README.md

---
license: apache-2.0
tags:
- code-generation
- t5
- lora
- peft
- transformers
library_name: peft
base_model: t5-small
datasets: nvidia/OpenCodeReasoning
model-index:
- name: T5-Small with LoRA on OpenCodeReasoning
  results:
  - task:
      type: text2text-generation
      name: Code Generation
    dataset:
      name: OpenCodeReasoning
      type: nvidia/OpenCodeReasoning
    metrics:
      - name: Loss
        type: loss
        value: 4.69
---

# T5-Small with LoRA on OpenCodeReasoning

This is a LoRA fine-tuned version of T5-small on a subset of NVIDIA's OpenCodeReasoning dataset using [PEFT](https://github.com/huggingface/peft).
Improved version to be uploaded soon. 

## Loss Curve

| Step | Train Loss | Val Loss |
|------|------------|----------|
| 50   | 8.63       | 8.17     |
| 100  | 6.04       | 5.35     |
| 150  | 5.31       | 4.90     |
| 200  | 5.19       | 4.71     |
| 250  | 4.94       | 4.59     |
| 300  | 4.95       | 4.51     |
| 350  | 4.79       | 4.46     |
| 400  | 4.89       | 4.42     |
| 450  | 4.69       | 4.40     |

Final Train Loss: **4.69**
Final Eval Loss: **4.40**


## Notes

Trained on subset of OpenCodeReasoning due to Colab memory limits

Use PeftModel with t5-small base

Metrics used: Loss (BLEU skipped due to output structure)


## License
Apache 2.0


## Example Usage

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from peft import PeftModel, PeftConfig

config = PeftConfig.from_pretrained("ShahzebKhoso/t5-small-opencode-lora")
base_model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)
model = PeftModel.from_pretrained(base_model, "ShahzebKhoso/t5-small-opencode-lora")
tokenizer = AutoTokenizer.from_pretrained("ShahzebKhoso/t5-small-opencode-lora")

inputs = tokenizer("generate code: write a function to reverse a string", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
'''