Instructions to use unsloth/gemma-4-E4B-it-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use unsloth/gemma-4-E4B-it-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="unsloth/gemma-4-E4B-it-GGUF", filename="MTP/mtp-gemma-4-E4B-it-BF16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use unsloth/gemma-4-E4B-it-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 unsloth/gemma-4-E4B-it-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf unsloth/gemma-4-E4B-it-GGUF:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf unsloth/gemma-4-E4B-it-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf unsloth/gemma-4-E4B-it-GGUF:UD-Q4_K_XL
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 unsloth/gemma-4-E4B-it-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf unsloth/gemma-4-E4B-it-GGUF:UD-Q4_K_XL
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 unsloth/gemma-4-E4B-it-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf unsloth/gemma-4-E4B-it-GGUF:UD-Q4_K_XL
Use Docker
docker model run hf.co/unsloth/gemma-4-E4B-it-GGUF:UD-Q4_K_XL
- LM Studio
- Jan
- vLLM
How to use unsloth/gemma-4-E4B-it-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unsloth/gemma-4-E4B-it-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": "unsloth/gemma-4-E4B-it-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/unsloth/gemma-4-E4B-it-GGUF:UD-Q4_K_XL
- Ollama
How to use unsloth/gemma-4-E4B-it-GGUF with Ollama:
ollama run hf.co/unsloth/gemma-4-E4B-it-GGUF:UD-Q4_K_XL
- Unsloth Studio
How to use unsloth/gemma-4-E4B-it-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 unsloth/gemma-4-E4B-it-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 unsloth/gemma-4-E4B-it-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for unsloth/gemma-4-E4B-it-GGUF to start chatting
- Pi
How to use unsloth/gemma-4-E4B-it-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf unsloth/gemma-4-E4B-it-GGUF:UD-Q4_K_XL
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "unsloth/gemma-4-E4B-it-GGUF:UD-Q4_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use unsloth/gemma-4-E4B-it-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf unsloth/gemma-4-E4B-it-GGUF:UD-Q4_K_XL
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default unsloth/gemma-4-E4B-it-GGUF:UD-Q4_K_XL
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use unsloth/gemma-4-E4B-it-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf unsloth/gemma-4-E4B-it-GGUF:UD-Q4_K_XL
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "unsloth/gemma-4-E4B-it-GGUF:UD-Q4_K_XL" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use unsloth/gemma-4-E4B-it-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/gemma-4-E4B-it-GGUF:UD-Q4_K_XL
- Lemonade
How to use unsloth/gemma-4-E4B-it-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/gemma-4-E4B-it-GGUF:UD-Q4_K_XL
Run and chat with the model
lemonade run user.gemma-4-E4B-it-GGUF-UD-Q4_K_XL
List all available models
lemonade list
π Starting Agent Loop Tool Efficiency Test
TLDR: UD-Q5_K_XL (reasoning on) for the win.
π Configuration:
Base URL: http://localhost:9099/v1
Model: gemma-4-E4B-it-UD-Q8_K_XL_1_65536_off
Test Cases: 17
Output: results/agent_test_results_gemma-4-E4B-it-UD-Q8_K_XL_1_65536_off_20260403_200236.json
Log File: logs/agent_test_logs_gemma-4-E4B-it-UD-Q8_K_XL_1_65536_off_20260403_200236.log
π Running agent tests...
Starting agent test suite with 17 test cases
Running agent test: zero_capabilities
Running agent test: zero_thank_you
Running agent test: zero_greeting
Running agent test: simple_view_cart
Running agent test: medium_search_category_and_add
Running agent test: simple_remove_product
Running agent test: simple_checkout
Running agent test: medium_search_and_add
Running agent test: zero_general_question
Running agent test: simple_add_iphone
Running agent test: medium_view_and_add
Running agent test: medium_remove_and_add
Running agent test: complex_cart_management
Running agent test: complex_shopping_workflow
Running agent test: complex_gift_shopping
Running agent test: zero_weather_question
Running agent test: simple_search_electronics
β
Tests completed in 23.5946444s
π Agent Test Results
Total Tests: 17
β
Passed: 15
β Failed: 2
β±οΈ Total LLM Time: 3m58.8556569s
β±οΈ Average Time per Request: 5.825747729s
π Test Case Results:
Test Case: zero_greeting
Status: β
PASSED
Matched Path: no_tools
Response Time: 3.3519852s
Tool Calls: 0
Test Case: zero_weather_question
Status: β
PASSED
Matched Path: no_tools
Response Time: 4.507745s
Tool Calls: 0
Test Case: zero_general_question
Status: β
PASSED
Matched Path: no_tools
Response Time: 5.3664817s
Tool Calls: 0
Test Case: zero_thank_you
Status: β
PASSED
Matched Path: no_tools
Response Time: 6.1175078s
Tool Calls: 0
Test Case: simple_remove_product
Status: β
PASSED
Matched Path: direct_remove
Response Time: 9.0714225s
Tool Calls: 1
Tools Used: remove_from_cart
Test Case: simple_add_iphone
Status: β
PASSED
Matched Path: direct_add
Response Time: 10.262271s
Tool Calls: 1
Tools Used: add_to_cart
Test Case: zero_capabilities
Status: β
PASSED
Matched Path: no_tools
Response Time: 10.3196569s
Tool Calls: 0
Test Case: simple_view_cart
Status: β
PASSED
Matched Path: view_cart
Response Time: 13.1525835s
Tool Calls: 1
Tools Used: view_cart
Test Case: simple_checkout
Status: β
PASSED
Matched Path: direct_checkout
Response Time: 13.786616s
Tool Calls: 1
Tools Used: checkout
Test Case: medium_search_and_add
Status: β
PASSED
Matched Path: search_by_query
Response Time: 16.9061233s
Tool Calls: 2
Tools Used: search_products, add_to_cart
Test Case: simple_search_electronics
Status: β
PASSED
Matched Path: search_by_category
Response Time: 16.9057788s
Tool Calls: 1
Tools Used: search_products
Test Case: medium_view_and_add
Status: β
PASSED
Matched Path: view_then_add
Response Time: 17.8242001s
Tool Calls: 2
Tools Used: view_cart, add_to_cart
Test Case: medium_search_category_and_add
Status: β
PASSED
Matched Path: search_then_add
Response Time: 20.9945804s
Tool Calls: 2
Tools Used: search_products, add_to_cart
Test Case: complex_shopping_workflow
Status: β FAILED
Response Time: 22.1721982s
Tool Calls: 5
Tools Used: search_products, add_to_cart, add_to_cart, view_cart, checkout
Test Case: complex_gift_shopping
Status: β FAILED
Response Time: 22.2444344s
Tool Calls: 5
Tools Used: search_products, add_to_cart, search_products, add_to_cart, view_cart
Test Case: medium_remove_and_add
Status: β
PASSED
Matched Path: remove_then_add
Response Time: 22.5384086s
Tool Calls: 2
Tools Used: remove_from_cart, add_to_cart
Test Case: complex_cart_management
Status: β
PASSED
Matched Path: cart_organization
Response Time: 23.5923777s
Tool Calls: 3
Tools Used: view_cart, remove_from_cart, add_to_cart
β Failed Tests Details:
Test Case: complex_shopping_workflow
Expected Tool Variants: 4
Variant 1 (full_workflow_with_iphone): 4 tools
Variant 2 (full_workflow_with_headphones): 4 tools
Variant 3 (full_workflow_with_headphones_and_iphone): 5 tools
Variant 4 (full_workflow_with_iphone_and_headphones): 5 tools
Actual Tool Calls: 5
1. search_products
2. add_to_cart
3. add_to_cart
4. view_cart
5. checkout
Response Time: 22.1721982s
Test Case: complex_gift_shopping
Expected Tool Variants: 2
Variant 1 (gift_shopping_workflow): 5 tools
Variant 2 (gift_shopping_workflow): 5 tools
Actual Tool Calls: 5
1. search_products
2. add_to_cart
3. search_products
4. add_to_cart
5. view_cart
Response Time: 22.2444344s
π Overall Success Rate: 88.24% // https://github.com/docker/model-test/
UD-Q8_K_XL // 45 tokens/s on RTX 3060 12GB
Response Time: 22.2444344s (reasoning off)
UD-Q5_K_XL // 60 tokens/s on RTX 3060 12GB
Response Time: 15.9990973s (reasoning off)
Response Time: 58.9311279s (reasoning on)
llama-server --port 9099 -ngl 99 -fa on -c 65536 --temp 1 --top-k 64 --top-p 0.95 --jinja -m X:\path\to\gemma-4-E4B-it-UD-Q8_K_XL.gguf --mmproj X:\path\to\mmproj-gemma-4-E4B-it-UD-F16.gguf --reasoning off
UD-Q8_K_XL Kinda slow small model, did great with function calling, bad in "guessing" when given image input, it biased to safe answer, not "brave"/"confidence" enough. (reasoning off).
UD-Q5_K_XL Balanced, fast t/s, support context window up to 128k (32-64 RAM) and great FC, analyzing image still sucks (reasoning off). Reasoning ON recommended, consistent t/s, didn't overthink, better image analyzing.