Update README.md

README.md

@@ -1,5 +1,197 @@
----
-license: other
-license_name: deepseek
-license_link: LICENSE
----
+---
+license: other
+datasets:
+- wyt2000/InverseCoder-DS-6.7B-Evol-Instruct-90K
+- ise-uiuc/Magicoder-Evol-Instruct-110K
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- code
+model-index:
+- name: InverseCoder-DS-6.7B
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      type: openai_humaneval
+      name: HumanEval
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.799
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: openai_humaneval
+      name: HumanEval(+)
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.768
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: mbpp
+      name: MBPP
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.786
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: mbpp
+      name: MBPP(+)
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.690
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: ds1000
+      name: DS-1000 (Overall Completion)
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.442
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (Java)
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.607
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (JavaScript)
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.701
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (C++)
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.705
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (PHP)
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.636
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (Swift)
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.530
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (Rust)
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.574
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (Average for non-python languages)
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.626
+      verified: false
+---
+<div align="center">
+  <img src="https://huggingface.co/wyt2000/InverseCoder-CL-7B/resolve/main/assets/logo.png" style="zoom:25%;" /> 
+</div>
+
+# InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct
+
+<img src="https://huggingface.co/wyt2000/InverseCoder-CL-7B/resolve/main/assets/overview.png" style="zoom:50%;" /> 
+
+InverseCoder is a series of code LLMs instruction-tuned by generating data from itself through Inverse-Instruct.
+
+## Models and Datasets
+|     | Base Model                                                                                           | InverseCoder                                                                                      | Dataset                                                                                                                              |
+| --- | ---------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------ |
+| 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) **<= You are here** | [wyt2000/InverseCoder-DS-6.7B-Evol-Instruct-90K](https://huggingface.co/datasets/wyt2000/InverseCoder-DS-6.7B-Evol-Instruct-90K)     |
+| 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)       |
+| 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)  | [wyt2000/InverseCoder-CL-13B-Evol-Instruct-90K](https://huggingface.co/datasets/wyt2000/InverseCoder-CL-13B-Evol-Instruct-90K)       |
+
+## Usage
+
+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.
+
+```python
+from transformers import pipeline
+import torch
+INVERSECODER_PROMPT = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
+@@ Instruction
+{instruction}
+@@ Response
+"""
+instruction = <Your code instruction here>
+prompt = INVERSECODER_PROMPT.format(instruction=instruction)
+generator = pipeline(
+    model="wyt2000/InverseCoder-DS-6.7B",
+    task="text-generation",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+result = generator(prompt, max_length=1024, num_return_sequences=1, temperature=0.0)
+print(result[0]["generated_text"])
+```
+
+## Paper
+**Arxiv:** <https://arxiv.org/abs/2407.05700>
+
+Please cite the paper if you use the models or datasets from InverseCoder.
+
+```
+@misc{wu2024inversecoderunleashingpowerinstructiontuned,
+      title={InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct}, 
+      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},
+      year={2024},
+      eprint={2407.05700},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2407.05700}, 
+}
+```
+
+## Acknowledgements
+
+* [Magicoder](https://github.com/ise-uiuc/magicoder): Training code, original datasets and data decontamination
+* [DeepSeek-Coder](https://github.com/deepseek-ai/DeepSeek-Coder): Base model for InverseCoder-DS
+* [CodeLlama](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/): Base model for InverseCoder-CL
+* [AutoMathText](https://github.com/yifanzhang-pro/AutoMathText): Self-evaluation and data selection method