yyqoni
/

meta-llama-3.1-instruct-8b-segment-ppo-60k

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 ---
 library_name: transformers
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 ---
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 ---
 library_name: transformers
+license: mit
+datasets:
+- argilla/ultrafeedback-binarized-preferences-cleaned
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
+pipeline_tag: text-generation
 ---
+# meta-llama-3.1-segment-ppo Model Card
+The *meta-llama-3.1-segment-ppo* model introduces a segment-level reward model to improve reinforcement learning with human feedback (RLHF) in language models. This work builds upon the methods in our paper *[Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model](https://arxiv.org/abs/2501.02790)*.
+---
+## Method Illustration
+Below is an illustration of the segment-based reward modeling method, showing how entropy thresholds are used for segmentation, integrating both the reward model and PPO training:
+## Architecture
+<div align=center>
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/605e8dfd5abeb13e714c4c18/xeGwtrpnx2bWFg5ZOHA7R.png)
+</div>
+---
+## Model Overview
+This approach redefines the granularity of RLHF training by:
+- Assigning rewards to semantically complete text segments, defined based on entropy thresholds.
+- Introducing techniques to stabilize RLHF training under dense, segment-level rewards.
+Model checkpoints are available on [HuggingFace](https://huggingface.co/collections/yyqoni/denserewardrlhf-ppo-677d39b5521f1e366c196f14).
+---
+## Training Data
+We utilize the following datasets in our training pipeline:
+- **Preference-700K Dataset**: A diverse collection of open-source preference datasets, including HH-RLHF, Stanford Human Preferences Dataset (SHP), and HelpSteer.
+- **Ultrafeedback Dataset**: Used for sampling prompts during the PPO training routine.
+---
+## Base Model
+The *phi-instruct-segment-ppo* model is fine-tuned from **meta-llama/Llama-3.1-8B-Instruct**.
+---
+## Usage
+You can use this model directly with Hugging Face's Transformers library:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+# Load model and tokenizer
+model_name = "yyqoni/meta-llama-3.1-instruct-8b-segment-ppo-60k"
+model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+# Input text
+input_text = "What are the benefits of using reinforcement learning in AI?"
+# Apply chat template formatting with generation prompt
+formatted_input = tokenizer.apply_chat_template(
+    [{"role": "user", "content": input_text}],
+    tokenize=False,
+    add_generation_prompt=True
+)
+# Tokenize the formatted input
+inputs = tokenizer(formatted_input, return_tensors="pt", add_special_tokens=False)
+# Generate response
+outputs = model.generate(**inputs, max_new_tokens=50)
+# Decode and print the response
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+---
+## Citation
+If you find this model or our research useful, please consider citing our paper:
+```bibtex
+@misc{yin2025segmentingtextlearningrewards,
+      title={Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model},
+      author={Yueqin Yin and Shentao Yang and Yujia Xie and Ziyi Yang and Yuting Sun and Hany Awadalla and Weizhu Chen and Mingyuan Zhou},
+      year={2025},
+      eprint={2501.02790},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2501.02790},
+}
+```