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--- |
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pipeline_tag: text-generation |
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inference: true |
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widget: |
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- text: 'Question: Please write a function in Python that performs bubble sort.\n\nAnswer:' |
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example_title: Bubble sort |
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group: Python |
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license: bigcode-openrail-m |
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datasets: |
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- bigcode/commitpackft |
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- bigcode/oasst-octopack |
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metrics: |
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- code_eval |
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library_name: transformers |
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tags: |
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- code |
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model-index: |
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- name: OctoCoder |
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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: bigcode/humanevalpack |
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name: HumanEvalSynthesize Python |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 46.2 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalSynthesize JavaScript |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 39.2 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalSynthesize Java |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 38.2 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalSynthesize Go |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 30.4 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalSynthesize C++ |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 35.6 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalSynthesize Rust |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 23.4 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalSynthesize Average |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 35.5 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalFix Python |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 30.4 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalFix JavaScript |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 28.4 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalFix Java |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 30.6 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalFix Go |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 30.2 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalFix C++ |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 26.1 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalFix Rust |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 16.5 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalFix Average |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 27.0 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalExplain Python |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 35.1 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalExplain JavaScript |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 24.5 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalExplain Java |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 27.3 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalExplain Go |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 21.1 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalExplain C++ |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 24.1 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalExplain Rust |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 14.8 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalExplain Average |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 24.5 |
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verified: false |
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--- |
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# Table of Contents |
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1. [Model Summary](#model-summary) |
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2. [Use](#use) |
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3. [Training](#training) |
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4. [Citation](#citation) |
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# Model Summary |
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> OctoCoder is an instruction tuned model with 15.5B parameters created by finetuning StarCoder on CommitPackFT & OASST as described in the OctoPack paper. |
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- **Repository:** [bigcode-project/octopack](https://github.com/bigcode-project/octopack) |
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- **Paper:** [OctoPack: Instruction Tuning Code Large Language Models](https://arxiv.org/abs/2308.07124) |
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- **Languages:** 80+ Programming languages |
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- **OctoPack🐙🎒:** |
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<table> |
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<tr> |
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<th>Data</t> |
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<th><a href=https://huggingface.co/datasets/bigcode/commitpack>CommitPack</a></th> |
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<td>4TB of GitHub commits across 350 programming languages</td> |
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</tr> |
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<tr> |
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<th></t> |
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<th><a href=https://huggingface.co/datasets/bigcode/commitpackft>CommitPackFT</a></th> |
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<td>Filtered version of CommitPack for high-quality commit messages that resemble instructions</td> |
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</tr> |
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<tr> |
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<th>Model</t> |
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<th><a href=https://huggingface.co/bigcode/octocoder>OctoCoder</a></th> |
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<td>StarCoder (16B parameters) instruction tuned on CommitPackFT + OASST</td> |
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</tr> |
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<tr> |
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<th></t> |
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<th><a href=https://huggingface.co/bigcode/octogeex>OctoGeeX</a></th> |
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<td>CodeGeeX2 (6B parameters) instruction tuned on CommitPackFT + OASST</td> |
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</tr> |
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<tr> |
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<th>Evaluation </t> |
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<th><a href=https://huggingface.co/datasets/bigcode/humanevalpack>HumanEvalPack</a></th> |
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<td>Extension of OpenAI's HumanEval to cover 3 scenarios across 6 languages</td> |
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</tr> |
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</table> |
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# Use |
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## Intended use |
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The model follows instructions provided in the input. You should always preface your input with "Question: " and finish it with "Answer:", for example: "Question: Please write a function in Python that performs bubble sort.\n\nAnswer:" |
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**Feel free to share your generations in the Community tab!** |
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## Generation |
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```python |
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# pip install -q transformers |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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checkpoint = "bigcode/octocoder" |
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device = "cuda" # for GPU usage or "cpu" for CPU usage |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) |
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inputs = tokenizer.encode("Question: Please write a function in Python that performs bubble sort.\n\nAnswer:", return_tensors="pt").to(device) |
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outputs = model.generate(inputs) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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# Training |
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## Model |
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- **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective |
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- **Steps:** 250k pretraining & 30 instruction tuning |
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- **Pretraining tokens:** 1 trillion pretraining & 2M instruction tuning |
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- **Precision:** bfloat16 |
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## Hardware |
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- **Pretraining:** |
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- **GPUs:** 512 Tesla A100 |
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- **Training time:** 24 days |
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- **Instruction tuning:** |
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- **GPUs:** 8 Tesla A100 |
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- **Training time:** 4 hours |
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## Software |
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- **Orchestration:** [Megatron-LM/Transformers](https://github.com/bigcode-project/octopack#training) |
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- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) |
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# Citation |
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```bibtex |
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@article{muennighoff2023octopack, |
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title={OctoPack: Instruction Tuning Code Large Language Models}, |
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author={Niklas Muennighoff and Qian Liu and Armel Zebaze and Qinkai Zheng and Binyuan Hui and Terry Yue Zhuo and Swayam Singh and Xiangru Tang and Leandro von Werra and Shayne Longpre}, |
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journal={arXiv preprint arXiv:2308.07124}, |
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year={2023} |
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} |
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``` |
