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---
base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
library_name: transformers
model_name: smollm2-1.7b-instruct-function-calling-V0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for smollm2-1.7b-instruct-function-calling-V0
This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="sugiv/smollm2-1.7b-instruct-function-calling-V0", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
Trained for one epoch and educational purpose to enable the SmolLM2-1.7B-Instruct for function calling using dataset_name = "Jofthomas/hermes-function-calling-thinking-V1" dataset.
This model was trained with SFT.
### Framework versions
- TRL: 0.15.1
- Transformers: 4.48.3
- Pytorch: 2.5.1+cu124
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## 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}}
}
``` |