Text Generation
GGUF
English
Chinese
ICE
ice-quant
imatrix
sangreal
Mixture of Experts
qwen35moe
vision
conversational
Instructions to use gbuzhf/Nex-N2.5-mini-Sangreal-ICE 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 gbuzhf/Nex-N2.5-mini-Sangreal-ICE 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 gbuzhf/Nex-N2.5-mini-Sangreal-ICE:Q8_0 # Run inference directly in the terminal: llama cli -hf gbuzhf/Nex-N2.5-mini-Sangreal-ICE:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf gbuzhf/Nex-N2.5-mini-Sangreal-ICE:Q8_0 # Run inference directly in the terminal: llama cli -hf gbuzhf/Nex-N2.5-mini-Sangreal-ICE:Q8_0
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 gbuzhf/Nex-N2.5-mini-Sangreal-ICE:Q8_0 # Run inference directly in the terminal: ./build/bin/llama cli -hf gbuzhf/Nex-N2.5-mini-Sangreal-ICE:Q8_0
Use Docker
docker model run hf.co/gbuzhf/Nex-N2.5-mini-Sangreal-ICE:Q8_0
- Llama new
- LM Studio
- Jan
- vLLM
How to use gbuzhf/Nex-N2.5-mini-Sangreal-ICE with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "gbuzhf/Nex-N2.5-mini-Sangreal-ICE" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gbuzhf/Nex-N2.5-mini-Sangreal-ICE", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/gbuzhf/Nex-N2.5-mini-Sangreal-ICE:Q8_0
- Ollama
How to use gbuzhf/Nex-N2.5-mini-Sangreal-ICE with Ollama:
ollama run hf.co/gbuzhf/Nex-N2.5-mini-Sangreal-ICE:Q8_0
- Unsloth Desktop
- Pi
How to use gbuzhf/Nex-N2.5-mini-Sangreal-ICE with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf gbuzhf/Nex-N2.5-mini-Sangreal-ICE:Q8_0
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": "gbuzhf/Nex-N2.5-mini-Sangreal-ICE:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use gbuzhf/Nex-N2.5-mini-Sangreal-ICE with Docker Model Runner:
docker model run hf.co/gbuzhf/Nex-N2.5-mini-Sangreal-ICE:Q8_0
- Lemonade
How to use gbuzhf/Nex-N2.5-mini-Sangreal-ICE with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull gbuzhf/Nex-N2.5-mini-Sangreal-ICE:Q8_0
Run and chat with the model
lemonade run user.Nex-N2.5-mini-Sangreal-ICE-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use gbuzhf/Nex-N2.5-mini-Sangreal-ICE with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf gbuzhf/Nex-N2.5-mini-Sangreal-ICE:Q8_0
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 gbuzhf/Nex-N2.5-mini-Sangreal-ICE:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use gbuzhf/Nex-N2.5-mini-Sangreal-ICE with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf gbuzhf/Nex-N2.5-mini-Sangreal-ICE:Q8_0
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 "gbuzhf/Nex-N2.5-mini-Sangreal-ICE:Q8_0" \ --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"
File size: 7,183 Bytes
592307d | 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 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 860 540" width="860" height="540" font-family="Inter,Segoe UI,Helvetica,Arial,sans-serif">
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<text x="80" y="52" font-size="13" fill="#52514e">Mean KLD vs BF16 on the same tokens, llama-perplexity, n_ctx 16384, 8 chunks. Lower-left is better.</text>
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<text x="455.0" y="518" font-size="13" fill="#52514e" text-anchor="middle">File size (GB, lower is better)</text>
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<text x="307" y="82" font-size="13" fill="#0b0b0b">bartowski imatrix GGUF</text>
<text x="830" y="82" font-size="12" fill="#898781" text-anchor="end">bars: ±1 s.e.</text>
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