Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) pair-preference-model-LLaMA3-8B - GGUF - Model creator: https://huggingface.co/RLHFlow/ - Original model: https://huggingface.co/RLHFlow/pair-preference-model-LLaMA3-8B/ | Name | Quant method | Size | | ---- | ---- | ---- | | [pair-preference-model-LLaMA3-8B.Q2_K.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.Q2_K.gguf) | Q2_K | 2.96GB | | [pair-preference-model-LLaMA3-8B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.IQ3_XS.gguf) | IQ3_XS | 3.28GB | | [pair-preference-model-LLaMA3-8B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.IQ3_S.gguf) | IQ3_S | 3.43GB | | [pair-preference-model-LLaMA3-8B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.Q3_K_S.gguf) | Q3_K_S | 3.41GB | | [pair-preference-model-LLaMA3-8B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.IQ3_M.gguf) | IQ3_M | 3.52GB | | [pair-preference-model-LLaMA3-8B.Q3_K.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.Q3_K.gguf) | Q3_K | 3.74GB | | [pair-preference-model-LLaMA3-8B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.Q3_K_M.gguf) | Q3_K_M | 3.74GB | | [pair-preference-model-LLaMA3-8B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.Q3_K_L.gguf) | Q3_K_L | 4.03GB | | [pair-preference-model-LLaMA3-8B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.IQ4_XS.gguf) | IQ4_XS | 4.18GB | | [pair-preference-model-LLaMA3-8B.Q4_0.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.Q4_0.gguf) | Q4_0 | 4.34GB | | [pair-preference-model-LLaMA3-8B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.IQ4_NL.gguf) | IQ4_NL | 4.38GB | | [pair-preference-model-LLaMA3-8B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.Q4_K_S.gguf) | Q4_K_S | 4.37GB | | [pair-preference-model-LLaMA3-8B.Q4_K.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.Q4_K.gguf) | Q4_K | 4.58GB | | [pair-preference-model-LLaMA3-8B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.Q4_K_M.gguf) | Q4_K_M | 4.58GB | | [pair-preference-model-LLaMA3-8B.Q4_1.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.Q4_1.gguf) | Q4_1 | 4.78GB | | [pair-preference-model-LLaMA3-8B.Q5_0.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.Q5_0.gguf) | Q5_0 | 5.21GB | | [pair-preference-model-LLaMA3-8B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.Q5_K_S.gguf) | Q5_K_S | 5.21GB | | [pair-preference-model-LLaMA3-8B.Q5_K.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.Q5_K.gguf) | Q5_K | 5.34GB | | [pair-preference-model-LLaMA3-8B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.Q5_K_M.gguf) | Q5_K_M | 5.34GB | | [pair-preference-model-LLaMA3-8B.Q5_1.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.Q5_1.gguf) | Q5_1 | 5.65GB | | [pair-preference-model-LLaMA3-8B.Q6_K.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.Q6_K.gguf) | Q6_K | 6.14GB | | [pair-preference-model-LLaMA3-8B.Q8_0.gguf](https://huggingface.co/RichardErkhov/RLHFlow_-_pair-preference-model-LLaMA3-8B-gguf/blob/main/pair-preference-model-LLaMA3-8B.Q8_0.gguf) | Q8_0 | 7.95GB | Original model description: --- license: llama3 --- 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). 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. See our paper [RLHF Workflow: From Reward Modeling to Online RLHF](https://arxiv.org/abs/2405.07863) for more details of this model. ## Service the RM 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. ```python device = 0 model = AutoModelForCausalLM.from_pretrained(script_args.preference_name_or_path, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2").cuda() tokenizer = AutoTokenizer.from_pretrained(script_args.preference_name_or_path, use_fast=True) tokenizer_plain = AutoTokenizer.from_pretrained(script_args.preference_name_or_path, use_fast=True) tokenizer_plain.chat_template = "\n{% for message in messages %}{% if loop.index0 % 2 == 0 %}\n\n user\n {{ message['content'] }}{% else %}\n\n assistant\n {{ message['content'] }}{% endif %}{% endfor %}\n\n\n" prompt_template = "[CONTEXT] {context} [RESPONSE A] {response_A} [RESPONSE B] {response_B} \n" token_id_A = tokenizer.encode("A", add_special_tokens=False) token_id_B = tokenizer.encode("B", add_special_tokens=False) assert len(token_id_A) == 1 and len(token_id_B) == 1 token_id_A = token_id_A[0] token_id_B = token_id_B[0] temperature = 1.0 model.eval() response_chosen = "BBBB" response_rejected = "CCCC" ## We can also handle multi-turn conversation. instruction = [{"role": "user", "content": ...}, {"role": "assistant", "content": ...}, {"role": "user", "content": ...}, ] context = tokenizer_plain.apply_chat_template(instruction, tokenize=False) responses = [response_chosen, response_rejected] probs_chosen = [] for chosen_position in [0, 1]: # we swap order to mitigate position bias response_A = responses[chosen_position] response_B = responses[1 - chosen_position] prompt = prompt_template.format(context=context, response_A=response_A, response_B=response_B) message = [ {"role": "user", "content": prompt}, ] input_ids = tokenizer.encode(tokenizer.apply_chat_template(message, tokenize=False).replace(tokenizer.bos_token, ""), return_tensors='pt', add_special_tokens=False).cuda() with torch.no_grad(): output = model(input_ids) logit_A = output.logits[0, -1, token_id_A].item() logit_B = output.logits[0, -1, token_id_B].item() # take softmax to get the probability; using numpy Z = np.exp(logit_A / temperature) + np.exp(logit_B / temperature) logit_chosen = [logit_A, logit_B][chosen_position] prob_chosen = np.exp(logit_chosen / temperature) / Z probs_chosen.append(prob_chosen) avg_prob_chosen = np.mean(probs_chosen) correct = 0.5 if avg_prob_chosen == 0.5 else float(avg_prob_chosen > 0.5) print(correct) ``` ## Citation If you use this model in your research, please consider citing our paper ``` @misc{rlhflow, title={RLHF Workflow: From Reward Modeling to Online RLHF}, 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}, year={2024}, eprint={2405.07863}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` and Google's Slic paper (which initially proposes this pairwise preference model) ``` @article{zhao2023slic, title={Slic-hf: Sequence likelihood calibration with human feedback}, author={Zhao, Yao and Joshi, Rishabh and Liu, Tianqi and Khalman, Misha and Saleh, Mohammad and Liu, Peter J}, journal={arXiv preprint arXiv:2305.10425}, year={2023} } ```