remove_unused_columns": false,
"label_names": null,
"load_best_model_at_end": false,
"metric_for_best_model": null,
"greater_is_better": null,
"ignore_data_skip": false,
"fsdp": [],
"fsdp_min_num_params": 0,
"fsdp_config": {
"min_num_params": 0,
"xla": false,
"xla_fsdp_v2": false,
"xla_fsdp_grad_ckpt": false
},
"fsdp_transformer_layer_cls_to_wrap": null,
"accelerator_config": {
"split_batches": false,
"dispatch_batches": null,
"even_batches": true,
"use_seedable_sampler": true,
"non_blocking": false,
"gradient_accumulation_kwargs": null
},
"deepspeed": null,
"label_smoothing_factor": 0.0,
"optim": {
"_value_": "adamw_torch",
"_name_": "ADAMW_TORCH",
"__objclass__": "{'_generate_next_value_': <function Enum._generate_next_value_ at 0x71eb157c2b90>, '__module__': 'transformers.training_args', '__doc__': '\\n Stores the acceptable string identifiers for optimizers.\\n ', '_member_names_': ['ADAMW_TORCH', 'ADAMW_TORCH_FUSED', 'ADAMW_TORCH_XLA', 'ADAMW_TORCH_NPU_FUSED', 'ADAMW_APEX_FUSED', 'ADAFACTOR', 'ADAMW_ANYPRECISION', 'ADAMW_TORCH_4BIT', 'ADAMW_TORCH_8BIT', 'ADEMAMIX', 'SGD', 'ADAGRAD', 'ADAMW_BNB', 'ADAMW_8BIT', 'ADEMAMIX_8BIT', 'LION_8BIT', 'LION', 'PAGED_ADAMW', 'PAGED_ADAMW_8BIT', 'PAGED_ADEMAMIX', 'PAGED_ADEMAMIX_8BIT', 'PAGED_LION', 'PAGED_LION_8BIT', 'RMSPROP', 'RMSPROP_BNB', 'RMSPROP_8BIT', 'RMSPROP_32BIT', 'GALORE_ADAMW', 'GALORE_ADAMW_8BIT', 'GALORE_ADAFACTOR', 'GALORE_ADAMW_LAYERWISE', 'GALORE_ADAMW_8BIT_LAYERWISE', 'GALORE_ADAFACTOR_LAYERWISE', 'LOMO', 'ADALOMO', 'GROKADAMW', 'SCHEDULE_FREE_RADAM', 'SCHEDULE_FREE_ADAMW', 'SCHEDULE_FREE_SGD', 'APOLLO_ADAMW', 'APOLLO_ADAMW_LAYERWISE'], '_member_map_': {'ADAMW_TORCH': <OptimizerNames.ADAMW_TORCH: 'adamw_torch'>, 'ADAMW_TORCH_FUSED': <OptimizerNames.ADAMW_TORCH_FUSED: 'adamw_torch_fused'>, 'ADAMW_TORCH_XLA': <OptimizerNames.ADAMW_TORCH_XLA: 'adamw_torch_xla'>, 'ADAMW_TORCH_NPU_FUSED': <OptimizerNames.ADAMW_TORCH_NPU_FUSED: 'adamw_torch_npu_fused'>, 'ADAMW_APEX_FUSED': <OptimizerNames.ADAMW_APEX_FUSED: 'adamw_apex_fused'>, 'ADAFACTOR': <OptimizerNames.ADAFACTOR: 'adafactor'>, 'ADAMW_ANYPRECISION': <OptimizerNames.ADAMW_ANYPRECISION: 'adamw_anyprecision'>, 'ADAMW_TORCH_4BIT': <OptimizerNames.ADAMW_TORCH_4BIT: 'adamw_torch_4bit'>, 'ADAMW_TORCH_8BIT': <OptimizerNames.ADAMW_TORCH_8BIT: 'adamw_torch_8bit'>, 'ADEMAMIX': <OptimizerNames.ADEMAMIX: 'ademamix'>, 'SGD': <OptimizerNames.SGD: 'sgd'>, 'ADAGRAD': <OptimizerNames.ADAGRAD: 'adagrad'>, 'ADAMW_BNB': <OptimizerNames.ADAMW_BNB: 'adamw_bnb_8bit'>, 'ADAMW_8BIT': <OptimizerNames.ADAMW_8BIT: 'adamw_8bit'>, 'ADEMAMIX_8BIT': <OptimizerNames.ADEMAMIX_8BIT: 'ademamix_8bit'>, 'LION_8BIT': 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<OptimizerNames.ADAMW_TORCH_XLA: 'adamw_torch_xla'>, 'ADAMW_TORCH_NPU_FUSED': <OptimizerNames.ADAMW_TORCH_NPU_FUSED: 'adamw_torch_npu_fused'>, 'ADAMW_APEX_FUSED': <OptimizerNames.ADAMW_APEX_FUSED: 'adamw_apex_fused'>, 'ADAFACTOR': <OptimizerNames.ADAFACTOR: 'adafactor'>, 'ADAMW_ANYPRECISION': <OptimizerNames.ADAMW_ANYPRECISION: 'adamw_anyprecision'>, 'ADAMW_TORCH_4BIT': <OptimizerNames.ADAMW_TORCH_4BIT: 'adamw_torch_4bit'>, 'ADAMW_TORCH_8BIT': <OptimizerNames.ADAMW_TORCH_8BIT: 'adamw_torch_8bit'>, 'ADEMAMIX': <OptimizerNames.ADEMAMIX: 'ademamix'>, 'SGD': <OptimizerNames.SGD: 'sgd'>, 'ADAGRAD': <OptimizerNames.ADAGRAD: 'adagrad'>, 'ADAMW_BNB': <OptimizerNames.ADAMW_BNB: 'adamw_bnb_8bit'>, 'ADAMW_8BIT': <OptimizerNames.ADAMW_8BIT: 'adamw_8bit'>, 'ADEMAMIX_8BIT': <OptimizerNames.ADEMAMIX_8BIT: 'ademamix_8bit'>, 'LION_8BIT': <OptimizerNames.LION_8BIT: 'lion_8bit'>, 'LION': <OptimizerNames.LION: 'lion_32bit'>, 'PAGED_ADAMW': <OptimizerNames.PAGED_ADAMW: 'paged_adamw_32bit'>, 'PAGED_ADAMW_8BIT': <OptimizerNames.PAGED_ADAMW_8BIT: 'paged_adamw_8bit'>, 'PAGED_ADEMAMIX': <OptimizerNames.PAGED_ADEMAMIX: 'paged_ademamix_32bit'>, 'PAGED_ADEMAMIX_8BIT': <OptimizerNames.PAGED_ADEMAMIX_8BIT: 'paged_ademamix_8bit'>, 'PAGED_LION': <OptimizerNames.PAGED_LION: 'paged_lion_32bit'>, 'PAGED_LION_8BIT': <OptimizerNames.PAGED_LION_8BIT: 'paged_lion_8bit'>, 'RMSPROP': <OptimizerNames.RMSPROP: 'rmsprop'>, 'RMSPROP_BNB': <OptimizerNames.RMSPROP_BNB: 'rmsprop_bnb'>, 'RMSPROP_8BIT': <OptimizerNames.RMSPROP_8BIT: 'rmsprop_bnb_8bit'>, 'RMSPROP_32BIT': <OptimizerNames.RMSPROP_32BIT: 'rmsprop_bnb_32bit'>, 'GALORE_ADAMW': <OptimizerNames.GALORE_ADAMW: 'galore_adamw'>, 'GALORE_ADAMW_8BIT': <OptimizerNames.GALORE_ADAMW_8BIT: 'galore_adamw_8bit'>, 'GALORE_ADAFACTOR': <OptimizerNames.GALORE_ADAFACTOR: 'galore_adafactor'>, 'GALORE_ADAMW_LAYERWISE': <OptimizerNames.GALORE_ADAMW_LAYERWISE: 'galore_adamw_layerwise'>, 'GALORE_ADAMW_8BIT_LAYERWISE': <OptimizerNames.GALORE_ADAMW_8BIT_LAYERWISE: 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},
"optim_args": null,
"adafactor": false,
"group_by_length": false,
"length_column_name": "length",
"report_to": [
"tensorboard",
"wandb"
],
"ddp_find_unused_parameters": null,
"ddp_bucket_cap_mb": null,
"ddp_broadcast_buffers": null,
"dataloader_pin_memory": true,
"dataloader_persistent_workers": false,
"skip_memory_metrics": true,
"use_legacy_prediction_loop": false,
"push_to_hub": false,
"resume_from_checkpoint": null,
"hub_model_id": null,
"hub_strategy": {
"_value_": "every_save",
"_name_": "EVERY_SAVE",
"__objclass__": "{'_generate_next_value_': <function Enum._generate_next_value_ at 0x71eb157c2b90>, '__module__': 'transformers.trainer_utils', '__doc__': 'An enumeration.', '_member_names_': ['END', 'EVERY_SAVE', 'CHECKPOINT', 'ALL_CHECKPOINTS'], '_member_map_': {'END': <HubStrategy.END: 'end'>, 'EVERY_SAVE': <HubStrategy.EVERY_SAVE: 'every_save'>, 'CHECKPOINT': <HubStrategy.CHECKPOINT: 'checkpoint'>, 'ALL_CHECKPOINTS': <HubStrategy.ALL_CHECKPOINTS: 'all_checkpoints'>}, '_member_type_': <class 'str'>, '_value2member_map_': {'end': <HubStrategy.END: 'end'>, 'every_save': <HubStrategy.EVERY_SAVE: 'every_save'>, 'checkpoint': <HubStrategy.CHECKPOINT: 'checkpoint'>, 'all_checkpoints': <HubStrategy.ALL_CHECKPOINTS: 'all_checkpoints'>}, 'END': <HubStrategy.END: 'end'>, 'EVERY_SAVE': <HubStrategy.EVERY_SAVE: 'every_save'>, 'CHECKPOINT': <HubStrategy.CHECKPOINT: 'checkpoint'>, 'ALL_CHECKPOINTS': <HubStrategy.ALL_CHECKPOINTS: 'all_checkpoints'>, '__new__': <function Enum.__new__ at 0x71eb157c2b00>}"
},
"hub_token": null,
"hub_private_repo": null,
"hub_always_push": false,
"hub_revision": null,
"gradient_checkpointing": true,
"gradient_checkpointing_kwargs": null,
"include_inputs_for_metrics": false,
"include_for_metrics": [],
"eval_do_concat_batches": true,
"fp16_backend": "auto",
"push_to_hub_model_id": null,
"push_to_hub_organization": null,
"push_to_hub_token": null,
"mp_parameters": "",
"auto_find_batch_size": false,
"full_determinism": false,
"torchdynamo": null,
"ray_scope": "last",
"ddp_timeout": 1800,
"torch_compile": false,
"torch_compile_backend": null,
"torch_compile_mode": null,
"include_tokens_per_second": false,
"include_num_input_tokens_seen": false,
"neftune_noise_alpha": null,
"optim_target_modules": null,
"batch_eval_metrics": false,
"eval_on_start": false,
"use_liger_kernel": false,
"liger_kernel_config": null,
"eval_use_gather_object": false,
"average_tokens_across_devices": false,
"use_wandb": false,
"adapter_path": "",
"padding_side": "right",
"truncation_side": "left",
"add_sep_token": false,
"model_type": "llama",
"model_prefix": "llama",
"pooling_type": "average",
"model_name_or_path": "/data/models/Qwen3-14B",
"ref_model_name_or_path": "",
"critic_model_name_or_path": "FacebookAI/roberta-base",
"game_name": "WordTaboo",
"game_max_turn": 6,
"data_dir": "path/to/cleaned_data",
"data_type": "no_type",
"data_path": "yahma/alpaca-cleaned",
"train_data_path": [
"/home/jiashuo/datasets/wordtaboo/train_imitation_gpt4.json",
"/home/jiashuo/datasets/wordtaboo/train_imitation_gpt4.json",
"/home/jiashuo/datasets/rsagame/train_imitation_gpt4.1.json"
],
"eval_data_path": [],
"data_prefix": "yahma/alpaca-cleaned",
"data_suffix": "yahma/alpaca-cleaned",
"task_type": "training",
"train_method": "SFTWeightedWithKL",
"use_lora": true,
"debug_mode": false,
"cache_dir": null,
"clip_range": 0.2,
"length_penalty": 1.0,
"lm_sft_coeff": 0.0,
"lm_kl_coeff": 0.0,
"max_length": 2048,
"valid_data_size": 0,
"rollout_size": 128,
"replay_buffer_size": 10000,
"replay_batch_size": 16,
"critic_learning_rate": 2e-05,
"gamma": 0.99,
"tau": 0.95,
"max_new_tokens": 128,
"temperature": 0.9,
"top_p": 0.95,
"player_one_model_name_or_path": "",
"player_two_model_name_or_path": "",
"distributed_state": {
"_cpu": false,
"backend": "nccl",
"device": "cuda:1",
"debug": false,
"distributed_type": "DEEPSPEED",
"num_processes": 4,
"process_index": 1,
"local_process_index": 1,
"fork_launched": false
},
"_n_gpu": 1,
"__cached__setup_devices": "cuda:1",
"deepspeed_plugin": {
"hf_ds_config": {
"config": {
"train_batch_size": 128,
"train_micro_batch_size_per_gpu": 4,
"gradient_accumulation_steps": 8,
"zero_optimization": {
"stage": 2,
"offload_optimizer": {
"device": "none",
"nvme_path": null
},
"offload_param": {
"device": "none",
"nvme_path": null
},
"stage3_gather_16bit_weights_on_model_save": true
},
"gradient_clipping": 1.0,
"steps_per_print": Infinity,
"bf16": {
"enabled": true
},
"fp16": {
"enabled": false
},
"zero_allow_untested_optimizer": true
},
"_stage": 2,
"_offload": false,
"_dtype": "torch.bfloat16",
"mismatches": []
},
"gradient_accumulation_steps": "auto",
"gradient_clipping": 1.0,
"zero_stage": 2,
"is_train_batch_min": true,
"offload_optimizer_device": "none",
"offload_param_device": "none",
"offload_optimizer_nvme_path": "none",
"offload_param_nvme_path": "none",
"zero3_init_flag": false,
"zero3_save_16bit_model": true,
"transformer_moe_cls_names": null,
"enable_msamp": false,
"msamp_opt_level": "O1",
"deepspeed_config": {
"train_batch_size": 128,
"train_micro_batch_size_per_gpu": 4,
"gradient_accumulation_steps": 8,
"zero_optimization": {
"stage": 2,
"offload_optimizer": {
"device": "none",
"nvme_path": null
},
"offload_param": {
"device": "none",
"nvme_path": null
},
"stage3_gather_16bit_weights_on_model_save": true
},
"gradient_clipping": 1.0,
"steps_per_print": Infinity,
"bf16": {
"enabled": true
},
"fp16": {
"enabled": false
},
"zero_allow_untested_optimizer": true
},
"_selected": true,
"dschf": {
"config": {
"train_micro_batch_size_per_gpu": 1,
"gradient_accumulation_steps": 1,
"zero_optimization": {
"stage": 2,
"offload_optimizer": {
"device": "none",
"nvme_path": null
},
"offload_param": {
"device": "none",
"nvme_path": null
},
"stage3_gather_16bit_weights_on_model_save": true
},
"gradient_clipping": 1.0,
"steps_per_print": Infinity,
"bf16": {
"enabled": true
},
"fp16": {
"enabled": false
}
},
"_stage": 2,
"_offload": false
}
}
}
### Hardware Requirements
* GPU: 8 48G NVIDIA-SMI 5090
* Number of GPUs: 8
* Memory of each GPU: 48G
|
DevQuasar/NousResearch.Hermes-4-14B-GGUF
|
DevQuasar
| 2025-09-03T05:17:03Z | 0 | 0 | null |
[
"gguf",
"text-generation",
"base_model:NousResearch/Hermes-4-14B",
"base_model:quantized:NousResearch/Hermes-4-14B",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-09-03T03:18:49Z |
---
base_model:
- NousResearch/Hermes-4-14B
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [NousResearch/Hermes-4-14B](https://huggingface.co/NousResearch/Hermes-4-14B)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
GroomerG/blockassist-bc-vicious_pawing_badger_1756874956
|
GroomerG
| 2025-09-03T05:16:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vicious pawing badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:16:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vicious pawing badger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arif696/blockassist-bc-regal_spotted_pelican_1756876478
|
arif696
| 2025-09-03T05:16:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:15:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756876534
|
bah63843
| 2025-09-03T05:16:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:16:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756876515
|
akirafudo
| 2025-09-03T05:16:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:15:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hssnjfry/blockassist-bc-climbing_pouncing_dragonfly_1756876420
|
hssnjfry
| 2025-09-03T05:15:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"climbing pouncing dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:14:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- climbing pouncing dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-pawing_downy_anaconda_1756876553
|
AnerYubo
| 2025-09-03T05:15:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pawing downy anaconda",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:15:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pawing downy anaconda
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-snappy_tenacious_eagle_1756876540
|
AnerYubo
| 2025-09-03T05:15:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"snappy tenacious eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:15:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- snappy tenacious eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-fanged_camouflaged_cassowary_1756876534
|
AnerYubo
| 2025-09-03T05:15:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fanged camouflaged cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:15:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fanged camouflaged cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1756874846
|
calegpedia
| 2025-09-03T05:15:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:15:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy slimy rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sekirr/blockassist-bc-masked_tenacious_whale_1756876456
|
sekirr
| 2025-09-03T05:14:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:14:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hobbor/ppo-LunarLander-v2
|
hobbor
| 2025-09-03T05:13:56Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-03T03:43:03Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 236.46 +/- 45.14
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
systbs/zarvan-checkpoints
|
systbs
| 2025-09-03T05:13:17Z | 655 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-22T05:56:32Z |
---
license: apache-2.0
---
|
openbmb/MiniCPM-V-4_5
|
openbmb
| 2025-09-03T05:12:43Z | 13,870 | 837 |
transformers
|
[
"transformers",
"safetensors",
"minicpmv",
"feature-extraction",
"minicpm-v",
"vision",
"ocr",
"document parsing",
"multi-image",
"video",
"custom_code",
"image-text-to-text",
"conversational",
"multilingual",
"dataset:openbmb/RLAIF-V-Dataset",
"arxiv:2403.11703",
"region:us"
] |
image-text-to-text
| 2025-08-24T10:39:55Z |
---
pipeline_tag: image-text-to-text
datasets:
- openbmb/RLAIF-V-Dataset
library_name: transformers
language:
- multilingual
tags:
- minicpm-v
- vision
- ocr
- document parsing
- multi-image
- video
- custom_code
---
<h1>A GPT-4o Level MLLM for Single Image, Multi Image and High-FPS Video Understanding on Your Phone</h1>
[GitHub](https://github.com/OpenBMB/MiniCPM-o) | [CookBook](https://github.com/OpenSQZ/MiniCPM-V-CookBook) | [Demo](http://101.126.42.235:30910/)</a>
## MiniCPM-V 4.5
**MiniCPM-V 4.5** is the latest and most capable model in the MiniCPM-V series. The model is built on Qwen3-8B and SigLIP2-400M with a total of 8B parameters. It exhibits a significant performance improvement over previous MiniCPM-V and MiniCPM-o models, and introduces new useful features. Notable features of MiniCPM-V 4.5 include:
- ๐ฅ **State-of-the-art Vision-Language Capability.**
MiniCPM-V 4.5 achieves an average score of 77.0 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. **With only 8B parameters, it surpasses widely used proprietary models like GPT-4o-latest, Gemini-2.0 Pro, and strong open-source models like Qwen2.5-VL 72B** for vision-language capabilities, making it the most performant MLLM under 30B parameters.
- ๐ฌ **Efficient High-FPS and Long Video Understanding.** Powered by a new unified 3D-Resampler over images and videos, MiniCPM-V 4.5 can now achieve 96x compression rate for video tokens, where 6 448x448 video frames can be jointly compressed into 64 video tokens (normally 1,536 tokens for most MLLMs). This means that the model can perceive significantly more video frames without increasing the LLM inference cost. This brings state-of-the-art high-FPS (up to 10FPS) video understanding and long video understanding capabilities on Video-MME, LVBench, MLVU, MotionBench, FavorBench, etc., efficiently.
- โ๏ธ **Controllable Hybrid Fast/Deep Thinking.** MiniCPM-V 4.5 supports both fast thinking for efficient frequent usage with competitive performance, and deep thinking for more complex problem solving. To cover efficiency and performance trade-offs in different user scenarios, this fast/deep thinking mode can be switched in a highly controlled fashion.
- ๐ช **Strong OCR, Document Parsing and Others.**
Based on [LLaVA-UHD](https://arxiv.org/pdf/2403.11703) architecture, MiniCPM-V 4.5 can process high-resolution images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344), using 4x less visual tokens than most MLLMs. The model achieves **leading performance on OCRBench, surpassing proprietary models such as GPT-4o-latest and Gemini 2.5**. It also achieves state-of-the-art performance for PDF document parsing capability on OmniDocBench among general MLLMs. Based on the latest [RLAIF-V](https://github.com/RLHF-V/RLAIF-V/) and [VisCPM](https://github.com/OpenBMB/VisCPM) techniques, it features **trustworthy behaviors**, outperforming GPT-4o-latest on MMHal-Bench, and supports **multilingual capabilities** in more than 30 languages.
- ๐ซ **Easy Usage.**
MiniCPM-V 4.5 can be easily used in various ways: (1) [llama.cpp](https://github.com/tc-mb/llama.cpp/blob/Support-MiniCPM-V-4.5/docs/multimodal/minicpmv4.5.md) and [ollama](https://github.com/tc-mb/ollama/tree/MIniCPM-V) support for efficient CPU inference on local devices, (2) [int4](https://huggingface.co/openbmb/MiniCPM-V-4_5-int4), [GGUF](https://huggingface.co/openbmb/MiniCPM-V-4_5-gguf) and [AWQ](https://github.com/tc-mb/AutoAWQ) format quantized models in 16 sizes, (3) [SGLang](https://github.com/tc-mb/sglang/tree/main) and [vLLM](#efficient-inference-with-llamacpp-ollama-vllm) support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks with [Transformers](https://github.com/tc-mb/transformers/tree/main) and [LLaMA-Factory](./docs/llamafactory_train_and_infer.md), (5) quick [local WebUI demo](#chat-with-our-demo-on-gradio), (6) optimized [local iOS app](https://github.com/tc-mb/MiniCPM-o-demo-iOS) on iPhone and iPad, and (7) online web demo on [server](http://101.126.42.235:30910/). See our [Cookbook](https://github.com/OpenSQZ/MiniCPM-V-CookBook) for full usages!
### Key Techniques
<div align="center">
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpm-v-4dot5-framework.png" , width=100%>
</div>
- **Architechture: Unified 3D-Resampler for High-density Video Compression.** MiniCPM-V 4.5 introduces a 3D-Resampler that overcomes the performance-efficiency trade-off in video understanding. By grouping and jointly compressing up to 6 consecutive video frames into just 64 tokens (the same token count used for a single image in MiniCPM-V series), MiniCPM-V 4.5 achieves a 96ร compression rate for video tokens. This allows the model to process more video frames without additional LLM computational cost, enabling high-FPS video and long video understanding. The architecture supports unified encoding for images, multi-image inputs, and videos, ensuring seamless capability and knowledge transfer.
- **Pre-training: Unified Learning for OCR and Knowledge from Documents.** Existing MLLMs learn OCR capability and knowledge from documents in isolated training approaches. We observe that the essential difference between these two training approaches is the visibility of the text in images. By dynamically corrupting text regions in documents with varying noise levels and asking the model to reconstruct the text, the model learns to adaptively and properly switch between accurate text recognition (when text is visible) and multimodal context-based knowledge reasoning (when text is heavily obscured). This eliminates reliance on error-prone document parsers in knowledge learning from documents, and prevents hallucinations from over-augmented OCR data, resulting in top-tier OCR and multimodal knowledge performance with minimal engineering overhead.
- **Post-training: Hybrid Fast/Deep Thinking with Multimodal RL.** MiniCPM-V 4.5 offers a balanced reasoning experience through two switchable modes: fast thinking for efficient daily use and deep thinking for complex tasks. Using a new hybrid reinforcement learning method, the model jointly optimizes both modes, significantly enhancing fast-mode performance without compromising deep-mode capability. Incorporated with [RLPR](https://github.com/OpenBMB/RLPR) and [RLAIF-V](https://github.com/RLHF-V/RLAIF-V), it generalizes robust reasoning skills from broad multimodal data while effectively reducing hallucinations.
### Evaluation
<div align="center">
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/radar_minicpm_v45.png", width=60%>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv_4_5_evaluation_result.png" , width=100%>
</div>
### Inference Efficiency
**OpenCompass**
<div align="left">
<table style="margin: 0px auto;">
<thead>
<tr>
<th align="left">Model</th>
<th>Size</th>
<th>Avg Score โ</th>
<th>Total Inference Time โ</th>
</tr>
</thead>
<tbody align="center">
<tr>
<td nowrap="nowrap" align="left">GLM-4.1V-9B-Thinking</td>
<td>10.3B</td>
<td>76.6</td>
<td>17.5h</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">MiMo-VL-7B-RL</td>
<td>8.3B</td>
<td>76.4</td>
<td>11h</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">MiniCPM-V 4.5</td>
<td>8.7B</td>
<td><b>77.0</td>
<td><b>7.5h</td>
</tr>
</tbody>
</table>
</div>
**Video-MME**
<div align="left">
<table style="margin: 0px auto;">
<thead>
<tr>
<th align="left">Model</th>
<th>Size</th>
<th>Avg Score โ</th>
<th>Total Inference Time โ</th>
<th>GPU Mem โ</th>
</tr>
</thead>
<tbody align="center">
<tr>
<td nowrap="nowrap" align="left">Qwen2.5-VL-7B-Instruct</td>
<td>8.3B</td>
<td>71.6</td>
<td>3h</td>
<td>60G</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">GLM-4.1V-9B-Thinking</td>
<td>10.3B</td>
<td><b>73.6</td>
<td>2.63h</td>
<td>32G</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">MiniCPM-V 4.5</td>
<td>8.7B</td>
<td>73.5</td>
<td><b>0.26h</td>
<td><b>28G</td>
</tr>
</tbody>
</table>
</div>
Both Video-MME and OpenCompass were evaluated using 8รA100 GPUs for inference. The reported inference time of Video-MME includes full model-side computation, and excludes the external cost of video frame extraction (dependent on specific frame extraction tools) for fair comparison.
### Examples
<div align="center">
<a href="https://www.youtube.com/watch?v=Cn23FujYMMU"><img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/MiniCPM-V%204.5-8.26_img.jpeg", width=70%></a>
</div>
<div style="display: flex; flex-direction: column; align-items: center;">
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/en_case1.png" alt="en_case1" style="margin-bottom: 5px;">
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/en_case2.png" alt="en_case2" style="margin-bottom: 5px;">
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/en_case3.jpeg" alt="en_case3" style="margin-bottom: 5px;">
</div>
We deploy MiniCPM-V 4.5 on iPad M4 with [iOS demo](https://github.com/tc-mb/MiniCPM-o-demo-iOS). The demo video is the raw screen recording without editing.
<div align="center">
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/v45_en_handwriting.gif" width="45%" style="display: inline-block; margin: 0 10px;"/>
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/v45_en_cot.gif" width="45%" style="display: inline-block; margin: 0 10px;"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/v45_cn_handwriting.gif" width="45%" style="display: inline-block; margin: 0 10px;"/>
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/v45_cn_travel.gif" width="45%" style="display: inline-block; margin: 0 10px;"/>
</div>
## Framework Support Matrix
<table>
<thead>
<tr>
<th>Category</th>
<th>Framework</th>
<th>Cookbook Link</th>
<th>Upstream PR</th>
<th>Supported since (branch)</th>
<th>Supported since (release)</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="2">Edge (On-device)</td>
<td>Llama.cpp</td>
<td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/llama.cpp/minicpm-v4_5_llamacpp.md">Llama.cpp Doc</a></td>
<td><a href="https://github.com/ggml-org/llama.cpp/pull/15575">#15575</a> (2025-08-26)</td>
<td>master (2025-08-26)</td>
<td><a href="https://github.com/ggml-org/llama.cpp/releases/tag/b6282">b6282</a></td>
</tr>
<tr>
<td>Ollama</td>
<td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/ollama/minicpm-v4_5_ollama.md">Ollama Doc</a></td>
<td><a href="https://github.com/ollama/ollama/pull/12078">#12078</a> (2025-08-26)</td>
<td>Merging</td>
<td>Waiting for official release</td>
</tr>
<tr>
<td rowspan="2">Serving (Cloud)</td>
<td>vLLM</td>
<td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/vllm/minicpm-v4_5_vllm.md">vLLM Doc</a></td>
<td><a href="https://github.com/vllm-project/vllm/pull/23586">#23586</a> (2025-08-26)</td>
<td>main (2025-08-27)</td>
<td>Waiting for official release</td>
</tr>
<tr>
<td>SGLang</td>
<td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/sglang/MiniCPM-v4_5_sglang.md">SGLang Doc</a></td>
<td><a href="https://github.com/sgl-project/sglang/pull/9610">#9610</a> (2025-08-26)</td>
<td>Merging</td>
<td>Waiting for official release</td>
</tr>
<tr>
<td>Finetuning</td>
<td>LLaMA-Factory</td>
<td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/finetune/finetune_llamafactory.md">LLaMA-Factory Doc</a></td>
<td><a href="https://github.com/hiyouga/LLaMA-Factory/pull/9022">#9022</a> (2025-08-26)</td>
<td>main (2025-08-26)</td>
<td>Waiting for official release</td>
</tr>
<tr>
<td rowspan="3">Quantization</td>
<td>GGUF</td>
<td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/quantization/gguf/minicpm-v4_5_gguf_quantize.md">GGUF Doc</a></td>
<td>โ</td>
<td>โ</td>
<td>โ</td>
</tr>
<tr>
<td>BNB</td>
<td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/quantization/bnb/minicpm-v4_5_bnb_quantize.md">BNB Doc</a></td>
<td>โ</td>
<td>โ</td>
<td>โ</td>
</tr>
<tr>
<td>AWQ</td>
<td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/quantization/awq/minicpm-v4_5_awq_quantize.md">AWQ Doc</a></td>
<td>โ</td>
<td>โ</td>
<td>โ</td>
</tr>
<tr>
<td>Demos</td>
<td>Gradio Demo</td>
<td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/demo/web_demo/gradio/README.md">Gradio Demo Doc</a></td>
<td>โ</td>
<td>โ</td>
<td>โ</td>
</tr>
</tbody>
</table>
> Note: If you'd like us to prioritize support for another open-source framework, please let us know via this [short form](https://docs.google.com/forms/d/e/1FAIpQLSdyTUrOPBgWqPexs3ORrg47ZcZ1r4vFQaA4ve2iA7L9sMfMWw/viewform).
## Usage
If you wish to enable thinking mode, provide the argument `enable_thinking=True` to the chat function.
#### Chat with Image
```python
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer
torch.manual_seed(100)
model = AutoModel.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True, # or openbmb/MiniCPM-o-2_6
attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
model = model.eval().cuda()
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True) # or openbmb/MiniCPM-o-2_6
image = Image.open('./assets/minicpmo2_6/show_demo.jpg').convert('RGB')
enable_thinking=False # If `enable_thinking=True`, the thinking mode is enabled.
stream=True # If `stream=True`, the answer is string
# First round chat
question = "What is the landform in the picture?"
msgs = [{'role': 'user', 'content': [image, question]}]
answer = model.chat(
msgs=msgs,
tokenizer=tokenizer,
enable_thinking=enable_thinking,
stream=True
)
generated_text = ""
for new_text in answer:
generated_text += new_text
print(new_text, flush=True, end='')
# Second round chat, pass history context of multi-turn conversation
msgs.append({"role": "assistant", "content": [answer]})
msgs.append({"role": "user", "content": ["What should I pay attention to when traveling here?"]})
answer = model.chat(
msgs=msgs,
tokenizer=tokenizer,
stream=True
)
generated_text = ""
for new_text in answer:
generated_text += new_text
print(new_text, flush=True, end='')
```
You will get the following output:
```shell
# round1
The landform in the picture is karst topography. Karst landscapes are characterized by distinctive, jagged limestone hills or mountains with steep, irregular peaks and deep valleysโexactly what you see here These unique formations result from the dissolution of soluble rocks like limestone over millions of years through water erosion.
This scene closely resembles the famous karst landscape of Guilin and Yangshuo in Chinaโs Guangxi Province. The area features dramatic, pointed limestone peaks rising dramatically above serene rivers and lush green forests, creating a breathtaking and iconic natural beauty that attracts millions of visitors each year for its picturesque views.
# round2
When traveling to a karst landscape like this, here are some important tips:
1. Wear comfortable shoes: The terrain can be uneven and hilly.
2. Bring water and snacks for energy during hikes or boat rides.
3. Protect yourself from the sun with sunscreen, hats, and sunglassesโespecially since youโll likely spend time outdoors exploring scenic spots.
4. Respect local customs and nature regulations by not littering or disturbing wildlife.
By following these guidelines, you'll have a safe and enjoyable trip while appreciating the stunning natural beauty of places such as Guilinโs karst mountains.
```
#### Chat with Video
```python
## The 3d-resampler compresses multiple frames into 64 tokens by introducing temporal_ids.
# To achieve this, you need to organize your video data into two corresponding sequences:
# frames: List[Image]
# temporal_ids: List[List[Int]].
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer
from decord import VideoReader, cpu # pip install decord
from scipy.spatial import cKDTree
import numpy as np
import math
model = AutoModel.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True, # or openbmb/MiniCPM-o-2_6
attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
model = model.eval().cuda()
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True) # or openbmb/MiniCPM-o-2_6
MAX_NUM_FRAMES=180 # Indicates the maximum number of frames received after the videos are packed. The actual maximum number of valid frames is MAX_NUM_FRAMES * MAX_NUM_PACKING.
MAX_NUM_PACKING=3 # indicates the maximum packing number of video frames. valid range: 1-6
TIME_SCALE = 0.1
def map_to_nearest_scale(values, scale):
tree = cKDTree(np.asarray(scale)[:, None])
_, indices = tree.query(np.asarray(values)[:, None])
return np.asarray(scale)[indices]
def group_array(arr, size):
return [arr[i:i+size] for i in range(0, len(arr), size)]
def encode_video(video_path, choose_fps=3, force_packing=None):
def uniform_sample(l, n):
gap = len(l) / n
idxs = [int(i * gap + gap / 2) for i in range(n)]
return [l[i] for i in idxs]
vr = VideoReader(video_path, ctx=cpu(0))
fps = vr.get_avg_fps()
video_duration = len(vr) / fps
if choose_fps * int(video_duration) <= MAX_NUM_FRAMES:
packing_nums = 1
choose_frames = round(min(choose_fps, round(fps)) * min(MAX_NUM_FRAMES, video_duration))
else:
packing_nums = math.ceil(video_duration * choose_fps / MAX_NUM_FRAMES)
if packing_nums <= MAX_NUM_PACKING:
choose_frames = round(video_duration * choose_fps)
else:
choose_frames = round(MAX_NUM_FRAMES * MAX_NUM_PACKING)
packing_nums = MAX_NUM_PACKING
frame_idx = [i for i in range(0, len(vr))]
frame_idx = np.array(uniform_sample(frame_idx, choose_frames))
if force_packing:
packing_nums = min(force_packing, MAX_NUM_PACKING)
print(video_path, ' duration:', video_duration)
print(f'get video frames={len(frame_idx)}, packing_nums={packing_nums}')
frames = vr.get_batch(frame_idx).asnumpy()
frame_idx_ts = frame_idx / fps
scale = np.arange(0, video_duration, TIME_SCALE)
frame_ts_id = map_to_nearest_scale(frame_idx_ts, scale) / TIME_SCALE
frame_ts_id = frame_ts_id.astype(np.int32)
assert len(frames) == len(frame_ts_id)
frames = [Image.fromarray(v.astype('uint8')).convert('RGB') for v in frames]
frame_ts_id_group = group_array(frame_ts_id, packing_nums)
return frames, frame_ts_id_group
video_path="video_test.mp4"
fps = 5 # fps for video
force_packing = None # You can set force_packing to ensure that 3D-Resampler packing is forcibly enabled; otherwise, encode_video will dynamically set the packing quantity based on the duration.
frames, frame_ts_id_group = encode_video(video_path, fps, force_packing=force_packing)
question = "Describe the video"
msgs = [
{'role': 'user', 'content': frames + [question]},
]
answer = model.chat(
msgs=msgs,
tokenizer=tokenizer,
use_image_id=False, # ensure use_image_id=False when video inference
max_slice_nums=1,
temporal_ids=frame_ts_id_group
)
print(answer)
```
#### Chat with multiple images
<details>
<summary> Click to show Python code running MiniCPM-V 4.5 with multiple images input. </summary>
```python
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True,
attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2
model = model.eval().cuda()
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True)
image1 = Image.open('image1.jpg').convert('RGB')
image2 = Image.open('image2.jpg').convert('RGB')
question = 'Compare image 1 and image 2, tell me about the differences between image 1 and image 2.'
msgs = [{'role': 'user', 'content': [image1, image2, question]}]
answer = model.chat(
msgs=msgs,
tokenizer=tokenizer
)
print(answer)
```
</details>
#### In-context few-shot learning
<details>
<summary> Click to view Python code running MiniCPM-V 4.5 with few-shot input. </summary>
```python
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True,
attn_implementation='sdpa', torch_dtype=torch.bfloat16)
model = model.eval().cuda()
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True)
question = "production date"
image1 = Image.open('example1.jpg').convert('RGB')
answer1 = "2023.08.04"
image2 = Image.open('example2.jpg').convert('RGB')
answer2 = "2007.04.24"
image_test = Image.open('test.jpg').convert('RGB')
msgs = [
{'role': 'user', 'content': [image1, question]}, {'role': 'assistant', 'content': [answer1]},
{'role': 'user', 'content': [image2, question]}, {'role': 'assistant', 'content': [answer2]},
{'role': 'user', 'content': [image_test, question]}
]
answer = model.chat(
msgs=msgs,
tokenizer=tokenizer
)
print(answer)
```
</details>
## License
#### Model License
* The code in this repo is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License.
* The usage of MiniCPM-V series model weights must strictly follow [MiniCPM Model License.md](https://github.com/OpenBMB/MiniCPM-o/blob/main/MiniCPM%20Model%20License.md).
* The models and weights of MiniCPM are completely free for academic research. After filling out a ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, MiniCPM-V 4.5 weights are also available for free commercial use.
#### Statement
* As an LMM, MiniCPM-V 4.5 generates contents by learning a large amount of multimodal corpora, but it cannot comprehend, express personal opinions or make value judgement. Anything generated by MiniCPM-V 4.5 does not represent the views and positions of the model developers
* We will not be liable for any problems arising from the use of the MinCPM-V models, including but not limited to data security issues, risk of public opinion, or any risks and problems arising from the misdirection, misuse, dissemination or misuse of the model.
## Key Techniques and Other Multimodal Projects
๐ Welcome to explore key techniques of MiniCPM-V 4.5 and other multimodal projects of our team:
[VisCPM](https://github.com/OpenBMB/VisCPM/tree/main) | [RLPR](https://github.com/OpenBMB/RLPR) | [RLHF-V](https://github.com/RLHF-V/RLHF-V) | [LLaVA-UHD](https://github.com/thunlp/LLaVA-UHD) | [RLAIF-V](https://github.com/RLHF-V/RLAIF-V)
## Citation
If you find our work helpful, please consider citing our papers ๐ and liking this project โค๏ธ๏ผ
```bib
@article{yao2024minicpm,
title={MiniCPM-V: A GPT-4V Level MLLM on Your Phone},
author={Yao, Yuan and Yu, Tianyu and Zhang, Ao and Wang, Chongyi and Cui, Junbo and Zhu, Hongji and Cai, Tianchi and Li, Haoyu and Zhao, Weilin and He, Zhihui and others},
journal={Nat Commun 16, 5509 (2025)},
year={2025}
}
```
|
Halfotter/korean-xlm-roberta-classifier
|
Halfotter
| 2025-09-03T05:12:40Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"korean",
"ko",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-03T03:51:30Z |
---
language: ko
library_name: transformers
pipeline_tag: text-classification
base_model: xlm-roberta-base
tags:
- xlm-roberta
- text-classification
- korean
license: mit
---
# Korean XLM-RoBERTa Classifier (HF Compatible)
This model is converted from a custom classifier to be compatible with Hugging Face Inference API.
## Model Info
- Base Model: xlm-roberta-base
- Task: text-classification
- Language: Korean
- Labels: 66
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load directly from Hugging Face Hub
tokenizer = AutoTokenizer.from_pretrained("Halftotter/korean-xlm-roberta-classifier")
model = AutoModelForSequenceClassification.from_pretrained("Halftotter/korean-xlm-roberta-classifier")
# Predict
inputs = tokenizer("์๋ณธ ํฌ์
๋ฌผ๋ช
", return_tensors="pt")
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
print(predictions)
|
arif696/blockassist-bc-regal_spotted_pelican_1756876263
|
arif696
| 2025-09-03T05:12:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:12:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roshanVarghese/my-gpt2-model
|
roshanVarghese
| 2025-09-03T05:12:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-03T05:10:26Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Chandan683/Qwen2.5-14b-GA1
|
Chandan683
| 2025-09-03T05:11:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-03T05:10:32Z |
---
base_model: unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Chandan683
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
bah63843/blockassist-bc-plump_fast_antelope_1756876198
|
bah63843
| 2025-09-03T05:10:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:10:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dsaddsdsdd/blockassist-bc-stinging_darting_anteater_1756874872
|
dsaddsdsdd
| 2025-09-03T05:09:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinging darting anteater",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:09:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinging darting anteater
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
OneclickAI/LSTM_GUE_test_Model
|
OneclickAI
| 2025-09-03T05:09:38Z | 0 | 0 |
keras
|
[
"keras",
"license:apache-2.0",
"region:us"
] | null | 2025-09-03T04:03:12Z |
---
license: apache-2.0
---
์๋
ํ์ธ์ Oneclick AI ์
๋๋ค!!
์ค๋์, RNN์ ํ๊ณ๋ฅผ ๊ทน๋ณตํ LSTM(Long Short-Term Memory)๊ณผ GRU(Gated Recurrent Unit) ๋ชจ๋ธ์ ๋ํด์ ์์๋ณด๋ ์๊ฐ์ ๊ฐ์ ธ๋ณผ๊น ํฉ๋๋ค.
RNN์ด ์์ฐจ ๋ฐ์ดํฐ๋ฅผ ๋ค๋ฃจ๋ ๋ฐ ํ์ ์ ๊ฐ์ ธ์์ง๋ง, ๊ธด ์ํ์ค์์ ๊ณผ๊ฑฐ ์ ๋ณด๋ฅผ ์ ๋๋ก ๊ธฐ์ตํ์ง ๋ชปํ๋ '์ฅ๊ธฐ ์์กด์ฑ ๋ฌธ์ '๋ก ์ธํด ํ๊ณ๋ฅผ ๋๋ฌ๋์ต๋๋ค.
LSTM๊ณผ GRU๋ ์ด ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํด ๊ณ ์๋ ๊ณ ๊ธ ์ํ ์ ๊ฒฝ๋ง์ผ๋ก, ๋ง์น ์ฌ๋์ ์ฅ๊ธฐ ๊ธฐ์ต์ฒ๋ผ ์ค์ํ ์ ๋ณด๋ฅผ ์ ํ์ ์ผ๋ก ์ ์งํ๊ณ ์์ด๋ฒ๋ฆด ์ ์๋ '๊ฒ์ดํธ' ๋ฉ์ปค๋์ฆ์ ๋์
ํ์ต๋๋ค.
์ค๋์ ์ด ๋ ๋ชจ๋ธ์ด ์ด๋ป๊ฒ RNN์ ์ฝ์ ์ ๋ณด์ํ๋ฉฐ ์๋ํ๋์ง, ๊ทธ๋ฆฌ๊ณ ์ด๋ป๊ฒ ๋ ๋ณต์กํ ๋ฌธ์ฅ์ด๋ ์๊ณ์ด ๋ฐ์ดํฐ๋ฅผ ์ ๊ตํ๊ฒ ์ฒ๋ฆฌํ ์ ์๋์ง ์์๋ด
์๋ค.
---
## ๋ชฉ์ฐจ
1. LSTM/GRU ํต์ฌ ์๋ฆฌ ํ์
ํ๊ธฐ
- ์ LSTM/GRU๋ฅผ ์ฌ์ฉํด์ผ๋ง ํ ๊น?
- LSTM์ ์ฌ์ฅ : ์
์ํ์ 3๊ฐ์ ๊ฒ์ดํธ ๋ฉ์ปค๋์ฆ
- GRU : LSTM์ ๊ฐ์ํ๋ ๋ฒ์ ๊ณผ 2๊ฐ์ ๊ฒ์ดํธ
- LSTM๊ณผ GRU๋ฅผ ์๊ฐ์ ๋ฐ๋ผ ํผ์ณ๋ณด๊ธฐ
- LSTM/GRU์ ์ฃผ์ ๊ตฌ์ฑ ์์ ์์ธ ๋ถ์
2. ์ํคํ
์ฒ๋ฅผ ํตํ ๋ด๋ถ ์ฝ๋ ๋ค์ฌ๋ค ๋ณด๊ธฐ
- Keras๋ก ๊ตฌํํ LSTM/GRU ๋ชจ๋ธ ์ํคํ
์ฒ
- model.summary()๋ก ๊ตฌ์กฐ ํ์ธํ๊ธฐ
3. ์ง์ LSTM/GRU ๊ตฌํํด ๋ณด๊ธฐ
- 1๋จ๊ณ : ๋ฐ์ดํฐ ๋ก๋ ๋ฐ ์ ์ฒ๋ฆฌ
- 2๋จ๊ณ : ๋ชจ๋ธ ์ปดํ์ผ
- 3๋จ๊ณ : ๋ชจ๋ธ ํ์ต ๋ฐ ํ๊ฐ
- 4๋จ๊ณ : ํ์ต๋ ๋ชจ๋ธ ์ ์ฅ ๋ฐ ์ฌ์ฌ์ฉ
- 5๋จ๊ณ : ๋๋ง์ ๋ฌธ์ฅ์ผ๋ก ๋ชจ๋ธ ํ
์คํธํ๊ธฐ
4. ๋๋ง์ LSTM/GRU ๋ชจ๋ธ ์
๊ทธ๋ ์ด๋ํ๊ธฐ
- ๊ธฐ์ด ์ฒด๋ ฅ ํ๋ จ : ํ์ดํผํ๋ผ๋ฏธํฐ ํ๋
- ์ธต ์๊ธฐ : ๋ค์ค LSTM/GRU ๋ ์ด์ด
- ๊ณผ๊ฑฐ์ ๋ฏธ๋๋ฅผ ๋์์ : ์๋ฐฉํฅ LSTM/GRU
- ์ ์ดํ์ต์ผ๋ก ์ฑ๋ฅ ๊ทน๋ํ ํ๊ธฐ
5. ๊ฒฐ๋ก
---
## 1. LSTM/GRU ํต์ฌ์๋ฆฌ ํ์
ํ๊ธฐ
๊ฐ์ฅ ๋จผ์ , LSTM๊ณผ GRU๊ฐ ์ RNN์ ๋์์ผ๋ก ๋ฑ์ฅํ๋์ง ๊ทธ ๊ทผ๋ณธ์ ์ธ ์ด์ ๋ถํฐ ์ดํด๋ณด๊ฒ ์ต๋๋ค.
**์ LSTM/GRU๋ฅผ ์ฌ์ฉํ ๊น?? with RNN์ ํ๊ณ**
๊ธฐ๋ณธ RNN์ ์๋ ์ํ๋ฅผ ํตํด ๊ณผ๊ฑฐ ์ ๋ณด๋ฅผ ์ ๋ฌํ์ง๋ง, ์ํ์ค๊ฐ ๊ธธ์ด์ง๋ฉด ๊ทธ๋๋์ธํธ ์์ค(Vanishing Gradient)์ด๋ ํญ๋ฐ(Exploding Gradient) ๋ฌธ์ ๊ฐ ๋ฐ์ํฉ๋๋ค.
์ด๋ ํ์ต ๊ณผ์ ์์ ๊ธฐ์ธ๊ธฐ๊ฐ 0์ ๊ฐ๊น์์ง๊ฑฐ๋ ๋ฌดํ๋๊ฐ ๋์ด, ๋ฌธ์ฅ ์๋ถ๋ถ์ ์ค์ํ ์ ๋ณด๋ฅผ ์์ด๋ฒ๋ฆฌ๋ '์ฅ๊ธฐ ์์กด์ฑ ๋ฌธ์ (Long-Term Dependency)'๋ฅผ ์ด๋ํฉ๋๋ค.
์๋ฅผ ๋ค์ด, "์ด๋ฆฐ ์์ ํ๋์ค์์ ์๋๊ธฐ ๋๋ฌธ์... (๊ธด ๋ด์ฉ)... ๊ทธ๋์ ๋๋ ํ๋์ค์ด๋ฅผ ์ ์ฐฝํ๊ฒ ๊ตฌ์ฌํ๋ค."๋ผ๋ ๋ฌธ์ฅ์์ RNN์ 'ํ๋์ค'๋ผ๋ ์ด๊ธฐ ์ ๋ณด๋ฅผ ์๊ธฐ ์ฝ์ต๋๋ค.
LSTM๊ณผ GRU๋ ์ด ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํด '๊ฒ์ดํธ'๋ผ๋ ๊ตฌ์กฐ๋ฅผ ๋์
ํ์ฌ, ์ ๋ณด์ ํ๋ฆ์ ์ ์ดํฉ๋๋ค.
์ด๋ค์ RNN์ ๊ธฐ๋ณธ ๊ตฌ์กฐ๋ฅผ ์ ์งํ๋ฉด์๋ ์ค์ํ ์ ๋ณด๋ฅผ ์ ํ์ ์ผ๋ก ๊ธฐ์ตํ๊ณ ๋ถํ์ํ ๊ฒ์ ์์ด๋ฒ๋ฆด ์ ์๋๋ก ์ค๊ณ๋์์ต๋๋ค.
**LSTM์ ์ฌ์ฅ : ์
์ํ์ 3๊ฐ์ ๊ฒ์ดํธ ๋ฉ์ปค๋์ฆ**
LSTM์ ํต์ฌ์ '์
์ํ(Cell State, $C_t$)'์ ์ด๋ฅผ ์ ์ดํ๋ 3๊ฐ์ ๊ฒ์ดํธ์
๋๋ค.
- ์
์ํ(Cell State, $C_t$): ์ฅ๊ธฐ ๊ธฐ์ต์ ์ํ '์ปจ๋ฒ ์ด์ด ๋ฒจํธ'๋ก, ์ ๋ณด๊ฐ ๊ฑฐ์ ๋ณํ ์์ด ์ ๋ฌ๋ฉ๋๋ค.
- ๊ฒ์ดํธ(Gates): ์๊ทธ๋ชจ์ด๋(Sigmoid) ํจ์๋ฅผ ์ฌ์ฉํด 0~1 ์ฌ์ด์ ๊ฐ์ ์ถ๋ ฅํ๋ฉฐ, ์ ๋ณด์ ํต๊ณผ ์ฌ๋ถ๋ฅผ ๊ฒฐ์ ํฉ๋๋ค.
1. ๋ง๊ฐ ๊ฒ์ดํธ(Forget Gate, $f_t$): ์ด์ ์
์ํ $C_{t-1}$์์ ์ด๋ค ์ ๋ณด๋ฅผ ์์์ง ๊ฒฐ์ ํฉ๋๋ค.
$f_t = \sigma(W_f \cdot [h_{t-1}, x_t] + b_f)$
(์ฌ๊ธฐ์ $\sigma$๋ ์๊ทธ๋ชจ์ด๋ ํจ์, $h_{t-1}$์ ์ด์ ์๋ ์ํ, $x_t$๋ ํ์ฌ ์
๋ ฅ)
2. ์
๋ ฅ ๊ฒ์ดํธ(Input Gate, $i_t$)์ ํ๋ณด ์
์ํ($\tilde{C_t}$): ์๋ก์ด ์ ๋ณด๋ฅผ ์ผ๋ง๋ ์ถ๊ฐํ ์ง ๊ฒฐ์ ํฉ๋๋ค.
$i_t = \sigma(W_i \cdot [h_{t-1}, x_t] + b_i)$
$\tilde{C_t} = \tanh(W_C \cdot [h_{t-1}, x_t] + b_C)$
3. ์ถ๋ ฅ ๊ฒ์ดํธ(Output Gate, $o_t$): ์
์ํ์์ ์ด๋ค ์ ๋ณด๋ฅผ ์ถ๋ ฅํ ์ง ๊ฒฐ์ ํฉ๋๋ค.
$o_t = \sigma(W_o \cdot [h_{t-1}, x_t] + b_o)$
์ต์ข
์
์ํ $C_t = f_t \odot C_{t-1} + i_t \odot \tilde{C_t}$ ( $\odot$์ ์์๋ณ ๊ณฑ)
์๋ ์ํ $h_t = o_t \odot \tanh(C_t)$
์ด ๊ตฌ์กฐ ๋๋ถ์ LSTM์ ์ฅ๊ธฐ์ ์ธ ์์กด์ฑ์ ํจ๊ณผ์ ์ผ๋ก ํ์ตํฉ๋๋ค.
**GRU : LSTM์ ๊ฐ์ํ๋ ๋ฒ์ ๊ณผ 2๊ฐ์ ๊ฒ์ดํธ**
GRU๋ LSTM์ ๋ณํ์ผ๋ก, ํ๋ผ๋ฏธํฐ๋ฅผ ์ค์ฌ ๊ณ์ฐ ํจ์จ์ฑ์ ๋์์ต๋๋ค.
์๋ ์ํ $h_t$๊ฐ ์
์ํ ์ญํ ์ ๊ฒธํ๋ฉฐ, 2๊ฐ์ ๊ฒ์ดํธ๋ง ์ฌ์ฉํฉ๋๋ค.
- ๋ฆฌ์
๊ฒ์ดํธ(Reset Gate, $r_t$): ์ด์ ์๋ ์ํ๋ฅผ ์ผ๋ง๋ ๋ฌด์ํ ์ง ๊ฒฐ์ ํฉ๋๋ค.
$r_t = \sigma(W_r \cdot [h_{t-1}, x_t] + b_r)$
- ์
๋ฐ์ดํธ ๊ฒ์ดํธ(Update Gate, $z_t$): ์ด์ ์ํ์ ์ ํ๋ณด ์ํ๋ฅผ ์ผ๋ง๋ ์์์ง ๊ฒฐ์ ํฉ๋๋ค. (LSTM์ ๋ง๊ฐ+์
๋ ฅ ๊ฒ์ดํธ ์ญํ )
$z_t = \sigma(W_z \cdot [h_{t-1}, x_t] + b_z)$
ํ๋ณด ์๋ ์ํ $\tilde{h_t} = \tanh(W_h \cdot [r_t \odot h_{t-1}, x_t] + b_h)$
์ต์ข
$h_t = (1 - z_t) \odot h_{t-1} + z_t \odot \tilde{h_t}$
GRU๋ LSTM๋งํผ ๊ฐ๋ ฅํ๋ฉด์๋ ํ์ต์ด ๋ ๋น ๋ฆ
๋๋ค.
**LSTM/GRU๋ฅผ ์๊ฐ์ ๋ฐ๋ผ ํผ์ณ๋ณด๊ธฐ**
์๋ ๊ทธ๋ฆผ์ฒ๋ผ ์๊ฐ์ ๋ฐ๋ผ ๋คํธ์ํฌ๋ฅผ ๊ธธ๊ฒ ํผ์ณ์ ํํํ๋ฉด, ์ฝ๊ฒ ์ดํดํ ์ ์์ต๋๋ค.
```markdown
์๊ฐ ํ๋ฆ โโโโถ
์
๋ ฅ ์ํ์ค: xโ xโ xโ ... xโ
โ โ โ โ
โโโโโโ โโโโโโ โโโโโโ ... โโโโโโ
hโ, Cโ โโโถโLSTMโโถโLSTMโโถโLSTMโ โถ ... โถโLSTMโ (๋๋ GRU)
โโโโโโ โโโโโโ โโโโโโ โโโโโโ
โ โ โ โ
โผ โผ โผ โผ
hโ hโ hโ hโ
```
๊ฐ ํ์์คํ
์์ ๊ฒ์ดํธ๊ฐ ์ ๋ณด๋ฅผ ์ ์ดํ๋ฉฐ, ์
์ํ(๋๋ ์๋ ์ํ)๊ฐ ์ฅ๊ธฐ์ ์ผ๋ก ์ ๋ฌ๋ฉ๋๋ค.
**LSTM/GRU์ ์ฃผ์ ๊ตฌ์ฑ ์์**
- ๊ฒ์ดํธ ๋ฉ์ปค๋์ฆ: ์ ๋ณด ์ ํ๊ณผ ์ญ์ .
- ์๋/์
์ํ: ๋ฉ๋ชจ๋ฆฌ ์ญํ .
- ํ๋ผ๋ฏธํฐ ๊ณต์ : ๋ชจ๋ ํ์์คํ
์์ ๋์ผํ ๊ฐ์ค์น ์ฌ์ฉ.
---
## 2. ์ํคํ
์ฒ๋ฅผ ํตํ ๋ด๋ถ ์ฝ๋ ๋ค์ฌ๋ค ๋ณด๊ธฐ
์ด์ ์ด๋ก ์ ๋ฐํ์ผ๋ก, TensorFlow Keras ๋ฅผ ํตํด ์ง์ LSTM๊ณผ GRU๋ฅผ ๊ตฌํํด ๋ด
์๋ค.
Keras๋ก ๊ตฌํํ LSTM/GRU ๋ชจ๋ธ ์ํคํ
์ฒ ์ฌ์ธต ๋ถ์๋ค์์ IMDB ์ํ ๋ฆฌ๋ทฐ ๊ฐ์ฑ ๋ถ์์ ์ํ ๊ฐ๋จํ LSTM ๋ชจ๋ธ์
๋๋ค. (GRU๋ ์ ์ฌ)
```python
import tensorflow as tf
from tensorflow import keras
# ๋ชจ๋ธ ์ํคํ
์ฒ ์ ์
model = keras.Sequential([
# 1. ๋จ์ด ์๋ฒ ๋ฉ ์ธต
keras.layers.Embedding(input_dim=10000, output_dim=32),
# 2. LSTM ์ธต (GRU๋ก ๋ฐ๊พธ๋ ค๋ฉด SimpleRNN ๋์ LSTM ๋๋ GRU ์ฌ์ฉ)
keras.layers.LSTM(32),
# 3. ์ต์ข
๋ถ๋ฅ๊ธฐ
keras.layers.Dense(1, activation="sigmoid"),
])
# ๋ชจ๋ธ ๊ตฌ์กฐ ์์ฝ ์ถ๋ ฅ
model.summary()
```
๋ ์ด์ด๋ฅผ ์์ธํ ๋ค์ด๋ค ๋ด
์๋ค.
- **์๋ฒ ๋ฉ ์ธต(Embedding)**
```python
keras.layers.Embedding(input_dim=10000, output_dim=32)
```
๋จ์ด๋ฅผ ๋ฒกํฐ๋ก ๋ณํ, RNN ๋ฌธ์์ ๋์ผ.
- **์ํ ๊ณ์ธต(LSTM ๋๋ GRU)**
```python
keras.layers.LSTM(32),
```
๋๋
```python
keras.layers.GRU(32),
```
๋ด๋ถ์ ์ผ๋ก ๊ฒ์ดํธ๋ฅผ ์ฒ๋ฆฌํ๋ฉฐ, ์ฅ๊ธฐ ์์กด์ฑ์ ํ์ต. ๊ธฐ๋ณธ์ ์ผ๋ก ์ต์ข
์๋ ์ํ๋ง ์ถ๋ ฅ.
- **์์ ์ฐ๊ฒฐ ๊ณ์ธต(Dense)**
```python
keras.layers.Dense(1, activation="sigmoid")
```
์ต์ข
ํ๋จ.
model.summary()๋ก ํ๋ผ๋ฏธํฐ ์ ๊ณ์ฐ ์๋ฆฌ ์ดํดํ๊ธฐ์ ์ฝ๋์์ model.summary()๋ฅผ ์คํํ๋ฉด ๋ค์๊ณผ ๊ฐ์ ๊ฒฐ๊ณผ๊ฐ ๋์ต๋๋ค.
```bash
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, None, 32) 320000
lstm (LSTM) (None, 32) 8320
dense (Dense) (None, 1) 33
=================================================================
Total params: 328,353
Trainable params: 328,353
Non-trainable params: 0
_________________________________________________________________
```
๊ฐ ์ธต์ ํ๋ผ๋ฏธํฐ ์๋ ์ด๋ป๊ฒ ๊ณ์ฐ๋๋์ง ์์๋ณด์๋ฉด,
1. Embedding: 10,000 * 32 = 320,000 ๊ฐ.
2. LSTM: ์
๋ ฅ(32)๊ณผ ์๋(32)์ ๊ณ ๋ คํ 4๊ฐ์ ๊ฒ์ดํธ(์
๋ ฅ, ๋ง๊ฐ, ์ถ๋ ฅ, ํ๋ณด)๋ก, (32+32+1)*32*4 = 8,320 ๊ฐ. (GRU๋ 3๋ฐฐ: ์ฝ 6,240)
3. Dense: 32 * 1 + 1 = 33 ๊ฐ.
---
## 3. ์ง์ LSTM/GRU ๊ตฌํํด ๋ณด๊ธฐ
์ด์ , ์ ์ฒด ์ฝ๋๋ฅผ ๋จ๊ณ๋ณ๋ก ์คํํ๋ฉฐ ์ง์ ๋ชจ๋ธ์ ํ์ต์์ผ ๋ณด๊ฒ ์ต๋๋ค. (RNN ๋ฌธ์์ ์ ์ฌ, IMDB ๋ฐ์ดํฐ ์ฌ์ฉ)
**1๋จ๊ณ. ๋ฐ์ดํฐ ๋ก๋ ๋ฐ ์ ์ฒ๋ฆฌ**
```python
import numpy as np
import tensorflow as tf
from tensorflow import keras
from keras import layers
(x_train, y_train), (x_test, y_test) = keras.datasets.imdb.load_data(num_words=10000)
x_train = keras.preprocessing.sequence.pad_sequences(x_train, maxlen=256)
x_test = keras.preprocessing.sequence.pad_sequences(x_test, maxlen=256)
```
**2๋จ๊ณ. ๋ชจ๋ธ ์ปดํ์ผ**
```python
model = keras.Sequential([
layers.Embedding(input_dim=10000, output_dim=32),
layers.LSTM(32), # ๋๋ layers.GRU(32)
layers.Dense(1, activation="sigmoid")
])
model.compile(
loss="binary_crossentropy",
optimizer="adam",
metrics=["accuracy"]
)
```
**3๋จ๊ณ. ๋ชจ๋ธ ํ์ต ๋ฐ ํ๊ฐ**
```python
batch_size = 128
epochs = 10
history = model.fit(
x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test)
)
score = model.evaluate(x_test, y_test, verbose=0)
print(f"\nTest loss: {score[0]:.4f}")
print(f"Test accuracy: {score[1]:.4f}")
```
**4๋จ๊ณ. ํ์ต๋ ๋ชจ๋ธ ์ ์ฅ ๋ฐ ์ฌ์ฌ์ฉ**
```python
model.save("my_lstm_model_imdb.keras")
loaded_model = keras.models.load_model("my_lstm_model_imdb.keras")
```
**5๋จ๊ณ. ๋๋ง์ ๋ฌธ์ฅ์ผ๋ก ๋ชจ๋ธ ํ
์คํธํ๊ธฐ**
```python
word_index = keras.datasets.imdb.get_word_index()
review = "This movie was fantastic and wonderful"
tokens = [word_index.get(word, 2) for word in review.lower().split()]
padded_tokens = keras.preprocessing.sequence.pad_sequences([tokens], maxlen=256)
prediction = loaded_model.predict(padded_tokens)
print(f"๋ฆฌ๋ทฐ: '{review}'")
print(f"๊ธ์ ํ๋ฅ : {prediction[0][0] * 100:.2f}%")
```
## 4. ๋๋ง์ LSTM/GRU ๋ชจ๋ธ ์
๊ทธ๋ ์ด๋ํ๊ธฐ
๊ธฐ๋ณธ ๋ชจ๋ธ์ ๋ ๊ฐ๋ ฅํ๊ฒ ๋ง๋ค๊ธฐ ์ํด ๋ค์ํ ๊ธฐ๋ฒ์ ์ ์ฉํด ๋ณด๊ฒ ์ต๋๋ค.
- **๊ธฐ์ด ์ฒด๋ ฅ ํ๋ จ : ํ์ดํผํ๋ผ๋ฏธํฐ ํ๋**
ํ์ต๋ฅ , ๋ฐฐ์น ํฌ๊ธฐ, ์ ๋ ์ ๋ฑ์ ์กฐ์ .
```python
optimizer = keras.optimizers.Adam(learning_rate=0.001)
model.compile(loss="binary_crossentropy", optimizer=optimizer, metrics=["accuracy"])
```
- **์ธต ์๊ธฐ : ๋ค์ค LSTM/GRU ๋ ์ด์ด**
```python
model = keras.Sequential([
layers.Embedding(input_dim=10000, output_dim=64),
layers.LSTM(64, return_sequences=True),
layers.LSTM(32),
layers.Dense(1, activation='sigmoid')
])
```
- **๊ณผ๊ฑฐ์ ๋ฏธ๋๋ฅผ ๋์์ : ์๋ฐฉํฅ LSTM/GRU**
```python
model = keras.Sequential([
layers.Embedding(input_dim=10000, output_dim=64),
layers.Bidirectional(layers.LSTM(64)),
layers.Dropout(0.5),
layers.Dense(1, activation='sigmoid')
])
```
- **์ ์ดํ์ต์ผ๋ก ์ฑ๋ฅ ๊ทน๋ํ ํ๊ธฐ**
์ฌ์ ํ์ต๋ ๋ชจ๋ธ(์: GloVe ์๋ฒ ๋ฉ) ์ฌ์ฉํ๊ฑฐ๋, ๋ํ ๋ชจ๋ธ์ LSTM ๋ ์ด์ด freeze.
```python
# ์: ์ฌ์ ํ์ต๋ ์๋ฒ ๋ฉ ๋ก๋ (๋ณ๋ ํ์ผ ํ์)
embedding_layer = layers.Embedding(input_dim=10000, output_dim=100, trainable=False)
# GloVe ๋ฑ์ผ๋ก ์ด๊ธฐํ
```
## 5. ๊ฒฐ๋ก
์ค๋์, RNN์ ํ๊ณ๋ฅผ ๋์ด์ LSTM๊ณผ GRU์ ํต์ฌ ์๋ฆฌ๋ถํฐ ์ค์ ๊ตฌํ, ์
๊ทธ๋ ์ด๋ ๋ฐฉ๋ฒ๊น์ง ์์๋ณด์์ต๋๋ค.
์ด ๋ ๋ชจ๋ธ์ ์์ฐ์ด ์ฒ๋ฆฌ๋ฟ๋ง ์๋๋ผ ์๊ณ์ด ์์ธก, ์์ฑ ์ธ์ ๋ฑ์์ ์ฌ์ ํ ํต์ฌ์ ์ธ ์ญํ ์ ํฉ๋๋ค.
ํนํ, LSTM/GRU์ ๊ฒ์ดํธ ์์ด๋์ด๋ ์ดํ ์ดํ
์
๋ฉ์ปค๋์ฆ๊ณผ ํธ๋์คํฌ๋จธ ๋ชจ๋ธ์ ๊ธฐ๋ฐ์ด ๋์์ต๋๋ค.
๋ค์์๋ ํธ๋์คํฌ๋จธ ๋ชจ๋ธ๋ก ๋์์ค๊ฒ ์ต๋๋ค!!
์ค๋๋ ์ข์ํ๋ฃจ ๋ณด๋ด์ธ์!!
|
Locutusque/liberalis-cogitator-smollm3-3b
|
Locutusque
| 2025-09-03T05:09:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"smollm3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-03T03:21:23Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
MrunangG/phi-2-mbux-assistant
|
MrunangG
| 2025-09-03T05:09:01Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:microsoft/phi-2",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"base_model:microsoft/phi-2",
"region:us"
] |
text-generation
| 2025-09-03T05:08:46Z |
---
base_model: microsoft/phi-2
library_name: peft
model_name: results
tags:
- base_model:adapter:microsoft/phi-2
- lora
- sft
- transformers
- trl
licence: license
pipeline_tag: text-generation
---
# Model Card for results
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- PEFT 0.17.1
- TRL: 0.22.1
- Transformers: 4.56.0
- Pytorch: 2.8.0
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Coolboi0099/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tall_rangy_porcupine
|
Coolboi0099
| 2025-09-03T05:07:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am tall_rangy_porcupine",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-03T05:06:39Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am tall_rangy_porcupine
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
swayamsingal/tencent-Hunyuan-MT-7B-light-nanoquant-light
|
swayamsingal
| 2025-09-03T05:07:19Z | 0 | 0 | null |
[
"safetensors",
"hunyuan_v1_dense",
"region:us"
] | null | 2025-09-03T02:25:25Z |
---
language: en
tags:
- llm
- compression
- nanoquant
- quantization
- pruning
license: apache-2.0
datasets: []
model-index: []
---
# NanoQuant Compressed Model
## Model Description
This is a compressed version of [tencent/Hunyuan-MT-7B](https://huggingface.co/tencent/Hunyuan-MT-7B)
created using NanoQuant, an advanced LLM compression toolkit.
## Compression Details
- **Compression Level**: light
- **Size Reduction**: 65.0%
- **Techniques Used**:
- Quantization: 8bit
- Pruning: magnitude
- LoRA: {'r': 64, 'alpha': 32, 'dropout': 0.05}
## Deployment Options
### Option 1: Direct Usage with Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("tencent_Hunyuan-MT-7B_nanoquant_light")
tokenizer = AutoTokenizer.from_pretrained("tencent_Hunyuan-MT-7B_nanoquant_light")
```
### Option 2: Ollama Deployment
This model is also available for Ollama:
```bash
ollama pull nanoquant-tencent-Hunyuan-MT-7B:light
```
## Performance Characteristics
Due to the compression, this model:
- Requires significantly less storage space
- Has faster loading times
- Uses less memory during inference
- Maintains most of the original model's capabilities
## Original Model
For information about the original model, please visit: https://huggingface.co/tencent/Hunyuan-MT-7B
## License
This model is released under the Apache 2.0 license.
## NanoQuant
NanoQuant is an advanced model compression system that achieves up to 99.95% size reduction while maintaining model performance.
Learn more at [NanoQuant Documentation](https://github.com/nanoquant/nanoquant).
|
bah63843/blockassist-bc-plump_fast_antelope_1756875844
|
bah63843
| 2025-09-03T05:04:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:04:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbkts/blockassist-bc-keen_fast_giraffe_1756875843
|
omerbkts
| 2025-09-03T05:04:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:04:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Mayankm1411/GENAI_CAT2_STORY
|
Mayankm1411
| 2025-09-03T05:03:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-03T05:02:24Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sekirr/blockassist-bc-masked_tenacious_whale_1756875739
|
sekirr
| 2025-09-03T05:03:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:02:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1756874168
|
vwzyrraz7l
| 2025-09-03T05:02:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:02:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
John6666/realism-by-stable-yogi-pony-v60-fp16-sdxl
|
John6666
| 2025-09-03T05:01:29Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"realistic",
"photorealistic",
"asian",
"european girl",
"indian women",
"scifi",
"fantasy",
"game",
"3D",
"2D",
"illustration",
"landscapes",
"anatomical",
"realism",
"skin micro-texture",
"dynamic lighting",
"pony",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2025-09-03T04:55:24Z |
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- realistic
- photorealistic
- asian
- european girl
- indian women
- scifi
- fantasy
- game
- 3D
- 2D
- illustration
- landscapes
- anatomical
- realism
- skin micro-texture
- dynamic lighting
- pony
---
Original model is [here](https://civitai.com/models/166609/realism-by-stable-yogi-pony?modelVersionId=2176178).
This model created by [Stable_Yogi](https://civitai.com/user/Stable_Yogi).
|
omerbektass/blockassist-bc-keen_fast_giraffe_1756875612
|
omerbektass
| 2025-09-03T05:00:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-03T05:00:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sophie-Rain-Spider-Ma-n-Vi-de-o-Tuto-rial/Sophie.Rain.Spiderman.Video.Oficial
|
Sophie-Rain-Spider-Ma-n-Vi-de-o-Tuto-rial
| 2025-09-03T04:59:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-03T04:57:08Z |
<!-- HTML_TAG_END --><div>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐๐๐ญ๐๐ก ๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ)</a></p>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )</a></p>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a></p>
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|
emogie3D/granite-3.3-8b-instruct-gguf
|
emogie3D
| 2025-09-03T04:58:12Z | 0 | 0 |
transformers
|
[
"transformers",
"language",
"granite-3.3",
"text-generation",
"arxiv:0000.00000",
"base_model:ibm-granite/granite-3.3-8b-base",
"base_model:finetune:ibm-granite/granite-3.3-8b-base",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-09-03T04:34:50Z |
---
pipeline_tag: text-generation
inference: false
license: apache-2.0
library_name: transformers
tags:
- language
- granite-3.3
base_model:
- ibm-granite/granite-3.3-8b-base
---
# Granite-3.3-8B-Instruct
Quantized models of Granite-3.3-8B-Instruct
**used tool to Quantize the model:**
* https://github.com/kevkid/gguf_gui - Gui
* https://github.com/ggml-org/llama.cpp - Backend
**Original Model by:**
* ibm-granite/granite-3.3-8b-instruct
* URL: https://huggingface.co/ibm-granite/granite-3.3-8b-instruct
**Model Summary:**
* Granite-3.3-8B-Instruct is a 8-billion parameter 128K context length language model fine-tuned for improved reasoning and instruction-following capabilities. Built on top of Granite-3.3-8B-Base, the model delivers significant gains on benchmarks for measuring generic performance including AlpacaEval-2.0 and Arena-Hard, and improvements in mathematics, coding, and instruction following. It supports structured reasoning through \<think\>\<\/think\> and \<response\>\<\/response\> tags, providing clear separation between internal thoughts and final outputs. The model has been trained on a carefully balanced combination of permissively licensed data and curated synthetic tasks.
- **Developers:** Granite Team, IBM
- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
- **Release Date**: April 16th, 2025
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
**Supported Languages:**
English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. However, users may finetune this Granite model for languages beyond these 12 languages.
**Intended Use:**
This model is designed to handle general instruction-following tasks and can be integrated into AI assistants across various domains, including business applications.
**Capabilities**
* Thinking
* Summarization
* Text classification
* Text extraction
* Question-answering
* Retrieval Augmented Generation (RAG)
* Code related tasks
* Function-calling tasks
* Multilingual dialog use cases
<!-- * Fill-in-the-middle -->
* Long-context tasks including long document/meeting summarization, long document QA, etc.
**Generation:**
This is a simple example of how to use Granite-3.3-8B-Instruct model.
Install the following libraries:
```shell
pip install torch torchvision torchaudio
pip install accelerate
pip install transformers
```
Then, copy the snippet from the section that is relevant for your use case.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
import torch
model_path="ibm-granite/granite-3.3-8b-instruct"
device="cuda"
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map=device,
torch_dtype=torch.bfloat16,
)
tokenizer = AutoTokenizer.from_pretrained(
model_path
)
conv = [{"role": "user", "content":"Redesign a common household item to make it more sustainable and user-friendly. Explain the changes and their benefits."}]
input_ids = tokenizer.apply_chat_template(conv, return_tensors="pt", thinking=True, return_dict=True, add_generation_prompt=True).to(device)
set_seed(42)
output = model.generate(
**input_ids,
max_new_tokens=8192,
)
prediction = tokenizer.decode(output[0, input_ids["input_ids"].shape[1]:], skip_special_tokens=True)
print(prediction)
```
**Example Outputs**
- thinking=True
```md
<think>To address this prompt, I'll focus on redesigning a common household item: the plastic water bottle. The goal is to make it more sustainable and user-friendly. Here are the proposed changes and their benefits:
1. **Material**: Replace plastic with biodegradable or recyclable materials like glass or plant-based plastics (PLA).
2. **Design**: Introduce a modular design for easy refilling and cleaning.
3. **Functionality**: Incorporate a built-in water filter for purification.
4. **Durability**: Enhance the bottle's lifespan through robust construction.
5. **User-friendliness**: Simplify the cap mechanism for easy opening and closing.
These changes would significantly reduce plastic waste, promote recycling, ensure access to clean drinking water, and enhance the overall user experience.
Now, I will detail each change and its benefits in a structured format.</think>
<response>### Redesign of the Plastic Water Bottle for Sustainability and User-Friendliness
#### 1. Material: Biodegradable or Recyclable Alternatives
**Change**: Transition from traditional PET plastic to glass or plant-based plastics (Polylactic Acid - PLA).
**Benefits**:
- **Sustainability**: Glass and PLA are biodegradable or highly recyclable, drastically reducing plastic pollution.
- **Health**: Unlike PET, these materials do not leach chemicals into the water, ensuring safer consumption.
#### 2. Modular Design for Easy Refilling and Cleaning
**Change**: Implement a detachable, interchangeable inner liner within a reusable outer casing.
**Benefits**:
- **User-Friendliness**: Users can easily remove the inner liner for thorough cleaning, reducing the risk of bacterial growth.
- **Sustainability**: The outer casing remains intact, encouraging reuse, while the inner liner can be replaced or recycled when worn out.
#### 3. Built-in Water Filter
**Change**: Integrate a removable, replaceable water filter into the bottle cap.
**Benefits**:
- **Accessibility**: Provides clean drinking water directly from any source (tap, river, etc.), eliminating the need for additional filtration devices.
- **Health**: Removes contaminants, bacteria, and impurities, ensuring safe hydration.
- **Sustainability**: Reduces the use of single-use plastic water bottles by offering a purification solution within a reusable container.
#### 4. Enhanced Durability
**Change**: Use high-quality, durable materials and robust construction techniques for both the outer casing and inner liner.
**Benefits**:
- **Longevity**: A more robust design increases the bottle's lifespan, reducing the frequency of replacements.
- **Cost-effectiveness**: Although the initial cost might be higher, the long-term savings from reduced replacements make it economical.
- **Sustainability**: Less frequent disposal means lower environmental impact.
#### 5. Simplified Cap Mechanism
**Change**: Adopt a one-touch, easy-to-open and close cap design, possibly inspired by sports bottle caps.
**Benefits**:
- **User-Friendliness**: Especially beneficial for children, elderly, or those with limited hand dexterity.
- **Convenience**: Quick access to water encourages more frequent hydration, promoting healthier habits.
### Conclusion
This redesigned water bottle addresses key sustainability concerns by minimizing plastic use and waste, while also enhancing user-friendliness through improved functionality, durability, and accessibility. By integrating biodegradable materials, a modular design, a built-in filter, robust construction, and a simple cap mechanism, the bottle not only reduces environmental footprint but also offers a superior user experience.</response>
```
- thinking=False
```md
Item for Redesign: Plastic Bottle (e.g., water or beverage bottle)
Redesign Concept: Eco-Friendly, Modular, and Multi-Functional Reusable Bottle
Changes:
1. Material: Replace the conventional plastic with food-grade, BPA-free, and recyclable materials such as stainless steel or glass. These materials are durable, non-toxic, and have a longer lifespan, reducing waste.
2. Modular Design: Introduce a modular design that allows users to customize their bottle according to their needs. This could include interchangeable caps, mouthpiece sizes, and carrying loops. Users can purchase additional modules as needed, promoting a more sustainable approach by reducing the need to buy an entirely new bottle for different purposes.
3. Integrated Filter: Incorporate a built-in, washable, and reusable filter that can remove impurities and improve the taste of water. This eliminates the need for single-use disposable filters or bottled water, further reducing plastic waste.
4. Smart Cap: Develop a smart cap with a built-in digital display and temperature sensor. This feature allows users to track their daily water intake, set hydration goals, and monitor the temperature of their beverage. The smart cap can be synced with a mobile app for additional functionality, such as reminders and progress tracking.
5. Easy-to-Clean Design: Ensure the bottle has a wide mouth and smooth interior surfaces for easy cleaning. Include a brush for hard-to-reach areas, making maintenance simple and encouraging regular use.
6. Collapsible Structure: Implement a collapsible design that reduces the bottle's volume when not in use, making it more portable and convenient for storage.
Benefits:
1. Sustainability: By using recyclable materials and reducing plastic waste, this redesigned bottle significantly contributes to a more sustainable lifestyle. The modular design and reusable filter also minimize single-use plastic consumption.
2. User-Friendly: The smart cap, easy-to-clean design, and collapsible structure make the bottle convenient and user-friendly. Users can customize their bottle to suit their needs, ensuring a better overall experience.
3. Healthier Option: Using food-grade, BPA-free materials and an integrated filter ensures that the beverages consumed are free from harmful chemicals and impurities, promoting a healthier lifestyle.
4. Cost-Effective: Although the initial investment might be higher, the long-term savings from reduced purchases of single-use plastic bottles and disposable filters make this reusable bottle a cost-effective choice.
5. Encourages Hydration: The smart cap's features, such as hydration tracking and temperature monitoring, can motivate users to stay hydrated and develop healthier habits.
By redesigning a common household item like the plastic bottle, we can create a more sustainable, user-friendly, and health-conscious alternative that benefits both individuals and the environment.
```
**Evaluation Results:**
<table>
<thead>
<caption style="text-align:center"><b>Comparison with different models over various benchmarks<sup id="fnref1"><a href="#fn1">1</a></sup>. Scores of AlpacaEval-2.0 and Arena-Hard are calculated with thinking=True</b></caption>
<tr>
<th style="text-align:left; background-color: #001d6c; color: white;">Models</th>
<th style="text-align:center; background-color: #001d6c; color: white;">Arena-Hard</th>
<th style="text-align:center; background-color: #001d6c; color: white;">AlpacaEval-2.0</th>
<th style="text-align:center; background-color: #001d6c; color: white;">MMLU</th>
<th style="text-align:center; background-color: #001d6c; color: white;">PopQA</th>
<th style="text-align:center; background-color: #001d6c; color: white;">TruthfulQA</th>
<th style="text-align:center; background-color: #001d6c; color: white;">BigBenchHard<sup id="fnref2"><a href="#fn2">2</a></sup></th>
<th style="text-align:center; background-color: #001d6c; color: white;">DROP<sup id="fnref3"><a href="#fn3">3</a></sup></th>
<th style="text-align:center; background-color: #001d6c; color: white;">GSM8K</th>
<th style="text-align:center; background-color: #001d6c; color: white;">HumanEval</th>
<th style="text-align:center; background-color: #001d6c; color: white;">HumanEval+</th>
<th style="text-align:center; background-color: #001d6c; color: white;">IFEval</th>
<th style="text-align:center; background-color: #001d6c; color: white;">AttaQ</th>
</tr></thead>
<tbody>
<tr>
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Granite-3.1-2B-Instruct</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">23.3</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">27.17</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">57.11</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">20.55</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">59.79</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">61.82</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">20.99</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">67.55</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">79.45</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">75.26</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">63.59</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">84.7</td>
</tr>
<tr>
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Granite-3.2-2B-Instruct</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">24.86</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">34.51</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">57.18</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">20.56</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">59.8</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">61.39</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">23.84</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">67.02</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">80.13</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">73.39</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">61.55</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">83.23</td>
</tr>
<tr>
<td style="text-align:left; background-color: #DAE8FF; color: black;"><b>Granite-3.3-2B-Instruct</b></td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 28.86 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 43.45 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 55.88 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 18.4 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 58.97 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 63.91 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 44.33 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 72.48 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 80.51 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 75.68 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 65.8 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;">87.47</td>
</tr>
<tr>
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Llama-3.1-8B-Instruct</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">36.43</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">27.22</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">69.15</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">28.79</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">52.79</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">73.43</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">71.23</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">83.24</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">85.32</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">80.15</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">79.10</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">83.43</td>
</tr>
<tr>
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">DeepSeek-R1-Distill-Llama-8B</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">17.17</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">21.85</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">45.80</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">13.25</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">47.43</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">67.39</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">49.73</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">72.18</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">67.54</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">62.91</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">66.50</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">42.87</td>
</tr>
<tr>
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Qwen-2.5-7B-Instruct</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">25.44</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">30.34</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">74.30</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">18.12</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">63.06</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">69.19</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">64.06</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">84.46</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">93.35</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">89.91</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">74.90</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">81.90</td>
</tr>
<tr>
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">DeepSeek-R1-Distill-Qwen-7B</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">10.36</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">15.35</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">50.72</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">9.94</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">47.14</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">67.38</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">51.78</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">78.47</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">79.89</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">78.43</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">59.10</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">42.45</td>
</tr>
<tr>
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Granite-3.1-8B-Instruct</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">37.58</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">30.34</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">66.77</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">28.7</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">65.84</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">69.87</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">58.57</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">79.15</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">89.63</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">85.79</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">73.20</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">85.73</td>
</tr>
<tr>
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Granite-3.2-8B-Instruct</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">55.25</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">61.19</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">66.79</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">28.04</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">66.92</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">71.86</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">58.29</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">81.65</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">89.35</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">85.72</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">74.31</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">84.7</td>
</tr>
<tr>
<td style="text-align:left; background-color: #DAE8FF; color: black;"><b>Granite-3.3-8B-Instruct</b></td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 57.56 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 62.68 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 65.54 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 26.17 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 66.86 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 69.13 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 59.36 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 80.89 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 89.73 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 86.09 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 74.82 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;">88.5</td>
</tr>
</tbody></table>
<table>
<caption style="text-align:center"><b>Math Benchmarks</b></caption>
<thead>
<tr>
<th style="text-align:left; background-color: #001d6c; color: white;">Models</th>
<th style="text-align:center; background-color: #001d6c; color: white;">AIME24</th>
<th style="text-align:center; background-color: #001d6c; color: white;">MATH-500</th>
</tr></thead>
<tbody>
<tr>
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Granite-3.1-2B-Instruct</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;"> 0.89 </td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;"> 35.07 </td>
</tr>
<tr>
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Granite-3.2-2B-Instruct</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;"> 0.89 </td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;"> 35.54 </td>
</tr>
<tr>
<td style="text-align:left; background-color: #DAE8FF; color: black;"><b>Granite-3.3-2B-Instruct</b></td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 3.28 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 58.09 </td>
</tr>
<tr>
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Granite-3.1-8B-Instruct</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;"> 1.97 </td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;"> 48.73 </td>
</tr>
<tr>
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Granite-3.2-8B-Instruct</td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;"> 2.43 </td>
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;"> 52.8 </td>
</tr>
<tr>
<td style="text-align:left; background-color: #DAE8FF; color: black;"><b>Granite-3.3-8B-Instruct</b></td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 8.12 </td>
<td style="text-align:center; background-color: #DAE8FF; color: black;"> 69.02 </td>
</tr>
</tbody></table>
**Training Data:**
Overall, our training data is largely comprised of two key sources: (1) publicly available datasets with permissive license, (2) internal synthetically generated data targeted to enhance reasoning capabilites.
<!-- A detailed attribution of datasets can be found in [Granite 3.2 Technical Report (coming soon)](#), and [Accompanying Author List](https://github.com/ibm-granite/granite-3.0-language-models/blob/main/author-ack.pdf). -->
**Infrastructure:**
We train Granite-3.3-8B-Instruct using IBM's super computing cluster, Blue Vela, which is outfitted with NVIDIA H100 GPUs. This cluster provides a scalable and efficient infrastructure for training our models over thousands of GPUs.
**Ethical Considerations and Limitations:**
Granite-3.3-8B-Instruct builds upon Granite-3.3-8B-Base, leveraging both permissively licensed open-source and select proprietary data for enhanced performance. Since it inherits its foundation from the previous model, all ethical considerations and limitations applicable to [Granite-3.3-8B-Base](https://huggingface.co/ibm-granite/granite-3.3-8b-base) remain relevant.
**Resources**
- โญ๏ธ Learn about the latest updates with Granite: https://www.ibm.com/granite
- ๐ Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/
- ๐ก Learn about the latest Granite learning resources: https://github.com/ibm-granite-community/
<p><a href="#fnref1" title="Jump back to reference">[1]</a> Evaluated using <a href="https://github.com/allenai/olmes">OLMES</a> (except AttaQ and Arena-Hard scores)</p>
<p><a href="#fnref2" title="Jump back to reference">[2]</a> Added regex for more efficient asnwer extraction.</a></p>
<p><a href="#fnref3" title="Jump back to reference">[3]</a> Modified the implementation to handle some of the issues mentioned <a href="https://huggingface.co/blog/open-llm-leaderboard-drop">here</a></p>
<!-- ## Citation
<!-- ## Citation
```
@misc{granite-models,
author = {author 1, author2, ...},
title = {},
journal = {},
volume = {},
year = {2024},
url = {https://arxiv.org/abs/0000.00000},
}
``` -->
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