- 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"
