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--- |
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license: llama2 |
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datasets: |
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- wyt2000/InverseCoder-CL-13B-Evol-Instruct-90K |
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- ise-uiuc/Magicoder-Evol-Instruct-110K |
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library_name: transformers |
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pipeline_tag: text-generation |
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tags: |
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- code |
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model-index: |
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- name: InverseCoder-CL-13B |
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results: |
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- task: |
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type: text-generation |
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dataset: |
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type: openai_humaneval |
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name: HumanEval |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 0.799 |
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verified: false |
|
- task: |
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type: text-generation |
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dataset: |
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type: openai_humaneval |
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name: HumanEval(+) |
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metrics: |
|
- name: pass@1 |
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type: pass@1 |
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value: 0.744 |
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verified: false |
|
- task: |
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type: text-generation |
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dataset: |
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type: mbpp |
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name: MBPP |
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metrics: |
|
- name: pass@1 |
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type: pass@1 |
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value: 0.746 |
|
verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
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type: mbpp |
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name: MBPP(+) |
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metrics: |
|
- name: pass@1 |
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type: pass@1 |
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value: 0.630 |
|
verified: false |
|
- task: |
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type: text-generation |
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dataset: |
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type: ds1000 |
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name: DS-1000 (Overall Completion) |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 0.431 |
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verified: false |
|
- task: |
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type: text-generation |
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dataset: |
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type: nuprl/MultiPL-E |
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name: MultiPL-HumanEval (Java) |
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metrics: |
|
- name: pass@1 |
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type: pass@1 |
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value: 0.545 |
|
verified: false |
|
- task: |
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type: text-generation |
|
dataset: |
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type: nuprl/MultiPL-E |
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name: MultiPL-HumanEval (JavaScript) |
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metrics: |
|
- name: pass@1 |
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type: pass@1 |
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value: 0.654 |
|
verified: false |
|
- task: |
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type: text-generation |
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dataset: |
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type: nuprl/MultiPL-E |
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name: MultiPL-HumanEval (C++) |
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metrics: |
|
- name: pass@1 |
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type: pass@1 |
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value: 0.581 |
|
verified: false |
|
- task: |
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type: text-generation |
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dataset: |
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type: nuprl/MultiPL-E |
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name: MultiPL-HumanEval (PHP) |
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metrics: |
|
- name: pass@1 |
|
type: pass@1 |
|
value: 0.553 |
|
verified: false |
|
- task: |
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type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
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name: MultiPL-HumanEval (Swift) |
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metrics: |
|
- name: pass@1 |
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type: pass@1 |
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value: 0.525 |
|
verified: false |
|
- task: |
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type: text-generation |
|
dataset: |
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type: nuprl/MultiPL-E |
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name: MultiPL-HumanEval (Rust) |
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metrics: |
|
- name: pass@1 |
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type: pass@1 |
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value: 0.556 |
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verified: false |
|
- task: |
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type: text-generation |
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dataset: |
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type: nuprl/MultiPL-E |
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name: MultiPL-HumanEval (Average for non-python languages) |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 0.569 |
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verified: false |
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--- |
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<div align="center"> |
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<img src="https://huggingface.co/wyt2000/InverseCoder-CL-7B/resolve/main/assets/logo.png" style="zoom:25%;" /> |
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</div> |
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|
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# InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct |
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|
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<img src="https://huggingface.co/wyt2000/InverseCoder-CL-7B/resolve/main/assets/overview.png" style="zoom:50%;" /> |
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InverseCoder is a series of code LLMs instruction-tuned by generating data from itself through Inverse-Instruct. |
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## Models and Datasets |
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| | Base Model | InverseCoder | Dataset | |
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| --- | ---------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------ | |
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| 6.7B | [deepseek-ai/deepseek-coder-6.7b-base](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base) | [wyt2000/InverseCoder-DS-6.7B](https://huggingface.co/wyt2000/InverseCoder-DS-6.7B) | [wyt2000/InverseCoder-DS-6.7B-Evol-Instruct-90K](https://huggingface.co/datasets/wyt2000/InverseCoder-DS-6.7B-Evol-Instruct-90K) | |
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| 7B | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | [wyt2000/InverseCoder-CL-7B](https://huggingface.co/wyt2000/InverseCoder-CL-7B) | [wyt2000/InverseCoder-CL-7B-Evol-Instruct-90K](https://huggingface.co/datasets/wyt2000/InverseCoder-CL-7B-Evol-Instruct-90K) | |
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| 13B | [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) | [wyt2000/InverseCoder-CL-13B](https://huggingface.co/wyt2000/InverseCoder-CL-13B) **<= You are here** | [wyt2000/InverseCoder-CL-13B-Evol-Instruct-90K](https://huggingface.co/datasets/wyt2000/InverseCoder-CL-13B-Evol-Instruct-90K) | |
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|
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## Usage |
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Similar to [Magicoder-S-DS-6.7B](https://huggingface.co/ise-uiuc/Magicoder-S-DS-6.7B/), use the code below to get started with the model. Make sure you installed the [transformers](https://huggingface.co/docs/transformers/index) library. |
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```python |
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from transformers import pipeline |
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import torch |
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INVERSECODER_PROMPT = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions. |
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@@ Instruction |
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{instruction} |
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@@ Response |
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""" |
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instruction = <Your code instruction here> |
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prompt = INVERSECODER_PROMPT.format(instruction=instruction) |
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generator = pipeline( |
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model="wyt2000/InverseCoder-CL-13B", |
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task="text-generation", |
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torch_dtype=torch.bfloat16, |
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device_map="auto", |
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) |
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result = generator(prompt, max_length=1024, num_return_sequences=1, temperature=0.0) |
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print(result[0]["generated_text"]) |
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``` |
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## Paper |
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**Arxiv:** <https://arxiv.org/abs/2407.05700> |
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|
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Please cite the paper if you use the models or datasets from InverseCoder. |
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|
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``` |
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@misc{wu2024inversecoderunleashingpowerinstructiontuned, |
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title={InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct}, |
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author={Yutong Wu and Di Huang and Wenxuan Shi and Wei Wang and Lingzhe Gao and Shihao Liu and Ziyuan Nan and Kaizhao Yuan and Rui Zhang and Xishan Zhang and Zidong Du and Qi Guo and Yewen Pu and Dawei Yin and Xing Hu and Yunji Chen}, |
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year={2024}, |
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eprint={2407.05700}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2407.05700}, |
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} |
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``` |
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|
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## Code |
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|
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[Official code repo](https://github.com/wyt2000/InverseCoder) for Inverse-Instruct (under development). |
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|
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## Acknowledgements |
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|
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* [Magicoder](https://github.com/ise-uiuc/magicoder): Training code, original datasets and data decontamination |
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* [DeepSeek-Coder](https://github.com/deepseek-ai/DeepSeek-Coder): Base model for InverseCoder-DS |
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* [CodeLlama](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/): Base model for InverseCoder-CL |
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* [AutoMathText](https://github.com/yifanzhang-pro/AutoMathText): Self-evaluation and data selection method |
