Text Classification
Transformers
Safetensors
GGUF
gemma4
image-text-to-text
classification
decision
conversational
Instructions to use hilman2/gev-e4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hilman2/gev-e4b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hilman2/gev-e4b") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("hilman2/gev-e4b") model = AutoModelForMultimodalLM.from_pretrained("hilman2/gev-e4b", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use hilman2/gev-e4b 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 hilman2/gev-e4b:Q4_0 # Run inference directly in the terminal: llama cli -hf hilman2/gev-e4b:Q4_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf hilman2/gev-e4b:Q4_0 # Run inference directly in the terminal: llama cli -hf hilman2/gev-e4b:Q4_0
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf hilman2/gev-e4b:Q4_0 # Run inference directly in the terminal: ./build/bin/llama cli -hf hilman2/gev-e4b:Q4_0
Use Docker
docker model run hf.co/hilman2/gev-e4b:Q4_0
- Llama new
- LM Studio
- Jan
- Ollama
How to use hilman2/gev-e4b with Ollama:
ollama run hf.co/hilman2/gev-e4b:Q4_0
- Unsloth Desktop
- Pi
How to use hilman2/gev-e4b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf hilman2/gev-e4b:Q4_0
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "hilman2/gev-e4b:Q4_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use hilman2/gev-e4b with Docker Model Runner:
docker model run hf.co/hilman2/gev-e4b:Q4_0
- Lemonade
How to use hilman2/gev-e4b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull hilman2/gev-e4b:Q4_0
Run and chat with the model
lemonade run user.gev-e4b-Q4_0
List all available models
lemonade list
- Hermes Agent
How to use hilman2/gev-e4b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf hilman2/gev-e4b:Q4_0
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 hilman2/gev-e4b:Q4_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use hilman2/gev-e4b with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf hilman2/gev-e4b:Q4_0
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 "hilman2/gev-e4b:Q4_0" \ --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"
File size: 3,729 Bytes
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