README.md

---
base_model: bigcode/tiny_starcoder_py
library_name: transformers
model_name: tiny-starcoder-ft
tags:
- generated_from_trainer
- smol-course
- module_1
- code_generation
- trl
- sft
licence: license
---

# Model Card for tiny-starcoder-ft

This model is a fine-tuned version of [bigcode/tiny_starcoder_py](https://huggingface.co/bigcode/tiny_starcoder_py) using a samples from [iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).

## Quick start

```python
model_name = "sky-2002/tiny-starcoder-ft"
model = AutoModelForCausalLM.from_pretrained(
    pretrained_model_name_or_path=model_name
).to(device)
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)

prompt = "Write python code to calculate sum of a list"

# Format with template
messages = [{"role": "user", "content": prompt}]
formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)

inputs = tokenizer(formatted_prompt, return_tensors="pt").to(device)

outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training procedure



This model was trained with SFT.

### Framework versions

- TRL: 0.12.1
- Transformers: 4.46.3
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3

## Citations



Cite TRL as:
    
```bibtex
@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
```