README.md

---
base_model: HuggingFaceTB/SmolLM2-135M-Instruct
library_name: peft
model_name: smol-code-finetuned
tags:
- base_model:adapter:HuggingFaceTB/SmolLM2-135M-Instruct
- lora
- sft
- transformers
- trl
licence: licence
pipeline_tag: text-generation
license: cc-by-nc-4.0
---

# Model Card for SmolLLM2-135M-Code

This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).

## Quick start

```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

model = AutoPeftModelForCausalLM.from_pretrained("ereniko/SmolLLM2-135M-Code")
tokenizer = AutoTokenizer.from_pretrained("ereniko/SmolLLM2-135M-Code")

def ask(instruction):
    prompt = f"### Instruction:\n{instruction}\n\n### Input:\n\n### Output:\n"
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.7, do_sample=True)
    print(tokenizer.decode(outputs[0], skip_special_tokens=True))

ask("Write a Python function to reverse a string")
```

## Training procedure

 



This model was trained with SFT.

### Framework versions

- PEFT 0.18.1
- TRL: 0.29.0
- Transformers: 5.2.0
- Pytorch: 2.8.0+cu128
- Datasets: 4.6.0
- Tokenizers: 0.22.2

## Citations



Cite TRL as:
    
```bibtex
@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}
```