README.md

---
base_model: facebook/MobileLLM-R1-140M
library_name: transformers
model_name: >-
  transformer-facebook-MobileLLM-R1-140M-ml-512-bs-32-ws-100-lr-1e-05-full_ft-merge_user_input_system_prompt
tags:
- generated_from_trainer
- sft
- trl
licence: license
pipeline_tag: text-classification
language:
- en
---

# Model Card for transformer-facebook-MobileLLM-R1-140M-ml-512-bs-32-ws-100-lr-1e-05-full_ft-merge_user_input_system_prompt

This model is a fine-tuned version of [facebook/MobileLLM-R1-140M](https://huggingface.co/facebook/MobileLLM-R1-140M).
It has been trained using [TRL](https://github.com/huggingface/trl).

## Quick start

```python
from transformers import pipeline

question = """
You are a speaking turn-ending identifier. Your task is to identify whether the user's speaking turn is complete or not. Respond with `end` if the user's turn is complete, or `continue` if it is not.

User input: I want to
"""
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=1, return_full_text=False)[0]
print(output["generated_text"]) # "end" or "continue"
```

## Training procedure

This model was trained with SFT.

### Framework versions

- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.4

## 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 Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
```