Text Generation
PEFT
Safetensors
English
legal
grpo
reinforcement-learning
lora
gemma4
conversational
unsloth
trl
Instructions to use Semaj90/gemma4-e4b-legal-grpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Semaj90/gemma4-e4b-legal-grpo with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-4-E4B-it-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Semaj90/gemma4-e4b-legal-grpo") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Why did you use TRL 0.24.0?
#1
by PhilDevAi - opened
Why did you use trl version 0.24.0 instead of newer one, like 1.0.0?
Did you find to be more compatible with Gemma4? I was having a hard time with 1.0.0
yes watching "hidden feature of gemma4" youtube video by anythingllm creator, liteRT with google has multi-token processing, might be better than llama.cpp, a custom version i want to test out next but claude limits...xD Track 1: Clone TheTom/turboquant_plus, build with CUDA, run llama-server -m gemma4-legal.gguf -ctk turbo3 -ctv turbo3 --port 8090
Track 2: pip install litert-lm, download E2B model, run litert-lm