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--- |
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datasets: |
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- codeparrot/self-instruct-starcoder |
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pipeline_tag: text2text-generation |
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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: StarCoder-SelfInstruct |
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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: InstructHumanEval |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 0.391 |
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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: 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: 0.346 |
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verified: false |
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--- |
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# Model Card for Self-instruct-starcoder |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model is an instruction-tuned version of ⭐️ StarCoder. The instruction dataset involved is [Self-instruct-starcoder](https://huggingface.co/datasets/codeparrot/self-instruct-starcoder) |
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which was built by boostrapping on StarCoder's generations. |
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## Uses |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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The model was fine-tuned with the following template |
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``` |
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Question: <instruction> |
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Answer: <output> |
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``` |
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If you have your model and tokenizer loaded, you can use the following code to make the model generate the right output to a given instruction |
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```python |
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instruction = "Write a function to compute the GCD between two integers a and b" |
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prompt = f"Question:{instruction}\n\nAnswer:" |
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input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"] |
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completion = model.generate(input_ids, max_length=200) |
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print(tokenizer.batch_decode(completion[:,input_ids.shape[1]:])[0]) |
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``` |
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## More information |
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For additional information, check |
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- [self-intruct-starcoder](https://huggingface.co/codeparrot/self-instruct-starcoder) |
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- [starcoder](https://huggingface.co/bigcode/starcoder) |
