RLHFlow
/

RewardModel-Mistral-7B-for-DPA-v1

@@ -17,185 +17,82 @@ tags: []
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 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **Developed by:** Haoxiang Wang
+- **Model type:** Sequence Classifier
+- **Language(s) (NLP):** English
+- **License:** Apache-2.0
+- **Finetuned from model [optional]:** https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2
 ### Model Sources [optional]
 <!-- Provide the basic links for the model. -->
+- **Repository:** https://github.com/RLHFlow/directional-preference-alignment
+- **Paper [optional]:** https://arxiv.org/abs/2402.18571
 ## How to Get Started with the Model
 Use the code below to get started with the model.
+The model has 10-dimensional output, corresponding to the following attributes from HelpSteer and UltraFeedback
+['helpsteer-helpfulness', 'helpsteer-correctness', 'helpsteer-coherence', 'helpsteer-complexity', 'helpsteer-verbosity', 'ultrafeedback-overall_score', "ultrafeedback-instruction_following", "ultrafeedback-truthfulness", "ultrafeedback-honesty", "ultrafeedback-helpfulness"]
+Here is a sample code that you can try
+```python
+from transformers import AutoModelForSequenceClassification,AutoTokenizer
+import torch
+device = 'cuda'
+path = "Haoxiang-Wang/RewardModel-Mistral-7B-for-DPA-v1"
+rm = AutoModelForSequenceClassification.from_pretrained(path, trust_remote_code=True).to(device)
+tokenizer = AutoTokenizer.from_pretrained(path)
+input_template = "[INST] You must read the following conversation carefully and rate the assistant's response from score 0-100 in these aspects: helpfulness, correctness, coherence, honesty, complexity, verbosity\n\nUser: {prompt}\n\nAssistant: {response} [/INST]"
+# Use a sample from HelpSteer validation set
+prompt = 'What are some synonyms for the word "beautiful"?'
+response = "Nicely, Beautifully, Handsome, Stunning, Wonderful, Gorgeous, Pretty, Stunning, Elegant"
+model_inputs = tokenizer(input_template.format(prompt=prompt, response=response), return_tensors="pt").to(device)
+with torch.no_grad():
+    score = rm(**model_inputs).logits.squeeze().cpu().float().numpy()
+print(score)
+# [68.99269  69.62718  76.23071  33.48785  35.853596 63.833366 55.58917 68.7175 59.552124 46.465595]
+# Convert from our scale (0-100) to HelpSteer scale (0-4)
+helpsteer_rewards_pred = (score[:5]-10)/20
+print(helpsteer_rewards_pred)
+# [2.9496346 2.981359  3.3115356 1.1743925 1.2926798]
+# The actual rewards from the HelpSteer dataset for this sample are [3,3,4,2,2]
+```
+## Training
+![image/png](https://github.com/RLHFlow/directional-preference-alignment/raw/main/assets/preference-conflict.jpg)
+![image/png](https://github.com/RLHFlow/directional-preference-alignment/raw/main/assets/algo-illustration.jpg)
+## Citation
 **BibTeX:**
+If you find this work useful to your research, please consider citing our paper
+```
+@article{wang2024arithmetic,
+      title={Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards},
+      author={Haoxiang Wang and Yong Lin and Wei Xiong and Rui Yang and Shizhe Diao and Shuang Qiu and Han Zhao and Tong Zhang},
+      year={2024},
+      eprint={2402.18571},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+```
+## Model Card Authors
+Haoxiang Wang
 ## Model Card Contact
+hwang264@illinois.edu