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Model: Ba2han/test_CPTlsft2
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---
base_model: Ba2han/test_CPTl
library_name: transformers
model_name: test_CPTlsft2
tags:
- generated_from_trainer
- trl
- unsloth
- sft
licence: license
---
# Model Card for test_CPTlsft2
This model is a fine-tuned version of [Ba2han/test_CPTl](https://huggingface.co/Ba2han/test_CPTl).
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="Ba2han/test_CPTlsft2", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/batuhan409/huggingface/runs/ne3w0d1y)
This model was trained with SFT.
### Framework versions
- TRL: 0.24.0
- Transformers: 5.14.1
- Pytorch: 2.11.0
- Datasets: 4.8.0
- Tokenizers: 0.22.2
## 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}}
}
```

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{% for message in messages %}{{'<|im_start|>' + message['role'] + '
' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|im_end|>' + '
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{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{ '<|im_start|>assistant
' }}{% endif %}

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{
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"unsloth_version": "2026.8.4",
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{
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{% for message in messages %}{{'<|im_start|>' + message['role'] + '
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