Instructions to use tahsinahsen/birag-gemma4-e2b-response-only-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tahsinahsen/birag-gemma4-e2b-response-only-lora with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("tahsinahsen/birag-gemma4-e2b-response-only-lora")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

Unsloth Studio

How to use tahsinahsen/birag-gemma4-e2b-response-only-lora with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for tahsinahsen/birag-gemma4-e2b-response-only-lora to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for tahsinahsen/birag-gemma4-e2b-response-only-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tahsinahsen/birag-gemma4-e2b-response-only-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="tahsinahsen/birag-gemma4-e2b-response-only-lora",
    max_seq_length=2048,
)

MLX LM

How to use tahsinahsen/birag-gemma4-e2b-response-only-lora with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "tahsinahsen/birag-gemma4-e2b-response-only-lora"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "tahsinahsen/birag-gemma4-e2b-response-only-lora"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "tahsinahsen/birag-gemma4-e2b-response-only-lora",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

BIRAG Gemma 4 E2B – Response-only LoRA

This repository contains an Unsloth MLX LoRA adapter trained on top of unsloth/gemma-4-E2B-it.

Training configuration

Target: response_only
Maximum sequence length: 8096
LoRA rank: 16
LoRA alpha: 16
Epochs: 3
Training records: 1790
Validation records: 225
Test records: 225
Seed: 3407

Usage

This is a LoRA adapter, not a standalone merged model. It requires the unsloth/gemma-4-E2B-it base model and an Unsloth-compatible MLX runtime.

Limitations

The model is intended for research and supportive guidance. It must not be treated as medical, psychological, or clinical advice.

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MLX

Hardware compatibility

Quantized

Model tree for tahsinahsen/birag-gemma4-e2b-response-only-lora

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Finetuned

unsloth/gemma-4-E2B-it

Adapter

(50)

this model