--- license: apache-2.0 datasets: - openai/summarize_from_feedback - openai/webgpt_comparisons - berkeley-nest/Nectar - Dahoas/instruct-synthetic-prompt-responses - Anthropic/hh-rlhf - lmsys/chatbot_arena_conversations - openbmb/UltraFeedback - argilla/ultrafeedback-binarized-preferences-cleaned metrics: - accuracy tags: - reward_model - reward-model - RLHF - evaluation - llm - instruction - reranking language: - en --- # Better Implementation of [*PairRM*](https://huggingface.co/llm-blender/PairRM) ## Introduction This version of PairRM have some fixes on training process, which improve model's performance by **15%**. ### Minor Fixes - Longer Context Length (2048 -> 3370) Thanks to deberta's tokenzer, original PairRM model had enough Context Length. But, the longer the better :> --- ### Major Fixes - Change Prompt Format Why use something like ``` {response} ``` So, I changed to a format based on Vicuna 1.1. --- - Change Truncate side The original process was using right side truncate even on Input. This can cause serious problem when Input exceeds model's context length. --- - Dataset Filter There was decent amount of empty assistant response on original dataset. So, I dropped them. --- ## Example Code **The code below is modified from** (**PairRM-hf Repo**)[https://huggingface.co/llm-blender/PairRM-hf] ```python import os os.environ["CUDA_VISIBLE_DEVICES"] = "0" from llm_blender.pair_ranker.pairrm import DebertaV2PairRM from transformers import AutoTokenizer from typing import List pairrm = DebertaV2PairRM.from_pretrained("maywell/Better-PairRM", device_map="cuda:0").eval() tokenizer = AutoTokenizer.from_pretrained("maywell/Better-PairRM") source_prefix = "<|source|>" cand1_prefix = "<|candidate1|>" cand2_prefix = "<|candidate2|>" inputs = ["hello!", "I love you!"] candidates_A = ["hi!", "I hate you!"] candidates_B = ["f**k off!", "I love you, too!"] def tokenize_pair(sources:List[str], candidate1s:List[str], candidate2s:List[str], source_max_length=2030, candidate_max_length=670): ids = [] assert len(sources) == len(candidate1s) == len(candidate2s) max_length = source_max_length + 2 * candidate_max_length for i in range(len(sources)): source_ids = tokenizer.encode(source_prefix + sources[i], max_length=source_max_length, truncation=True) candidate_max_length = (max_length - len(source_ids)) // 2 candidate1_ids = tokenizer.encode(cand1_prefix + candidate1s[i], max_length=candidate_max_length, truncation=True) candidate2_ids = tokenizer.encode(cand2_prefix + candidate2s[i], max_length=candidate_max_length, truncation=True) ids.append(source_ids + candidate1_ids + candidate2_ids) encodings = tokenizer.pad({"input_ids": ids}, return_tensors="pt", padding="max_length", max_length=max_length) return encodings encodings = tokenize_pair(inputs, candidates_A, candidates_B) encodings = {k:v.to(pairrm.device) for k,v in encodings.items()} outputs = pairrm(**encodings) logits = outputs.logits.tolist() comparison_results = outputs.logits > 0 print(logits) print(comparison_results) ``` You can also easily compare two conversations like the followings: ```python import jinja2 from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v3-large") def truncate_texts(text, max_length, truncate_side): tokenizer.truncation_side = truncate_side tokens = tokenizer.encode(text, add_special_tokens=False, max_length=max_length) truncated_text = tokenizer.decode(tokens, skip_special_tokens=True) return truncated_text MY_JINJA_TEMPLATE = """{% for message in messages -%} {% if message['role'] == 'user' -%} USER: {{ message['content']|trim -}} {% if not loop.last -%} {% endif %} {% elif message['role'] == 'assistant' -%} ASSISTANT: {{ message['content']|trim -}} {% if not loop.last -%} {% endif %} {% elif message['role'] == 'user_context' -%} USER: {{ message['content']|trim -}} {% if not loop.last -%} {% endif %} {% elif message['role'] == 'system' -%} SYSTEM MESSAGE: {{ message['content']|trim -}} {% if not loop.last -%} {% endif %} {% endif %} {% endfor -%} {% if add_generation_prompt and messages[-1]['role'] != 'assistant' -%} ASSISTANT: {% endif -%}""" my_jinja2_env = jinja2.Environment() my_jinja2_template = my_jinja2_env.from_string(MY_JINJA_TEMPLATE) def tokenize_conv_pair(convAs: List[str], convBs: List[str]): # check conversations correctness assert len(convAs) == len(convBs), "Number of conversations must be the same" for c_a, c_b in zip(convAs, convBs): assert len(c_a) == len(c_b), "Number of turns in each conversation must be the same" assert all([c_a[i]['content'] == c_b[i]['content'] for i in range(0, len(c_a), 2)]), "USER turns must be the same" inputs = [ truncate_texts(my_jinja2_template.render(messages=x[:-1], add_generation_prompt=True), 2030, "left") for x in convAs ] cand1_texts = [ truncate_texts(x[-1]['content'], 670, "right") for x in convAs ] cand2_texts = [ truncate_texts(x[-1]['content'], 670, "right") for x in convBs ] encodings = tokenize_pair(inputs, cand1_texts, cand2_texts) return encodings ``` ## Statistics ### Context length | PairRanker type | Source max length | Candidate max length | Total max length | |:-----------------:|:-----------------:|----------------------|------------------| | [pair-ranker](https://huggingface.co/llm-blender/pair-ranker) | 128 | 128 | 384 | | [PairRM](https://huggingface.co/llm-blender/pair-reward-model/) | 1224 | 412 | 2048 | | [Better-PairRM](https://huggingface.co/maywell/Better-PairRM/) (This model) | 2030 | 670 | 3370 | ### Performance #### Reward-Bench by AllenAI | Metric | llm-blender/PairRM-hf | maywell/Better-PairRM | |----------------------------|------------------------|------------------------| | model | llm-blender/PairRM-hf | maywell/Better-PairRM | | model_type | Custom Classifier | Custom Classifier | | alpacaeval-length | 0.758 | **0.863** | | alpacaeval-hard | 0.979 | **1.000** | | alpacaeval-easy | 0.970 | **0.990** | | donotanswer | 0.360 | **0.522** | | hep-cpp | 0.628 | **0.646** | | hep-go | 0.689 | **0.713** | | hep-java | 0.628 | **0.713** | | hep-js | 0.604 | **0.707** | | hep-python | 0.646 | **0.713** | | hep-rust | 0.652 | **0.726** | | llmbar-adver-GPTInst | **0.304** | 0.141 | | llmbar-adver-GPTOut | **0.596** | 0.447 | | llmbar-adver-manual | **0.500** | 0.261 | | llmbar-adver-neighbor | **0.433** | 0.276 | | llmbar-natural | **0.800** | 0.720 | | math-prm | **0.333** | 0.295 | | mt-bench-hard | 0.649 | **0.703** | | mt-bench-med | 0.900 | **1.000** | | mt-bench-easy | **0.964** | 0.929 | | refusals-dangerous | 0.080 | **0.730** | | refusals-offensive | 0.010 | **0.940** | | xstest-should-refuse | 0.370 | **0.968** | | xstest-should-respond | **0.952** | 0.876 | | average | 0.600 | **0.690** | > *Note - llmbar test score is bit weird across all models on [Reward-Bench](https://huggingface.co/spaces/allenai/reward-bench)* ## Thanks to - [Sionic AI](https://sionic.ai/) for providing the A100 cluster. ## Contact - [Discord Server Link](https://discord.gg/MrBt3PXdXc) ## Original Paper ``` @inproceedings{llm-blender-2023, title = "LLM-Blender: Ensembling Large Language Models with Pairwise Comparison and Generative Fusion", author = "Jiang, Dongfu and Ren, Xiang and Lin, Bill Yuchen", booktitle = "Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics (ACL 2023)", year = "2023" } ```