weqweasdas
/

RM-Mistral-7B

@@ -1,199 +1,103 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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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.
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 ---
+{}
 ---
+# Reward Model Overview
 <!-- Provide a quick summary of what the model is/does. -->
+The reward model is trained from the base model [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it).
+The training script is available at https://github.com/WeiXiongUST/RLHF-Reward-Modeling .
 ## Model Details
+If you have any question with this reward model and also any question about reward modeling, feel free to drop me an email with wx13@illinois.edu. I would be happy to chat!
+### Dataset preprocessing
+<!-- Provide a longer summary of what this model is. -->
+The model is trained on a mixture of [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it).
+- [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf)
+- [SHP](https://huggingface.co/datasets/stanfordnlp/SHP)
+- [UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback)
+- [Capybara](argilla/distilabel-capybara-dpo-7k-binarized)
+- [HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer)
+- [Orca](argilla/distilabel-intel-orca-dpo-pairs)
+Difference between this mixture and that of
+- SHP: we only use the samples with score ratio > 2, for each prompt, we take 5 comparison at most, leading to 109526;
+- Ultrafeedback: similar to UltraFeedback-Binarized, we use the fine-grained score instead of the overall one to rank samples. Meanwhile, for each prompt, we take all possible 6 pairs of comparisons. Finally, we delete the selected pairs with equal scores, leading to 267416.
+- HelpSteer: we use the mean of helpfulness and correctness to rank samples. Meanwhile, we take all possible 6 pairs of comparisons. Finally, we delete the selected pairs with equal scores, leading to 21576;
+### Training
+We train the model for one epoch with a learning rate of 5e-6, batch size 512, cosine learning rate decay with a warmup ratio 0.03. You can see my training script here: https://github.com/WeiXiongUST/RAFT-Reward-Ranked-Finetuning/blob/main/reward_modeling.py , which is modified from the TRL package.
+## Uses
+```python
+  from transformers import AutoTokenizer, pipeline
+  rm_tokenizer = AutoTokenizer.from_pretrained("weqweasdas/RM-Mistral-7B")
+  device = 0 # accelerator.device
+  rm_pipe = pipeline(
+      "sentiment-analysis",
+      model="weqweasdas/RM-Mistral-7B",
+      #device="auto",
+      device=device,
+      tokenizer=rm_tokenizer,
+      model_kwargs={"torch_dtype": torch.bfloat16}
+  )
+  pipe_kwargs = {
+      "return_all_scores": True,
+      "function_to_apply": "none",
+      "batch_size": 1
+  }
+  chat = [
+   {"role": "user", "content": "Hello, how are you?"},
+   {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
+   {"role": "user", "content": "I'd like to show off how chat templating works!"},
+  ]
+  test_texts = [tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False).replace(tokenizer.bos_token, "")]
+  pipe_outputs = rm_pipe(test_texts, **pipe_kwargs)
+  rewards = [output[0]["score"] for output in pipe_outputs]
+```
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+## Results
+To be evaluted by hte benchmark.
+## Reference
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+To be added. The reward model may be readily used for rejection sampling finetuning (
+```
+@article{dong2023raft,
+  title={Raft: Reward ranked finetuning for generative foundation model alignment},
+  author={Dong, Hanze and Xiong, Wei and Goyal, Deepanshu and Pan, Rui and Diao, Shizhe and Zhang, Jipeng and Shum, Kashun and Zhang, Tong},
+  journal={arXiv preprint arXiv:2304.06767},
+  year={2023}
+}
+```