Instructions to use Notacape/EdgeDecoder-JAX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Notacape/EdgeDecoder-JAX 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 Notacape/EdgeDecoder-JAX:F16 # Run inference directly in the terminal: llama cli -hf Notacape/EdgeDecoder-JAX:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Notacape/EdgeDecoder-JAX:F16 # Run inference directly in the terminal: llama cli -hf Notacape/EdgeDecoder-JAX:F16
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 Notacape/EdgeDecoder-JAX:F16 # Run inference directly in the terminal: ./llama-cli -hf Notacape/EdgeDecoder-JAX:F16
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 Notacape/EdgeDecoder-JAX:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Notacape/EdgeDecoder-JAX:F16
Use Docker
docker model run hf.co/Notacape/EdgeDecoder-JAX:F16
- LM Studio
- Jan
- vLLM
How to use Notacape/EdgeDecoder-JAX with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Notacape/EdgeDecoder-JAX" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Notacape/EdgeDecoder-JAX", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Notacape/EdgeDecoder-JAX:F16
- Ollama
How to use Notacape/EdgeDecoder-JAX with Ollama:
ollama run hf.co/Notacape/EdgeDecoder-JAX:F16
- Unsloth Desktop
- Docker Model Runner
How to use Notacape/EdgeDecoder-JAX with Docker Model Runner:
docker model run hf.co/Notacape/EdgeDecoder-JAX:F16
- Lemonade
How to use Notacape/EdgeDecoder-JAX with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Notacape/EdgeDecoder-JAX:F16
Run and chat with the model
lemonade run user.EdgeDecoder-JAX-F16
List all available models
lemonade list
- Atomic Chat
Download model.safetensors from Notacape/EdgeDecoder-JAX: direct link, hf CLI and curl.
- Browser
- Download file 2.02 GB
-
https://huggingface.co/Notacape/EdgeDecoder-JAX/resolve/main/model.safetensors
- Command line
-
hf download hf://Notacape/EdgeDecoder-JAX/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Notacape/EdgeDecoder-JAX/resolve/main/model.safetensors
2.02 GB
- Xet hash:
- 698f66ea886a638d931fb3fb848d2a878cfeff47d96c879b5095f92121531ac3
- Size of remote file:
- 2.02 GB
- SHA256:
- b64e58bf835a56cf42b86c6156f2487bf121f96d8bfacc9c7ee49cf0880824cb
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