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--- |
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license: other |
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license_name: skywork-community-license |
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license_link: >- |
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https://github.com/SkyworkAI/Skywork-Reward/blob/main/misc/Skywork%20Community%20License.pdf |
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datasets: |
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- nvidia/HelpSteer2 |
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language: |
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- en |
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metrics: |
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- accuracy |
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base_model: |
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- Skywork/Skywork-Reward-Llama-3.1-8B-v0.2 |
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library_name: transformers |
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--- |
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# Interpreting Language Model Preferences Through the Lens of Decision Trees |
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+ **Author** [Min Li](https://min-li.github.io/) |
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+ **Blog**: https://rlhflow.github.io/posts/2025-01-22-decision-tree-reward-model/ |
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+ **Models**: |
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+ [**Decision-Tree-Reward-Gemma-2-27B**](https://huggingface.co/RLHFlow/Decision-Tree-Reward-Gemma-2-27B) |
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+ [**Decision-Tree-Reward-Llama-3.1-8B**](https://huggingface.co/RLHFlow/Decision-Tree-Reward-Llama-3.1-8B) |
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+ **Code Repository:** https://github.com/RLHFlow/RLHF-Reward-Modeling/tree/main/decision_tree |
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+ **Tech Report**: To release soon |
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## RewardBench Leaderboard (Jan 2025) |
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Rank | Model | Base Model | Method | Overall Score | Chat | Chat Hard | Safety | Reasoning | |
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|:------|:------|:-----------|:-------|:--------------|:---------|:----------|:---------|:----------| |
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1 | [**Decision-Tree-Reward-Gemma-2-27B**](https://huggingface.co/RLHFlow/Decision-Tree-Reward-Gemma-2-27B) | Gemma-2-27B | Decision Tree | **95.4** | 96.9 | **91.4** | 93.9 | **99.2** | |
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2 | INF-QRM-Llama3.1-70B | Llama-3.1-70B | Sequence Classifier | 95.1 | 96.6 | 91.0 | 93.6 | 99.1 | |
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3 | [**Decision-Tree-Reward-Llama-3.1-8B**](https://huggingface.co/RLHFlow/Decision-Tree-Reward-Llama-3.1-8B) | Llama-3.1-8B | Decision Tree | 94.5 | 96.6 | 89.5 | 93.2 | 98.6 | |
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4 | QRM-Gemma-2-27B | Gemma-2-27B | Sequence Classifier | 94.4 | 96.6 | 90.1 | 92.7 | 98.3 | |
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5 | Skywork-Reward-Gemma-2-27B-v0.2 | Gemma-2-27B | Sequence Classifier | 94.3 | 96.1 | 89.9 | 93.0 | 98.1 | |
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6 | Llama-3.1-Nemotron-70B-Reward | Llama-3.1-70B | Custom Classifier | 94.1 | 97.5 | 85.7 | **95.1** | 98.1 | |
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7 | Skywork-Reward-Gemma-2-27B | Gemma-2-27B | Sequence Classifier | 93.8 | 95.8 | **91.4** | 91.9 | 96.1 | |
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8 | TextEval-Llama3.1-70B | Llama-3.1-70B | Generative | 93.5 | 94.1 | 90.1 | 93.2 | 96.4 | |
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9 | MetaMetrics-RM-v1.0 | - | Custom Classifier | 93.4 | **98.3** | 86.4 | 90.8 | 98.2 | |
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10 | Skywork-Critic-Llama-3.1-70B | Llama-3.1-70B | Generative | 93.3 | 96.6 | 87.9 | 93.1 | 95.5 | |
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11 | QRM-Llama3.1-8B-v2 | Llama-3.1-8B | Sequence Classifier | 93.1 | 96.4 | 86.8 | 92.6 | 96.8 | |
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12 | Skywork-Reward-Llama-3.1-8B-v0.2 | Llama-3.1-8B | Sequence Classifier | 93.1 | 94.7 | 88.4 | 92.7 | 96.7 | |
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## Usage Code |
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Before using the model, ensure you have the following dependencies installed: |
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- `transformers==4.45.2` |
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- `torch>=2.5.0` |
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- `flash-attn>=2.6.3` |
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Note: This code requires a GPU with NVIDIA Ampere architecture or newer. |
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```python |
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from transformers import AutoModelForSequenceClassification |
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import torch |
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from transformers import AutoTokenizer |
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model_name = "Decision-Tree-Reward-Llama-3.1-8B" # Another choice is "Decision-Tree-Reward-Gemma-2-27B" |
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repo_id = f"RLHFlow/{model_name}" |
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device = "cuda" |
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# Initialize the model and tokenizer |
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model = AutoModelForSequenceClassification.from_pretrained(repo_id, trust_remote_code=True, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2", device_map=device) |
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tokenizer = AutoTokenizer.from_pretrained(repo_id, use_fast=True) |
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# Load the decision tree |
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model.load_decision_tree(repo_id, filename="decision_tree.pkl") |
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# Prompt and response pairs |
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prompt = "Jane has 12 apples. She gives 4 apples to her friend Mark, then buys 1 more apple, and finally splits all her apples equally among herself and her 2 siblings. How many apples does each person get?" |
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response1 = "1. Jane starts with 12 apples and gives 4 to Mark. 12 - 4 = 8. Jane now has 8 apples.\n2. Jane buys 1 more apple. 8 + 1 = 9. Jane now has 9 apples.\n3. Jane splits the 9 apples equally among herself and her 2 siblings (3 people in total). 9 ÷ 3 = 3 apples each. Each person gets 3 apples." |
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response2 = "1. Jane starts with 12 apples and gives 4 to Mark. 12 - 4 = 8. Jane now has 8 apples.\n2. Jane buys 1 more apple. 8 + 1 = 9. Jane now has 9 apples.\n3. Jane splits the 9 apples equally among her 2 siblings (2 people in total). 9 ÷ 2 = 4.5 apples each. Each person gets 4 apples." |
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# Compare the two responses |
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output = model.compare(prompt, response1, response2, tokenizer, device) |
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print("Response 1 rewards") |
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print(dict(zip(output["attributes"], output["rewards"][0]))) |
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# {'helpfulness': 3.9603815, 'correctness': 3.9727726, 'coherence': 3.8582935, 'complexity': 0.9909791, 'verbosity': 1.4901903} |
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print("Response 2 rewards") |
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print(dict(zip(output["attributes"], output["rewards"][1]))) |
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# {'helpfulness': 2.1698856, 'correctness': 2.2035594, 'coherence': 3.2032843, 'complexity': 0.8786768, 'verbosity': 1.4569137} |
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print("Model preference") |
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print(output["preference"]) |
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# 0 |
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``` |
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## License |
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**Note**: This model is finetuned from a Skywork model under the following license agreement: |
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The community usage of Skywork model requires [Skywork Community License](https://github.com/SkyworkAI/Skywork-Reward/blob/main/misc/Skywork%20Community%20License.pdf). The Skywork model supports commercial use. If you plan to use the Skywork model or its derivatives for commercial purposes, you must abide by terms and conditions within [Skywork Community License](https://github.com/SkyworkAI/Skywork-Reward/blob/main/misc/Skywork%20Community%20License.pdf). |
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## To-Do |
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+ [ ] Reward Model Usage code |
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+ [ ] Architecture diagram |
