Authors: Dongfu Jiang, Xiang Ren, Bill Yuchen Lin @ AI2-Mosaic USC-INK
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[2024/1/5] PairRM can now be directly loaded using Hugging face Wrapper
DebertaV2PairRM.from_pretrained("llm-blender/PairRM-hf"), see more in our 🤗Model page -
[2023/11/10] Glad to announce that our pairwise reward-model, 🤗PairRM, has released. It's trained on high-quality and large-scale human reference dataset and approaches GPT-4's alignment with human preference with a extremly small model size (0.4B).
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[2023/10/24] Pre-trained PairRanker is able to be loaded directly from 🤗 Hugging face Models llm-blender/PairRM within 3 lines of code. See Guidance for Rank & Fusion for details.
Abstract
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We introduce LLM-Blender, an innovative ensembling framework to attain consistently superior performance by leveraging the diverse strengths of multiple open-source large language models (LLMs). LLM-Blender cut the weaknesses through ranking and integrate the strengths through fusing generation to enhance the capability of LLMs.
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Our framework consists of two complementary modules: PairRanker and GenFuser, addressing the observation that optimal LLMs for different examples can significantly vary. PairRanker employs a specialized pairwise comparison method to distinguish subtle differences between candidate outputs. GenFuser aims to merge the top-ranked candidates from the aggregation of PairRanker's pairwise comparisons into an improved output by capitalizing on their strengths and mitigating their weaknesses.
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To facilitate large-scale evaluation, we introduce a benchmark dataset, MixInstruct, which is a mixture of multiple instruction datasets featuring oracle pairwise comparisons for testing purposes. Our LLM-Blender significantly surpasses the best LLMs and baseline ensembling methods across various metrics on MixInstruct, establishing a substantial performance gap.
pip install llm-blender
# pip install git+https://github.com/yuchenlin/LLM-Blender.gitThen you are good to go through our LLM-Blender with import llm_blender.
For development, you can clone the repo and install it locally.
git clone https://github.com/yuchenlin/LLM-Blender.git
cd LLM-Blender
pip install -e .import llm_blender
blender = llm_blender.Blender()
blender.loadranker("llm-blender/PairRM") # load ranker checkpoint- Then you can rank with the following function
inputs = ["hello, how are you!", "I love you!"]
candidates_texts = [["get out!", "hi! I am fine, thanks!", "bye!"],
["I love you too!", "I hate you!", "Thanks! You're a good guy!"]]
ranks = blender.rank(inputs, candidates_texts, return_scores=False, batch_size=1)
# ranks is a list of ranks where ranks[i][j] represents the ranks of candidate-j for input-i
"""
ranks -->
array([[3, 1, 2], # it means "hi! I am fine, thanks!" ranks the 1st, "bye" ranks the 2nd, and "get out!" ranks the 3rd.
[1, 3, 2]], # it means "I love you too"! ranks the the 1st, and "I hate you!" ranks the 3rd.
dtype=int32)
"""- Using llm-blender to directly compare two candidates
inputs = ["hello!", "I love you!"]
candidates_A = ["hi!", "I hate you!"]
candidates_B = ["f**k off!", "I love you, too!"]
comparison_results = blender.compare(inputs, candidates_A, candidates_B)
# comparison_results is a list of bool, where comparison_results[i] denotes whether candidates_A[i] is better than candidates_B[i] for inputs[i]
# comparison_results[0]--> True - You can also fuse the top-ranked candidates with the following code
blender.loadfuser("llm-blender/gen_fuser_3b") # load fuser checkpoint if you want to use pre-trained fuser; or you can use ranker only
from llm_blender.blender.blender_utils import get_topk_candidates_from_ranks
topk_candidates = get_topk_candidates_from_ranks(ranks, candidates_texts, top_k=3)
fuse_generations = blender.fuse(inputs, topk_candidates, batch_size=2)
# fuse_generations are the fused generations from our fine-tuned checkpoint
# You can also do the rank and fusion with a single function
fuse_generations, ranks = blender.rank_and_fuse(inputs, candidates_texts, return_scores=False, batch_size=2, top_k=3)Best-of-n Sampling, aka, rejection sampling, is a strategy to enhance the response quality by selecting the one that was ranked highest by the reward model (Learn more atOpenAI WebGPT section 3.2 and OpenAI Blog).
Best-of-n sampling is a easy way to improve your LLMs by sampling and re-ranking with just a few lines of code. An example of applying on Zephyr-7b is as follows.
import llm_blender
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta", device_map="auto")
system_message = {"role": "system", "content": "You are a friendly chatbot."}
inputs = ["can you tell me a joke about OpenAI?"]
messages = [[system_message, {"role": "user", "content": _input}] for _input in inputs]
prompts = [tokenizer.apply_chat_template(m, tokenize=False, add_generation_prompt=True) for m in messages]
# standard sampling generation
input_ids = tokenizer(prompts[0], return_tensors="pt").input_ids
sampled_outputs = model.generate(input_ids, do_sample=True, top_k=50, top_p=0.95, num_return_sequences=1)
print(tokenizer.decode(sampled_outputs[0][len(input_ids[0]):], skip_special_tokens=False))
# --> `Sure`
# using our PairRM for best-of-n sampling
blender = llm_blender.Blender()
blender.loadranker("llm-blender/PairRM") # load ranker checkpoint
outputs = blender.best_of_n_generate(model, tokenizer, prompts, n=10)
print("### Prompt:")
print(prompts[0])
print("### best-of-n generations:")
print(outputs[0])
# -->
"""
Sure, here's a joke about OpenAI:
Why did OpenAI decide to hire a mime as their new AI researcher?
Because they wanted someone who could communicate complex ideas without making a sound!
(Note: This is a joke, not a reflection of OpenAI's actual hiring practices.)
"""Our latest 🤗PairRM, which has been further trained on various high-quality and large-scale dataset with human preference annotations, has shown great correlation with human preferences with an extremely small model size (0.4B), approaching the performance of GPT-4. (See detailed comparison in 🤗PairRM)
To get scalar rewards, you can use blender.rank_with_ref method (see the example below). This method compares all the candidates with the reference and returns the relative scalar rewards.
import llm_blender
blender = llm_blender.Blender()
blender.loadranker("llm-blender/PairRM") # load ranker checkpoint
inputs = ["hello, how are you!", "I love you!"]
candidates_texts = [["get out!", "hi! I am fine, thanks!", "bye!"],
["I love you too!", "I hate you!", "Thanks! You're a good guy!"]]
rewards = blender.rank_with_ref(inputs, candidates_texts, return_scores=True, batch_size=2, mode="longest")
print("Rewards for input 1:", rewards[0]) # rewards of candidates for input 1
"""
rewards is a List[List[float]] of shape (len(inputs), len(candidates_texts[0])).
representing the rewards of each candidate for each input.
By default, the rewards are calculated based on the the comparison with the longest generation as a reference.(mode="longest").
other supported modes are "shortest" "median_length" "first" "last"
"""You can also pass a list of references to compare with, instead of automatically selecting one from the candidates as the fixed reference.
ref_candidates = [_c[0] for _c in candidates_texts] # use the first candidate as the reference, same as mode="first"
rewards = blender.rank_with_ref(inputs, candidates_texts, return_scores=True, batch_size=2, ref_candidates=ref_candidates)
"""
ref_candidates = [ref1, ref2, ref3, ...] # ref_candidates is a List[str], shape (len(inputs),)
this parameter will override the mode parameter, and use the ref_candidates as the reference for reward calculation.
rewards is a List[List[float]] of shape (len(inputs), len(candidates_texts[0])).
"""You can easily integrate PairRM to popular RLHF toolkits like trl.
PairRM's blender.compare naturally supports DPO, which is a direct preference optimization method to optimize the model with the pairwise comparison signal.
In this way, you don't need to install llm-blender to use PairRM. More custom development can be achived based on the model
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from llm_blender.pair_ranker.pairrm import DebertaV2PairRM # or copy the DebertaV2PairRM definition here, https://github.com/yuchenlin/LLM-Blender/blob/main/llm_blender/pair_ranker/pairrm.py
from transformers import AutoTokenizer
from typing import List
pairrm = DebertaV2PairRM.from_pretrained("llm-blender/PairRM-hf", device_map="cuda:0").eval()
tokenizer = AutoTokenizer.from_pretrained('llm-blender/PairRM-hf')
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=1224, candidate_max_length=412):
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)
# [1.9003021717071533, -1.2547134160995483]
print(comparison_results)
# tensor([ True, False], device='cuda:0'), which means whether candidate A is better than candidate B for each input🔥 Check more details on our example Jupyter notebook usage: blender_usage.ipynb
- To facilitate large-scale evaluation, we introduce a benchmark dataset, MixInstruct, which is a mixture of multiple instruction datasets featuring oracle pairwise comparisons for testing purposes.
- MixInstruct is the first large-scale dataset consisting of responses from 11 popular open-source LLMs on the instruction-following dataset. Each split of train/val/test contains 100k/5k/5k examples.
- MixInstruct instruct is collected from 4 famous instruction dataset: Alpaca-GPT4, Dolly-15k, GPT4All-LAION and ShareGPT. The ground-truth outputs comes from either ChatGPT, GPT-4 or human annotations.
- MixInstruct is evaluated by both auto-metrics including BLEURT, BARTScore, BERTScore, etc. and ChatGPT. We provide 4771 examples on test split that is evaluated by ChatGPT through pairwise comparison.
- Code to construct the dataset:
get_mixinstruct.py - HuggingFace 🤗 Dataset link
Train PairRanker
# installation
pip install -e .[train]See more details in train_ranker.sh
Please follow the guide in the script to train the ranker.
Here are some explanations for the script parameters:
Changing the torchrun cmd
TORCHRUN_CMD=<you torchrun cmd path>Normally, it's just torchrun with proper conda env activated.
Changing the dataset
dataset="<your dataset>`Changing the ranker backbone
backbone_type="deberta" # "deberta" or "roberta"
backbone_name="microsoft/deberta-v3-large" # "microsoft/deberta-v3-large" or "roberta-large"Changing the ranker type
ranker="Pairranker" # "PairRanker" or "Summaranker" or "SimCLS"Filter the candidates used
candidate_model="flan-t5-xxl" # or "alpaca-native"
candidate_decoding_method="top_p_sampling"
n_candidates=15 # number of candidates to generate
using_metrics="rouge1,rouge2,rougeLsum,bleu" # metrics used to train the signalDo Training or Inference
do_inference=False # training
do_inference=True # inferenceWhen doing inference, you can change inference_mode to bubble or full to select difference pairwise inference model
Limit the datasize used for training, dev and test
max_train_data_size=-1 # -1 means no limit
max_eval_data_size=-1 # -1 means no limit
max_predict_data_size=-1 # -1 means no limitDo inference on dataset A with ranker training on dataset B
dataset=<A>
checkpoint_trained_dataset=<B>
do_inference=TrueToolkits
- LLM-Gen: A simple generation script used to get large-scale responses from various large language models.
Model checkpoints
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🤗PairRanker checkpoint fine-tuned on DeBERTa-v3-Large (304m)
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🤗GenFuser checkpoint fine-tuned on Flan-T5-XL (3b)
PairRM has been widely used in various applications, including but not limited to:
- snorkelai/Snorkel-Mistral-PairRM-DPO (SOTA in Alpaca-eval leaderboard)
- argilla/OpenHermesPreferences (1M+ preference datasets annotated by PairRM)
We are looking forward to more applications and contributions from the community 🤗!
@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"
}
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