README.md

---
license: apache-2.0
datasets:
- motexture/cData
language:
- en
base_model:
- HuggingFaceTB/SmolLM2-1.7B-Instruct
pipeline_tag: text-generation
tags:
- smoll
- coding
- coder
- model
- small
---

# SmolLCoder-1.7B-Instruct

## Introduction

SmolLCoder-1.7B-Instruct is a fine-tuned version of SmolLM2-1.7B-Instruct, trained on the cData coding dataset.

## Quickstart

Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "motexture/SmolLCoder-1.7B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("motexture/SmolLCoder-1.7B-Instruct")

prompt = "Write a C++ program that prints Hello World!"
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
        model_inputs.input_ids,
        max_new_tokens=4096,
        do_sample=True,
        temperature=0.3
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```

## License

[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)

## Citation
```bash
@misc{allal2024SmolLM2,
      title={SmolLM2 - with great data, comes great performance}, 
      author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Gabriel Martín Blázquez and Lewis Tunstall and Agustín Piqueres and Andres Marafioti and Cyril Zakka and Leandro von Werra and Thomas Wolf},
      year={2024},
}
```