Instructions to use ShinyUser/m24e 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 ShinyUser/m24e 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 ShinyUser/m24e # Run inference directly in the terminal: llama cli -hf ShinyUser/m24e
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ShinyUser/m24e # Run inference directly in the terminal: llama cli -hf ShinyUser/m24e
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 ShinyUser/m24e # Run inference directly in the terminal: ./llama-cli -hf ShinyUser/m24e
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 ShinyUser/m24e # Run inference directly in the terminal: ./build/bin/llama-cli -hf ShinyUser/m24e
Use Docker
docker model run hf.co/ShinyUser/m24e
- LM Studio
- Jan
- Ollama
How to use ShinyUser/m24e with Ollama:
ollama run hf.co/ShinyUser/m24e
- Unsloth Desktop
- Pi
How to use ShinyUser/m24e with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ShinyUser/m24e
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "ShinyUser/m24e" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use ShinyUser/m24e with Docker Model Runner:
docker model run hf.co/ShinyUser/m24e
- Lemonade
How to use ShinyUser/m24e with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ShinyUser/m24e
Run and chat with the model
lemonade run user.m24e-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use ShinyUser/m24e with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ShinyUser/m24e
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 ShinyUser/m24e
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use ShinyUser/m24e with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ShinyUser/m24e
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "ShinyUser/m24e" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Download manifest.json from ShinyUser/m24e: direct link, hf CLI and curl.
- Browser
- Download file 886 Bytes
-
https://huggingface.co/ShinyUser/m24e/resolve/main/manifest.json
- Command line
-
hf download hf://ShinyUser/m24e/manifest.json
-
curl -L -o manifest.json https://huggingface.co/ShinyUser/m24e/resolve/main/manifest.json
886 Bytes
| {"artifact_digest":"sha256:8a38a58ab51139e6b2046de1e2a27745c7aa4878056c884cf6d26fed9bba519d","declared":{"p95_latency_ms":8478,"peak_rss_bytes":1847228416,"size_bytes":1559529769},"files":[{"digest":"sha256:bf5f097e229158681d6ace39d3c83365ed8a4d35fe97598c2449ef52ca7c4157","path":"model.gguf","size_bytes":1417754336}],"hardware_class":"mt-3g","hotkey":"5G7Ev8rEwso93S9wQEAK4f3uMRnaNKon279hEkAzrGaPJtvt","load":{"base_model":"Qwen/Qwen3-1.7B@70d244cc86ccca08cf5af4e1e306ecf908b1ad5e","entrypoint":"model.gguf","format":"gguf","max_input":{"tokens":704},"preprocessing":{"tokenizer":"tokenizer.json"},"quantization":"Q6_K"},"round_index":1237,"sealed":null,"signature":"70b115f368a271afbbafd3c74b4db7bb0eea8bbeb2ebf7b4e70417bebedaca343340c99fd63c1e25d41df5695b0907122aed2c85b38841530cb44850184e8287","source":"hf:ShinyUser/m24e@8a38a58","system":null,"track":"extract","version":"0.1.2"} |