Text Generation
GGUF
edge0
Mixture of Experts
edge-inference
latent-moe
4-bit precision
agent
coding
cuda
conversational
Instructions to use Edge0/Edge0-120B-NVFP4 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 Edge0/Edge0-120B-NVFP4 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 Edge0/Edge0-120B-NVFP4 # Run inference directly in the terminal: llama cli -hf Edge0/Edge0-120B-NVFP4
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Edge0/Edge0-120B-NVFP4 # Run inference directly in the terminal: llama cli -hf Edge0/Edge0-120B-NVFP4
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf Edge0/Edge0-120B-NVFP4 # Run inference directly in the terminal: ./build/bin/llama cli -hf Edge0/Edge0-120B-NVFP4
Use Docker
docker model run hf.co/Edge0/Edge0-120B-NVFP4
- Llama new
- LM Studio
- Jan
- vLLM
How to use Edge0/Edge0-120B-NVFP4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Edge0/Edge0-120B-NVFP4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Edge0/Edge0-120B-NVFP4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Edge0/Edge0-120B-NVFP4
- Ollama
How to use Edge0/Edge0-120B-NVFP4 with Ollama:
ollama run hf.co/Edge0/Edge0-120B-NVFP4
- Unsloth Desktop
- Pi
How to use Edge0/Edge0-120B-NVFP4 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Edge0/Edge0-120B-NVFP4
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": "Edge0/Edge0-120B-NVFP4" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Edge0/Edge0-120B-NVFP4 with Docker Model Runner:
docker model run hf.co/Edge0/Edge0-120B-NVFP4
- Lemonade
How to use Edge0/Edge0-120B-NVFP4 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Edge0/Edge0-120B-NVFP4
Run and chat with the model
lemonade run user.Edge0-120B-NVFP4-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use Edge0/Edge0-120B-NVFP4 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Edge0/Edge0-120B-NVFP4
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 Edge0/Edge0-120B-NVFP4
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Edge0/Edge0-120B-NVFP4 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Edge0/Edge0-120B-NVFP4
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 "Edge0/Edge0-120B-NVFP4" \ --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 model.json from Edge0/Edge0-120B-NVFP4: direct link, hf CLI and curl.
- Browser
- Download file 1.53 kB
-
https://huggingface.co/Edge0/Edge0-120B-NVFP4/resolve/main/model.json
- Command line
-
hf download hf://Edge0/Edge0-120B-NVFP4/model.json
-
curl -L -o model.json https://huggingface.co/Edge0/Edge0-120B-NVFP4/resolve/main/model.json
1.53 kB
| { | |
| "model_name": "edge0-120b-fp4", | |
| "display_name": "Edge0-120B", | |
| "family": "Edge0", | |
| "base_model": "Qwen3.8-Flash-Next", | |
| "architecture": "latent sparse MoE", | |
| "params": "120B", | |
| "active_params": "6.1 B", | |
| "layers": 48, | |
| "experts": 512, | |
| "experts_per_token": "10 routed + 1 shared", | |
| "latent_dim": 1280, | |
| "context": 262144, | |
| "build": "NVFP4", | |
| "weights": "32.96 GiB", | |
| "pack_format": "strata pack index v3", | |
| "exe": "strata", | |
| "args": [ | |
| "--pack", | |
| ".", | |
| "--native", | |
| "./model-skeleton.gguf", | |
| "--native-dense-gguf", | |
| "./model-skeleton.gguf", | |
| "--ple-gguf", | |
| "./ple-fp8.gguf", | |
| "--ple-io", | |
| "direct", | |
| "--expert-profile", | |
| "./profile.bin", | |
| "--expert-cache", | |
| "auto", | |
| "--prefill", | |
| "auto", | |
| "--pcie-frac", | |
| "0.1", | |
| "--spec", | |
| "4", | |
| "--spec-min-p", | |
| "0.5", | |
| "--mtp", | |
| "./mtp", | |
| "--max-context", | |
| "131072", | |
| "--kv", | |
| "int8", | |
| "--kv-resident", | |
| "32768", | |
| "--vram-reserve-mib", | |
| "700", | |
| "--pool-workers", | |
| "24", | |
| "--pcie-mode", | |
| "dma" | |
| ], | |
| "cwd": ".", | |
| "tokenizer": "./tokenizer", | |
| "log": "./serve.log", | |
| "port": 8122, | |
| "gpu": 0, | |
| "gpus_asked": true, | |
| "env": { | |
| "STRATA_LATENT_DENSE": "./latent_dense_q4.bin", | |
| "STRATA_LATENT_DENSE_FMT": "4", | |
| "STRATA_LATENT_A": "./experts.bin", | |
| "STRATA_LATENT_LABELS": "./labels.bin", | |
| "STRATA_EMBED_VRAM": "1", | |
| "STRATA_LATENT_FUSED": "1", | |
| "STRATA_LATENT_FUSED_DU": "1", | |
| "STRATA_LATENT_U_GPU": "1", | |
| "STRATA_LAT_FUSED_Q3K": "0" | |
| } | |
| } | |