adapter/README.md

---
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}
}
```