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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use tahsinahsen/birag-gemma4-e2b-response-only-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tahsinahsen/birag-gemma4-e2b-response-only-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tahsinahsen/birag-gemma4-e2b-response-only-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tahsinahsen/birag-gemma4-e2b-response-only-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tahsinahsen/birag-gemma4-e2b-response-only-gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf tahsinahsen/birag-gemma4-e2b-response-only-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tahsinahsen/birag-gemma4-e2b-response-only-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf tahsinahsen/birag-gemma4-e2b-response-only-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tahsinahsen/birag-gemma4-e2b-response-only-gguf:Q4_K_M

Use Docker

docker model run hf.co/tahsinahsen/birag-gemma4-e2b-response-only-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use tahsinahsen/birag-gemma4-e2b-response-only-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tahsinahsen/birag-gemma4-e2b-response-only-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tahsinahsen/birag-gemma4-e2b-response-only-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tahsinahsen/birag-gemma4-e2b-response-only-gguf:Q4_K_M

Ollama
How to use tahsinahsen/birag-gemma4-e2b-response-only-gguf with Ollama:
```
ollama run hf.co/tahsinahsen/birag-gemma4-e2b-response-only-gguf:Q4_K_M
```

Unsloth Studio

How to use tahsinahsen/birag-gemma4-e2b-response-only-gguf 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-gguf 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-gguf 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-gguf to start chatting

Docker Model Runner
How to use tahsinahsen/birag-gemma4-e2b-response-only-gguf with Docker Model Runner:
```
docker model run hf.co/tahsinahsen/birag-gemma4-e2b-response-only-gguf:Q4_K_M
```

Lemonade

How to use tahsinahsen/birag-gemma4-e2b-response-only-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tahsinahsen/birag-gemma4-e2b-response-only-gguf:Q4_K_M

Run and chat with the model

lemonade run user.birag-gemma4-e2b-response-only-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

BIRAG Gemma 4 E2B Response-Only — GGUF Q4_K_M

Bu repo, tahsinahsen/birag-gemma4-e2b-response-only merged 16-bit modelinin Unsloth ve llama.cpp kullanılarak oluşturulmuş Q4_K_M GGUF dağıtımıdır.

Dataset: tahsinahsen/birag-response-only-tr, revision v0.4
Quantization: Q4_K_M
Multimodal projector: BF16
Doğrulanan context: 16384
Chat template: GGUF metadata içindeki Gemma 4 template

llama.cpp ile kullanım

hf download tahsinahsen/birag-gemma4-e2b-response-only-gguf \
  birag-gemma4-e2b-response-only-Q4_K_M.gguf \
  birag-gemma4-e2b-response-only-mmproj-BF16.gguf \
  --local-dir ./birag-gemma4-e2b-gguf

llama-server \
  --model ./birag-gemma4-e2b-gguf/birag-gemma4-e2b-response-only-Q4_K_M.gguf \
  --mmproj ./birag-gemma4-e2b-gguf/birag-gemma4-e2b-response-only-mmproj-BF16.gguf \
  --ctx-size 16384 \
  --jinja \
  --chat-template-kwargs '{"enable_thinking":false}' \
  --host 127.0.0.1 \
  --port 8080

curl http://127.0.0.1:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "birag-gemma4-e2b-q4",
    "messages": [
      {
        "role": "system",
        "content": "Türkçe, destekleyici ve özerkliği güçlendiren yanıt ver."
      },
      {
        "role": "user",
        "content": "Daha bağımsız karar vermeye nasıl başlayabilirim?"
      }
    ],
    "max_tokens": 256,
    "temperature": 1.0,
    "top_p": 0.95,
    "stream": false
  }'

Downloads last month: -

GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

4-bit

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

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Finetuned

unsloth/gemma-4-E2B-it

Adapter

tahsinahsen/birag-gemma4-e2b-response-only

Quantized

(1)

this model

tahsinahsen
/

birag-gemma4-e2b-response-only-gguf

BIRAG Gemma 4 E2B Response-Only — GGUF Q4_K_M

llama.cpp ile kullanım

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

Dataset used to train tahsinahsen/birag-gemma4-e2b-response-only-gguf