Instructions to use DevQuasar/facebook.KernelLLM-GGUF 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 DevQuasar/facebook.KernelLLM-GGUF 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 DevQuasar/facebook.KernelLLM-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf DevQuasar/facebook.KernelLLM-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf DevQuasar/facebook.KernelLLM-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf DevQuasar/facebook.KernelLLM-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 DevQuasar/facebook.KernelLLM-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf DevQuasar/facebook.KernelLLM-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 DevQuasar/facebook.KernelLLM-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf DevQuasar/facebook.KernelLLM-GGUF:Q4_K_M
Use Docker
docker model run hf.co/DevQuasar/facebook.KernelLLM-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use DevQuasar/facebook.KernelLLM-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DevQuasar/facebook.KernelLLM-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": "DevQuasar/facebook.KernelLLM-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/DevQuasar/facebook.KernelLLM-GGUF:Q4_K_M
- Ollama
How to use DevQuasar/facebook.KernelLLM-GGUF with Ollama:
ollama run hf.co/DevQuasar/facebook.KernelLLM-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use DevQuasar/facebook.KernelLLM-GGUF with Docker Model Runner:
docker model run hf.co/DevQuasar/facebook.KernelLLM-GGUF:Q4_K_M
- Lemonade
How to use DevQuasar/facebook.KernelLLM-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull DevQuasar/facebook.KernelLLM-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.facebook.KernelLLM-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download facebook.KernelLLM.Q3_K_M.gguf from DevQuasar/facebook.KernelLLM-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 4.02 GB
-
https://huggingface.co/DevQuasar/facebook.KernelLLM-GGUF/resolve/main/facebook.KernelLLM.Q3_K_M.gguf
- Command line
-
hf download hf://DevQuasar/facebook.KernelLLM-GGUF/facebook.KernelLLM.Q3_K_M.gguf
-
curl -L -o facebook.KernelLLM.Q3_K_M.gguf https://huggingface.co/DevQuasar/facebook.KernelLLM-GGUF/resolve/main/facebook.KernelLLM.Q3_K_M.gguf
4.02 GB
- Xet hash:
- adf70c6d878d364f2a560f8d256eae12bbbaf9a4bfb15d13b05d0734860d9030
- Size of remote file:
- 4.02 GB
- SHA256:
- baa164f694662e5c67cefb8f5e38e37a764fdf320e176b4eb8ff5d197a6f0736
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