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library_name: peft
model_name: gemma-roleplay-v2-lora
tags:
- base_model:adapter:google/gemma-4-E4B-it
- lora
- sft
- transformers
- trl
- roleplay
license: gemma
base_model: google/gemma-4-E4B-it
pipeline_tag: text-generation
---
# Gemma Roleplay v2 LoRA adapter
This is the PEFT adapter for [Gemma Roleplay v2](https://huggingface.co/text-generator/llmtrain),
trained from [google/gemma-4-E4B-it](https://huggingface.co/google/gemma-4-E4B-it)
with QLoRA SFT. It is intended for fictional consenting-adult roleplay and
creative chat. See the parent model card for usage, limitations, and the live
hosted inference endpoint.
## Quick start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = "google/gemma-4-E4B-it"
tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(model, "text-generator/llmtrain", subfolder="adapter")
inputs = tokenizer.apply_chat_template(
[{"role": "user", "content": "Write a short scene in a haunted hotel."}],
add_generation_prompt=True, return_tensors="pt",
).to(model.device)
output = model.generate(inputs, max_new_tokens=128, do_sample=True, temperature=0.85)
print(tokenizer.decode(output[0, inputs.shape[-1]:], skip_special_tokens=True))
```
## Training procedure
This model was trained with SFT.
### Framework versions
- PEFT 0.18.0
- TRL: 1.8.0
- Transformers: 5.5.0
- Pytorch: 2.9.1
- Datasets: 4.0.0
- 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}
}
```
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