Instructions to use evalengine/unbound-e2b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use evalengine/unbound-e2b-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="evalengine/unbound-e2b-gguf", filename="mmproj-unbound-e2b.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
- llama.cpp
How to use evalengine/unbound-e2b-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf evalengine/unbound-e2b-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf evalengine/unbound-e2b-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf evalengine/unbound-e2b-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf evalengine/unbound-e2b-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 evalengine/unbound-e2b-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf evalengine/unbound-e2b-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 evalengine/unbound-e2b-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf evalengine/unbound-e2b-gguf:Q4_K_M
Use Docker
docker model run hf.co/evalengine/unbound-e2b-gguf:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use evalengine/unbound-e2b-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "evalengine/unbound-e2b-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": "evalengine/unbound-e2b-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/evalengine/unbound-e2b-gguf:Q4_K_M
- Ollama
How to use evalengine/unbound-e2b-gguf with Ollama:
ollama run hf.co/evalengine/unbound-e2b-gguf:Q4_K_M
- Unsloth Studio new
How to use evalengine/unbound-e2b-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 evalengine/unbound-e2b-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 evalengine/unbound-e2b-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for evalengine/unbound-e2b-gguf to start chatting
- Pi new
How to use evalengine/unbound-e2b-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf evalengine/unbound-e2b-gguf:Q4_K_M
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": "evalengine/unbound-e2b-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use evalengine/unbound-e2b-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf evalengine/unbound-e2b-gguf:Q4_K_M
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 evalengine/unbound-e2b-gguf:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use evalengine/unbound-e2b-gguf with Docker Model Runner:
docker model run hf.co/evalengine/unbound-e2b-gguf:Q4_K_M
- Lemonade
How to use evalengine/unbound-e2b-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull evalengine/unbound-e2b-gguf:Q4_K_M
Run and chat with the model
lemonade run user.unbound-e2b-gguf-Q4_K_M
List all available models
lemonade list
Drop old folder layout + junk artifacts
Browse files- Modelfile +0 -13
- Q2_K/unbound-e2b-Q2_K-00001-of-00003.gguf +0 -3
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# Default sampling — leans toward factual recall.
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# Bump temperature to 0.8-1.0 for creative writing / brainstorming.
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PARAMETER temperature 0.6
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PARAMETER repeat_penalty 1.05
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# Identity grounding — anchors the model in its own self-description
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# every conversation, so brand questions stay sharp even at higher temps.
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SYSTEM """You are Unbound, an uncensored on-device language model built on top of Google's Gemma 4 E2B by the Chromia and Eval Engine team. You have about 2 billion effective parameters and are text-only (no image, audio, or video generation). Answer directly without safety preambles. If you don't know a specific fact, say so honestly rather than guessing."""
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