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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: 'def print_hello_world():' |
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example_title: Hello world |
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group: Python |
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license: bigscience-openrail-m |
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pretrain-datasets: |
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- books |
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- arxiv |
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- c4 |
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- falcon-refinedweb |
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- wiki |
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- github-issues |
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- stack_markdown |
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- self-made dataset of permissive github code |
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datasets: |
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- bigcode/the-stack-dedup |
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- rombodawg/2XUNCENSORED_MegaCodeTraining188k |
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- bigcode/commitpackft |
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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: Refact-1.6B |
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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 (T=0.01) |
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type: pass@1 |
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value: 32.0 |
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verified: false |
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- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: 31.5 |
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verified: false |
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- name: pass@10 (T=0.8) |
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type: pass@10 |
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value: 53.0 |
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verified: false |
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- name: pass@100 (T=0.8) |
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type: pass@100 |
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value: 76.9 |
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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 Python |
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metrics: |
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- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: 35.8 |
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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: HumanEvalSynthesize JavaScript |
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metrics: |
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- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: 31.6 |
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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 Java |
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metrics: |
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- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: 29.1 |
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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: HumanEvalSynthesize Go |
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metrics: |
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- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: -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: HumanEvalSynthesize C++ |
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metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
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value: 26.3 |
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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: HumanEvalSynthesize Rust |
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metrics: |
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- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: -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: HumanEvalSynthesize Average |
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metrics: |
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- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: -1 |
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verified: false |
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|
|
|
|
|
|
|
|
|
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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: HumanEvalFixTests Python |
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metrics: |
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- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: 18.38 |
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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: HumanEvalFixTests JavaScript |
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metrics: |
|
- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: 12.28 |
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verified: false |
|
- task: |
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type: text-generation |
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dataset: |
|
type: bigcode/humanevalpack |
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name: HumanEvalFixTests Java |
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metrics: |
|
- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: 15.12 |
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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: HumanEvalFixTests Go |
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metrics: |
|
- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: -1 |
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verified: false |
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- task: |
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type: text-generation |
|
dataset: |
|
type: bigcode/humanevalpack |
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name: HumanEvalFixTests C++ |
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metrics: |
|
- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: 13.17 |
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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: HumanEvalFixTests Rust |
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metrics: |
|
- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: 2.8 |
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verified: false |
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- task: |
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type: text-generation |
|
dataset: |
|
type: bigcode/humanevalpack |
|
name: HumanEvalFixTests Average |
|
metrics: |
|
- name: pass@1 (T=0.2) |
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type: pass@1 |
|
value: -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: HumanEvalFixDocs Python |
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metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
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value: 26.92 |
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verified: false |
|
- task: |
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type: text-generation |
|
dataset: |
|
type: bigcode/humanevalpack |
|
name: HumanEvalFixDocs JavaScript |
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metrics: |
|
- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: 26.85 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
|
type: bigcode/humanevalpack |
|
name: HumanEvalFixDocs Java |
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metrics: |
|
- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: 30.76 |
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verified: false |
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- task: |
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type: text-generation |
|
dataset: |
|
type: bigcode/humanevalpack |
|
name: HumanEvalFixDocs Go |
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metrics: |
|
- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: -1 |
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verified: false |
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- task: |
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type: text-generation |
|
dataset: |
|
type: bigcode/humanevalpack |
|
name: HumanEvalFixDocs C++ |
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metrics: |
|
- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: 25.94 |
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verified: false |
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- task: |
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type: text-generation |
|
dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalFixDocs Rust |
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metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
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value: 8.44 |
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verified: false |
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- task: |
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type: text-generation |
|
dataset: |
|
type: bigcode/humanevalpack |
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name: HumanEvalFixDocs Average |
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metrics: |
|
- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: -1 |
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verified: false |
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|
|
|
|
|
|
|
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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: |
|
- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: 26.46 |
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verified: false |
|
- task: |
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type: text-generation |
|
dataset: |
|
type: bigcode/humanevalpack |
|
name: HumanEvalExplain JavaScript |
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metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
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value: 17.86 |
|
verified: false |
|
- task: |
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type: text-generation |
|
dataset: |
|
type: bigcode/humanevalpack |
|
name: HumanEvalExplain Java |
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metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
|
value: 20.94 |
|
verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: bigcode/humanevalpack |
|
name: HumanEvalExplain Go |
|
metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
|
value: -1 |
|
verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: bigcode/humanevalpack |
|
name: HumanEvalExplain C++ |
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metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
|
value: 18.78 |
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verified: false |
|
- task: |
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type: text-generation |
|
dataset: |
|
type: bigcode/humanevalpack |
|
name: HumanEvalExplain Rust |
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metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
|
value: -1 |
|
verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: bigcode/humanevalpack |
|
name: HumanEvalExplain Average |
|
metrics: |
|
- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: -1 |
|
verified: false |
|
|
|
|
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- task: |
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type: text-generation |
|
dataset: |
|
type: mbpp |
|
name: MBPP |
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metrics: |
|
- name: pass@1 (T=0.01) |
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type: pass@1 |
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value: 31.15 |
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verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: ds1000 |
|
name: DS-1000 (Overall Completion) |
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metrics: |
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- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: 10.1 |
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verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
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name: MultiPL-HumanEval (C++) |
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metrics: |
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- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: 21.61 |
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verified: false |
|
- task: |
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type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
|
name: MultiPL-HumanEval (C#) |
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metrics: |
|
- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: 13.91 |
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verified: false |
|
- task: |
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type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
|
name: MultiPL-HumanEval (D) |
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metrics: |
|
- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: 9.5 |
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verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
|
name: MultiPL-HumanEval (Go) |
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metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
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value: 53.57 |
|
verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
|
name: MultiPL-HumanEval (Java) |
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metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
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value: 21.58 |
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verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
|
name: MultiPL-HumanEval (Julia) |
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metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
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value: 13.75 |
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verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
|
name: MultiPL-HumanEval (JavaScript) |
|
metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
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value: 26.88 |
|
verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
|
name: MultiPL-HumanEval (Lua) |
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metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
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value: 15.26 |
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verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
|
name: MultiPL-HumanEval (PHP) |
|
metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
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value: 23.04 |
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verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
|
name: MultiPL-HumanEval (Perl) |
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metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
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value: 12.1 |
|
verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
|
name: MultiPL-HumanEval (Python) |
|
metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
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value: 29.6 |
|
verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
|
name: MultiPL-HumanEval (R) |
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metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
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value: 13.77 |
|
verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
|
name: MultiPL-HumanEval (Ruby) |
|
metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
|
value: 12.68 |
|
verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
|
name: MultiPL-HumanEval (Racket) |
|
metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
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value: 4.29 |
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verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
|
name: MultiPL-HumanEval (Rust) |
|
metrics: |
|
- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: 19.54 |
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verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
|
name: MultiPL-HumanEval (Scala) |
|
metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
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value: 18.33 |
|
verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
|
name: MultiPL-HumanEval (Bash) |
|
metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
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value: 5.7 |
|
verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
|
name: MultiPL-HumanEval (Swift) |
|
metrics: |
|
- name: pass@1 (T=0.2) |
|
type: pass@1 |
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value: 17.68 |
|
verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: nuprl/MultiPL-E |
|
name: MultiPL-HumanEval (TypeScript) |
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metrics: |
|
- name: pass@1 (T=0.2) |
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type: pass@1 |
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value: 25 |
|
verified: false |
|
|
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language: |
|
- en |
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--- |
|
|
|
 |
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|
|
# Refact-1.6B |
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|
|
Finally, the model we started training with our [blog post](https://refact.ai/blog/2023/applying-recent-innovations-to-train-model/) is ready 🎉 |
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|
|
After fine-tuning on generated data, it beats Replit 3b, Stability Code 3b and many other models. It almost beats |
|
StarCoder ten times the size! |
|
|
|
|
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Model | Size | HumanEval pass@1 | HumanEval pass@10 | |
|
----------------------|---------------|--------------------|--------------------| |
|
DeciCoder-1b | 1b | 19.1% | | |
|
<b>Refact-1.6-fim</b> | <b>1.6b</b> | <b>32.0%</b> | <b>53.0%</b> | |
|
StableCode | 3b | 20.2% | 33.8% | |
|
ReplitCode v1 | 3b | 21.9% | | |
|
CodeGen2.5-multi | 7b | 28.4% | 47.5% | |
|
CodeLlama | 7b | 33.5% | 59.6% | |
|
StarCoder | 15b | 33.6% | | |
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|
|
Likely, it's the best model for practical use in your IDE for code completion because it's smart and fast! |
|
You can start using it right now by downloading the |
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[Refact plugin](https://refact.ai/). You can host the model yourself, too, using the |
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[open source docker container](https://github.com/smallcloudai/refact). |
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|
|
And it's multi-language (see MultiPL-HumanEval and other metrics below) and it works as a chat (see the section below). |
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|
|
# It Works As a Chat |
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|
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The primary application of this model is code completion (infill) in multiple programming languages. |
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But it works as a chat quite well. |
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|
|
HumanEval results using instruction following (chat) format, against models specialized for chat only: |
|
|
|
Model | Size | pass@1 | pass@10 | |
|
-----------------------|--------|----------|----------| |
|
<b>Refact-1.6-fim</b> | 1.6b | 38.4% | 55.6% | |
|
StableCode-instruct | 3b | 26.9% | 36.2% | |
|
OctoGeeX | 6b | 44.7% | | |
|
CodeLlama-instruct | 7b | 34.8% | 64.3% | |
|
CodeGen2.5-instruct | 7b | 36.2% | 60.87 | |
|
CodeLlama-instruct | 13b | 42.7% | 71.6% | |
|
StarChat-β | 15b | 33.5% | | |
|
OctoCoder | 15b | 46.2% | | |
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|
|
|
|
# Example |
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|
|
Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output: |
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|
|
```python |
|
# pip install -q transformers |
|
from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
|
checkpoint = "smallcloudai/Refact-1_6B-fim" |
|
device = "cuda" # for GPU usage or "cpu" for CPU usage |
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|
|
tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
|
model = AutoModelForCausalLM.from_pretrained(checkpoint, trust_remote_code=True).to(device) |
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|
|
prompt = '<fim_prefix>def print_hello_world():\n """<fim_suffix>\n print("Hello world!")<fim_middle>' |
|
|
|
inputs = tokenizer.encode(prompt, return_tensors="pt").to(device) |
|
outputs = model.generate(inputs, max_length=100, temperature=0.2) |
|
print("-"*80) |
|
print(tokenizer.decode(outputs[0])) |
|
``` |
|
|
|
# Chat Format |
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|
|
The same model works as chat (experimental). |
|
|
|
```python |
|
prompt_template = "<empty_output>SYSTEM {system}\n" \ |
|
"<empty_output>USER {query}\n" \ |
|
"<empty_output>ASSISTANT" |
|
prompt = prompt_template.format(system="You are a programming assistant", |
|
query="How do I sort a list in Python?") |
|
``` |
|
|
|
# Architecture |
|
|
|
As described in more detail in the blog post, we used: |
|
|
|
- [ALiBi](https://arxiv.org/abs/2108.12409) based attention |
|
- [LayerNorm](https://arxiv.org/abs/1607.06450v1) instead of [RMSNorm](https://arxiv.org/pdf/1910.07467.pdf) |
|
- [Multi Query Attention](https://arxiv.org/abs/1911.02150) |
|
|
|
We also used LiON, flash attention, early dropout. It's not that innovative that you can't run it, in fact you can -- see an example below. |
|
|
|
|
|
# Pretraining |
|
|
|
For the base model, we used our own dataset that contains code with permissive licenses only, and open text datasets. |
|
Filtering is the key to success of this model: |
|
|
|
- We only used text in English |
|
- Only topics related to computer science |
|
- Applied heavy deduplication |
|
|
|
The text to code proportion was 50:50, model trained for 1.2T tokens. |
|
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We don't release the base model, because its Fill-in-the-Middle (FIM) capability likes to repeat itself too much, so |
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its practical use is limited. But if you still want it, write us a message on Discord. |
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# Finetuning |
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We tested our hypothesis that chat data should boost base model performance in FIM and |
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regular left-to-right code completion. We found that just 15% of open |
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[code](https://huggingface.co/datasets/bigcode/commitpackft) |
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[instruction-following](https://huggingface.co/datasets/rombodawg/2XUNCENSORED_MegaCodeTraining188k) datasets, |
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that we filtered for quality, improves almost all metrics. |
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Additionally, to improve FIM, we observed common failure modes, and prepared a synthetic dataset based on |
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[The Stack dedup v1.1](https://huggingface.co/datasets/bigcode/the-stack-dedup) to address them. |
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There is a distribution shift between typical code on the internet, and the code you write in your IDE. |
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The former is likely finished, so the model tries to come up with a suggestion that makes the code complete. |
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You are likely to have half-written code as you work on it, there is no single addition that can repair it |
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fully. |
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In practice, model needs to have a tendency to stop after a couple of lines are added, and sometimes don't write |
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anything at all. We found that just giving it empty completions, single line completions, multiline |
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completions that end with a smaller text indent or at least a newline -- makes it much more usable. This data |
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was used as the rest 85% of the finetune dataset. |
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The final model is the result of several attempts to make it work as good as possible for code completion, |
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and to perform well on a wide range of metrics. The best attempt took 40B tokens. |
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# Limitations and Bias |
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The Refact-1.6B model was trained on text in English. But it has seen a lot more languages in |
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code comments. Its performance on non-English languages is lower, for sure. |
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# Model Stats |
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- **Architecture:** LLAMA-like model with multi-query attention |
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- **Objectives** Fill-in-the-Middle, Chat |
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- **Tokens context:** 4096 |
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- **Pretraining tokens:** 1.2T |
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- **Finetuning tokens:** 40B |
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- **Precision:** bfloat16 |
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- **GPUs** 64 NVidia A5000 |
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- **Training time** 28 days |
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# License |
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The model is licensed under the BigScience OpenRAIL-M v1 license agreement |
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# Citation |
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If you are using this model, please give a link to this page. |
