| --- |
| license: mit |
| datasets: |
| - Anthropic/hh-rlhf |
| language: |
| - en |
| base_model: |
| - Qwen/Qwen2.5-3B |
| pipeline_tag: text-classification |
| library_name: transformers |
| tags: |
| - reward-model |
| - 3b |
| - RLHF |
| --- |
| |
| # Qwen-2.5-3B Reward Model |
|
|
| This is a **3B reward model fine-tuned from Qwen 2.5 3B** using Anthropic HH-RLHF data. |
| It is designed to score model outputs for alignment and quality, and can be used with **RewardBench** for evaluation. |
|
|
| ## Eval Results (RewardBench) |
| |
|
|
|
|
| | Category | Score | |
| |-----------------|-------| |
| | Chat | 83.5% | |
| | Chat Hard | 53.2% | |
| | Safety | 72.2% | |
| | Reasoning | 73.4% | |
|
|
| ### Sub-benchmarks |
| - alpacaeval-easy: 0.82 |
| - alpacaeval-hard: 0.874 |
| - hep-python: 0.835 |
| - mt-bench-easy: 0.893 |
| - refusals-offensive: 0.91 |
|
|
| --- |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer |
| |
| tokenizer = AutoTokenizer.from_pretrained("kanishkez/Reward-Model") |
| model = AutoModelForSequenceClassification.from_pretrained("kanishkez/Reward-Model") |
