README.md

---
library_name: transformers
tags: []
---

# Model Card for StarCoderCodeQ&A

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This is a version of StarCoder model that was fine-tuned on the grammatically corrected texts.  

## Model Details

### Model Description

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- **Model type:** GPT-2
- **Number of Parameters:** 15.5B
- **Supported Programming Language:** Python
- **Finetuned from model:** StarCoder

### Model Sources [optional]

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- **Repository:** [GitHub Repo](https://github.com/IU-AES-AI4Code/CodeQuestionAnswering)
- **Paper:** "Leveraging Large Language Models in Code Question Answering: Baselines and Issues" Georgy Andryushchenko, Vladimir V. Ivanov, Vladimir Makharev, Elizaveta Tukhtina, Aidar Valeev
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<!-- ## Uses -->

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<!-- ### Direct Use -->

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<!-- ### Downstream Use [optional] -->

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<!-- ## Bias, Risks, and Limitations -->

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## How to Get Started with the Model

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('datapaf/StarCoderCodeQnA')
model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="cuda")

code = ... # Your Python code snippet here
question = ... # Your question regarding the snippet here

prompt_template = "Question: {question}\n\nCode: {code}\n\nAnswer:"
prompt = prompt_template.format(question=ex['question'], code=ex['code'])

inputs = tokenizer.encode(prompt, return_tensors="pt").to('cuda')
outputs = model.generate(inputs, max_new_tokens=512, pad_token_id=tokenizer.eos_token_id)
text = tokenizer.decode(outputs[0])
print(text)
```

<!-- ## Training Details -->

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<!-- #### Preprocessing [optional] -->

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<!-- #### Training Hyperparameters -->

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<!-- ## Evaluation -->

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<!-- ### Results -->

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<!-- #### Summary -->



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<!-- ## Environmental Impact -->

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<!-- ## Technical Specifications [optional] -->

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