README.md

---
datasets:
  - codeparrot/self-instruct-starcoder
pipeline_tag: text2text-generation
metrics:
- code_eval
library_name: transformers
tags:
- code
model-index:
- name: StarCoder-SelfInstruct
  results:
  - task:
      type: text-generation
    dataset:
      type: openai_humaneval
      name: InstructHumanEval
    metrics:
    - name: pass@1
      type: pass@1
      value: 0.391
      verified: false
  - task:
      type: text-generation
    dataset:
      type: openai_humaneval
      name: HumanEval
    metrics:
    - name: pass@1
      type: pass@1
      value: 0.346
      verified: false
---


# Model Card for Self-instruct-starcoder

<!-- Provide a quick summary of what the model is/does. -->

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)
which was built by boostrapping on StarCoder's generations.
## Uses

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
The model was fine-tuned with the following template
```
Question: <instruction>

Answer: <output>
```
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

```python
instruction = "Write a function to compute the GCD between two integers a and b"
prompt = f"Question:{instruction}\n\nAnswer:"
input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"]
completion = model.generate(input_ids, max_length=200)
print(tokenizer.batch_decode(completion[:,input_ids.shape[1]:])[0])
```

## More information

For additional information, check
- [self-intruct-starcoder](https://huggingface.co/codeparrot/self-instruct-starcoder)
- [starcoder](https://huggingface.co/bigcode/starcoder)