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
File size: 823 Bytes
1769200 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | {
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"model_max_length": 1024,
"pad_token": "<|endoftext|>",
"tokenizer_class": "GPT2Tokenizer",
"unk_token": "<|endoftext|>",
"vocab_size": 49152,
"add_bos_token": false,
"add_eos_token": false
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