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Model: KAKA22/CodeRM-8B Source: Original Platform
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README.md
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---
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license: apache-2.0
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datasets:
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- KAKA22/CodeRM-UnitTest
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language:
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- en
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base_model:
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- meta-llama/Llama-3.1-8B-Instruct
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pipeline_tag: text-generation
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tags:
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- code
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- llama
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library_name: transformers
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---
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# Introduction
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CodeRM-8B is a small yet powerful model designed to enable efficient and high-quality unit test generation.
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It is trained on a dataset of 60k high-quality synthetic Python unit tests using Llama3.1-70B-Instruct.
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These unit tests are synthesized based on two well-regarded code instruction tuning datasets:
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[CodeFeedback-Filtered-Instruction](https://huggingface.co/datasets/m-a-p/CodeFeedback-Filtered-Instruction) and the
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training set of [TACO](https://huggingface.co/datasets/BAAI/TACO).
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The training dataset used for unit test generation is openly available under
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[CodeRM-UnitTest](https://huggingface.co/datasets/KAKA22/CodeRM-UnitTest).
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For further information and details of training, refer to our paper:
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"Dynamic Scaling of Unit Tests for Code Reward Modeling" available on [arXiv](https://arxiv.org/abs/2501.01054).
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You can also visit the [homepage](https://code-reward-model.github.io/) and the github [repo](https://github.com/RUCKBReasoning/CodeRM) of the paper.
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# Model Information
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The model is trained based on [Llama3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
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# Prompt Format
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```
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Below is a question and it's corresponding code answer. Please write test cases to check the correctness of the code answer. You need to use the unittest library in Python and create a test class for testing.
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### question
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{question}
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### code solution
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{code in function format}
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Please add detailed comments to the test cases you write. You do not need to test the function's ability to throw exceptions.
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```
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# Performance
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## Best-of-N
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First, we evaluate the performance of CodeRM-8B using a best-of-N setting. In this setup, an LLM (policy model) generates
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100 candidate code solutions for a given programming problem, while another LLM (reward model) generates 100 unit
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tests. The optimal code solution is then selected based on majority voting derived from the execution results
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of these unit tests.
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Under this framework, our trained unit test generator demonstrates performance comparable to Llama3.1-70B-Instruct,
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despite having an 8x smaller parameter size. The detailed evaluation results across three well-known benchmarks
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are as follows:
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| Model | Policy: Llama3-8B | Policy: Llama3-70B | Policy: GPT-3.5 | Policy: GPT-4o-mini |
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| :------ | :------ | :------ | :------ | :------ |
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| **Benchmark: HumanEval Plus** |||||
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| Vanilla | 53.58 | 73.74 | 67.83 | 82.96 |
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| Reward: Llama3.1-8B | 66.84 (+13.26) | 77.14 (+3.40) | 76.32 (+8.49) | 83.11 (+0.15) |
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| Reward: Llama3.1-70B | **72.04 (+18.46)** | <u>78.54 (+4.80</u>) | **79.76 (+11.93)** | <u>85.45 (+2.49</u>) |
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| Reward: CodeRM-8B | <u>72.01 (+18.43</u>) | **78.69 (+4.95)** | <u>78.01 (+10.18</u>) | **86.38 (+3.42)** |
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| **Benchmark: MBPP Plus** |||||
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| Vanilla | 49.20 | 69.33 | 70.53 | 71.59 |
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| Reward: Llama3.1-8B | 64.31 (+15.11) | 71.64 (+2.31) | 74.18 (+3.65) | 74.48 (+2.89) |
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| Reward: Llama3.1-70B | <u>65.26 (+16.06</u>) | <u>71.85 (+2.52</u>) | <u>75.72 (+5.19</u>) | <u>74.96 (+3.37</u>) |
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| Reward: CodeRM-8B | **66.71 (+17.51)** | **72.44 (+3.11)** | **75.96 (+5.43)** | **75.20 (+3.61)** |
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| **Benchmark: LiveCodeBench** |||||
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| Vanilla | 11.98 | 25.30 | 20.55 | 34.83 |
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| Reward: Llama3.1-70B | <u>13.28 (+1.30</u>) | **28.46 (+3.16)** | **22.80 (+2.25)** | <u>38.60 (+3.77</u>) |
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| Reward: CodeRM-8B | **15.21 (+3.23)** | <u>27.73 (+2.43</u>)| <u>21.76 (+1.21</u>) | **39.20 (+4.37)** |
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## Quality of Unit Test
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We evaluate the quality of the unit test generated by CodeRM-8B. As each unit test functions as a classifier to
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determine correct or incorrect solutions, we first utilize accuracy and F1 score as metrics to assess the
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classification performance of the unit test.
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We further propose two new metrics to detailed evaluate the possibility of the unit test making incorrect judgments.
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False Acceptance Rate (FAR) measures the probability of wrong solutions being accepted by unit tests.
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False Rejection Rate (FRR) measures the probability of correct solutions being rejected by unit tests.
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The calculation formulas for these four metrics are introduced in Appendix D of the paper.
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Below is the quality of individual unit tests and the combination of multiple unit tests on HumanEval Plus,
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utilizing Llama3.1-8B as the policy model. The top two performances are marked in **bold** and _underlined_.
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| **Model** | **Acc (↑)** | **F1 (↑)** | **FAR (↓)** | **FRR (↓)** |
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|----------------------|---------------|---------------|---------------|---------------|
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| **Quality of Individual Unit Tests** | | | | |
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| Llama3.1-8B | 60.02 | 44.97 | 13.66 | 46.13 |
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| Llama3.1-70B | **73.65** | **70.15** | **11.10** | **34.51** |
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| *CodeRM-8B (Ours)* | <u>69.64</u> | <u>63.63</u> | <u>11.17</u> | <u>38.55</u> |
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| **Quality of Multiple Unit Tests** | | | | |
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| Llama3.1-8B | 74.21 | 74.35 | 20.44 | 30.55 |
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| Llama3.1-70B | <u>78.30</u> | <u>78.76</u> | <u>17.19</u> | <u>25.97</u> |
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| *CodeRM-8B (Ours)* | **80.46** | **81.27** | **16.48** | **22.71** |
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# Citation
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If you find our model helpful, please cite the original paper:
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```
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@misc{ma2025coderm,
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title={Dynamic Scaling of Unit Tests for Code Reward Modeling},
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author={Zeyao Ma and Xiaokang Zhang and Jing Zhang and Jifan Yu and Sijia Luo and Jie Tang},
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year={2025},
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eprint={2501.01054},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2501.01054},
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}
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```
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# Contact
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If you have any problems, feel free to raise an issue or reach out to us via email at: <zeyaoma@gmail.com>.
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config.json
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"vocab_size": 128256
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}
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||||
"model.norm.weight": "model-00004-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
17
special_tokens_map.json
Normal file
17
special_tokens_map.json
Normal file
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "<|eot_id|>"
|
||||
}
|
||||
410568
tokenizer.json
Normal file
410568
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2064
tokenizer_config.json
Normal file
2064
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
6907
trainer_state.json
Normal file
6907
trainer_state.json
Normal file
File diff suppressed because it is too large
Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:cc17c85a679518bddf484319a8822643fd6de02bd6dcf0399d5150823e03e4ed
|
||||
size 6584
|
||||
Reference in New Issue
Block a user