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
Canonicalize v18 GGUF layout: flatten Q*_K/* -> root with dot separator
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|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:34074a31585c776332f707434e317b948fdb2d4eb8390a45f61d1fb86ea9a5c6
|
| 3 |
size 1487228832
|
unbound-e2b.Q8_0-00004-of-00004.gguf
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 921259616
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6a104d152766d69b758c31f31277d9be4b30f2486d482a7173cdef2cd77494eb
|
| 3 |
size 921259616
|