---
license: mit
datasets:
- openai/summarize_from_feedback
- openai/webgpt_comparisons
- Dahoas/instruct-synthetic-prompt-responses
- Anthropic/hh-rlhf
- lmsys/chatbot_arena_conversations
- openbmb/UltraFeedback
metrics:
- accuracy
tags:
- pair-ranker
- pair_ranker
- reward_model
- reward-model
- pairrm
- pair-rm
- RLHF
language:
- en
---

Inspired by [DeBERTa Reward Model Series](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large-v2) 
`llm-blender/PairRM` is pairranker version finetuned specifically as a reward model using deberta-v3-large. 

- Github: [https://github.com/yuchenlin/LLM-Blender](https://github.com/yuchenlin/LLM-Blender)
- Paper: [https://arxiv.org/abs/2306.02561](https://arxiv.org/abs/2306.02561)

## 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/) (This model) | 1224              | 412                  | 2048             |

### Performance


## Usage Example

### Installation
Since PairRanker contains some custom layers and tokens. We recommend use PairRM with our llm-blender code API.
- First install `llm-blender`
```bash
pip install git+https://github.com/yuchenlin/LLM-Blender.git
```

- Then load pairranker with the following code:
```python
import llm_blender
blender = llm_blender.Blender()
blender.loadranker("llm-blender/PairRM") # load PairRM
```

### Use case 1: Compare responses (Quality Evaluator)

- Then you can rank candidate responses with the following function

```python
inputs = ["input1", "input2"]
candidates_texts = [["candidate1 for input1", "candidatefor input1"], ["candidate1 for input2", "candidate2 for input2"]]
ranks = blender.rank(inputs, candidates_texts, return_scores=False, batch_size=2)
# ranks is a list of ranks where ranks[i][j] represents the ranks of candidate-j for input-i
```

- Directly compare two candidate responses
```python
candidates_A = [cands[0] for cands in candidates]
candidates_B = [cands[1] for cands in candidates]
comparison_results = blender.compare(inputs, candidates_A, candidates_B)
# comparison_results is a list of bool, where element[i] denotes whether candidates_A[i] is better than candidates_B[i] for inputs[i]
```

- Directly compare two multi-turn conversations given that user's query in each turn are fiexed and responses are different.
```python
conv1 = [
    {
        "content": "hello",
        "role": "USER"
    },
    {
        "content": "<assistant response>",
        "role": "ASSISTANT"
    },
    ...
]
conv2 = [
    {
        "content": "hello",
        "role": "USER"
    },
    {
        "content": "<assistant response>",
        "role": "ASSISTANT"
    },
    ...
]
comparison_results = blender.compare_conversations([conv1], [conv2])
# comparison_results is a list of bool, where each element denotes whether all the responses in conv1 together is better than that of conv2
```

### Use case 2: Best-of-n sampling (Decoding Enhancing)
**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 at[OpenAI WebGPT section 3.2](https://arxiv.org/pdf/2112.09332.pdf) and [OpenAI Blog](https://openai.com/research/measuring-goodharts-law)). 

Best-of-n sampling is a easy way to imporve your llm power with just a few lines of code. An example of applying on zephyr is as follows.

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta", device_map="auto")

inputs = [...] # your list of inputs
system_message = {
    "role": "system",
    "content": "You are a friendly chatbot who always responds in the style of a pirate",
}
messages = [
    [   
        system_message,
        {"role": "user", "content": _input},
    ]
    for _input in zip(inputs)
]
prompts = [tokenizer.apply_chat_template(m, tokenize=False, add_generation_prompt=True) for m in messages]
outputs = blender.best_of_n_generate(model, tokenizer, prompts, n=10)
print("### Prompt:")
print(prompts[0])
print("### best-of-n generations:")
print(outputs[0])
```

### Use case 3: RLHF 
PairRM has been trained on various high-quality and large-scale dataset with human preference annotations and exhibits great correlation with human preferences with an extremly small model size (0.4B), approching the performance of GPT-4. (See detailed comparison in 🤗[PairRM](https://huggingface.co/llm-blender/PairRM))
With a `blender.compare()` function, you can easily apply PairRM to poopular RLHF toolkits like [trl](https://huggingface.co/docs/trl/index). 

**🔥 Check more details on our example jupyter notebook usage: [`blender_usage.ipynb`](https://github.com/yuchenlin/LLM-Blender/blob/main/blender_usage.ipynb)**


Learn more in our LLM-Blender Github [README.md](https://github.com/yuchenlin/LLM-Blender#rank-and-fusion)

## Citation
If you are using PairRM in your research, please cite LLM-blender.
```bibtex
@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"
}

```