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--- |
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license: llama3 |
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--- |
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This preference model is trained from [LLaMA3-8B-it](meta-llama/Meta-Llama-3-8B-Instruct) with the training script at [Reward Modeling](https://github.com/RLHFlow/RLHF-Reward-Modeling/tree/pm_dev/pair-pm). |
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The dataset is RLHFlow/pair_preference_model_dataset. It achieves Chat-98.6, Char-hard 65.8, Safety 89.6, and reasoning 94.9 in reward bench. |
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See our paper [RLHF Workflow: From Reward Modeling to Online RLHF](https://arxiv.org/abs/2405.07863) for more details of this model. |
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## Service the RM |
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Here is an example to use the Preference Model to rank a pair. For n>2 responses, it is recommened to use the tournament style ranking strategy to get the best response so that the complexity is linear in n. |
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```python |
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device = 0 |
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model = AutoModelForCausalLM.from_pretrained(script_args.preference_name_or_path, |
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torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2").cuda() |
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tokenizer = AutoTokenizer.from_pretrained(script_args.preference_name_or_path, use_fast=True) |
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tokenizer_plain = AutoTokenizer.from_pretrained(script_args.preference_name_or_path, use_fast=True) |
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tokenizer_plain.chat_template = "\n{% for message in messages %}{% if loop.index0 % 2 == 0 %}\n\n<turn> user\n {{ message['content'] }}{% else %}\n\n<turn> assistant\n {{ message['content'] }}{% endif %}{% endfor %}\n\n\n" |
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prompt_template = "[CONTEXT] {context} [RESPONSE A] {response_A} [RESPONSE B] {response_B} \n" |
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token_id_A = tokenizer.encode("A", add_special_tokens=False) |
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token_id_B = tokenizer.encode("B", add_special_tokens=False) |
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assert len(token_id_A) == 1 and len(token_id_B) == 1 |
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token_id_A = token_id_A[0] |
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token_id_B = token_id_B[0] |
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temperature = 1.0 |
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model.eval() |
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response_chosen = "BBBB" |
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response_rejected = "CCCC" |
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## We can also handle multi-turn conversation. |
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instruction = [{"role": "user", "content": ...}, |
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{"role": "assistant", "content": ...}, |
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{"role": "user", "content": ...}, |
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] |
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context = tokenizer_plain.apply_chat_template(instruction, tokenize=False) |
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responses = [response_chosen, response_rejected] |
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probs_chosen = [] |
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for chosen_position in [0, 1]: |
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# we swap order to mitigate position bias |
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response_A = responses[chosen_position] |
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response_B = responses[1 - chosen_position] |
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prompt = prompt_template.format(context=context, response_A=response_A, response_B=response_B) |
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message = [ |
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{"role": "user", "content": prompt}, |
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] |
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input_ids = tokenizer.encode(tokenizer.apply_chat_template(message, tokenize=False).replace(tokenizer.bos_token, ""), return_tensors='pt', add_special_tokens=False).cuda() |
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with torch.no_grad(): |
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output = model(input_ids) |
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logit_A = output.logits[0, -1, token_id_A].item() |
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logit_B = output.logits[0, -1, token_id_B].item() |
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# take softmax to get the probability; using numpy |
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Z = np.exp(logit_A / temperature) + np.exp(logit_B / temperature) |
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logit_chosen = [logit_A, logit_B][chosen_position] |
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prob_chosen = np.exp(logit_chosen / temperature) / Z |
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probs_chosen.append(prob_chosen) |
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avg_prob_chosen = np.mean(probs_chosen) |
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correct = 0.5 if avg_prob_chosen == 0.5 else float(avg_prob_chosen > 0.5) |
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print(correct) |
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``` |
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## Citation |
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If you use this model in your research, please consider citing our paper |
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``` |
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@misc{rlhflow, |
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title={RLHF Workflow: From Reward Modeling to Online RLHF}, |
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author={Hanze Dong and Wei Xiong and Bo Pang and Haoxiang Wang and Han Zhao and Yingbo Zhou and Nan Jiang and Doyen Sahoo and Caiming Xiong and Tong Zhang}, |
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year={2024}, |
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eprint={2405.07863}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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``` |
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and Google's Slic paper (which initially proposes this pairwise preference model) |
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``` |
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@article{zhao2023slic, |
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title={Slic-hf: Sequence likelihood calibration with human feedback}, |
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author={Zhao, Yao and Joshi, Rishabh and Liu, Tianqi and Khalman, Misha and Saleh, Mohammad and Liu, Peter J}, |
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journal={arXiv preprint arXiv:2305.10425}, |
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year={2023} |
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} |
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``` |