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base_model: unsloth/gemma-4-e4b-it-unsloth-bnb-4bit
library_name: transformers
model_name: gemma_4_lora_E4b
tags:
- generated_from_trainer
- unsloth
- sft
- trl
licence: license
---
# Model Card for gemma_4_lora_E4b
This model is a fine-tuned version of [unsloth/gemma-4-e4b-it-unsloth-bnb-4bit](https://huggingface.co/unsloth/gemma-4-e4b-it-unsloth-bnb-4bit).
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="HoangVuSnape/gemma_4_lora_E4b", 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/comet-ml/comet-examples/master/logo/comet_badge.png" alt="Visualize in Comet" width="150" height="24"/>](https://www.comet.com/ho-ng-v-7034/gemma4-medical-vqa/d064674d9f5940b09d40ed22436c7148)
This model was trained with SFT.
### Framework versions
- TRL: 1.6.0
- Transformers: 5.5.0
- Pytorch: 2.10.0+cu128
- Datasets: 4.3.0
- 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}
}
``` |