Instructions to use tchbcb/nqwen27 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 tchbcb/nqwen27 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 tchbcb/nqwen27:IQ2_XS # Run inference directly in the terminal: llama cli -hf tchbcb/nqwen27:IQ2_XS
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tchbcb/nqwen27:IQ2_XS # Run inference directly in the terminal: llama cli -hf tchbcb/nqwen27:IQ2_XS
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 tchbcb/nqwen27:IQ2_XS # Run inference directly in the terminal: ./llama-cli -hf tchbcb/nqwen27:IQ2_XS
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 tchbcb/nqwen27:IQ2_XS # Run inference directly in the terminal: ./build/bin/llama-cli -hf tchbcb/nqwen27:IQ2_XS
Use Docker
docker model run hf.co/tchbcb/nqwen27:IQ2_XS
- LM Studio
- Jan
- Ollama
How to use tchbcb/nqwen27 with Ollama:
ollama run hf.co/tchbcb/nqwen27:IQ2_XS
- Unsloth Desktop
- Pi
How to use tchbcb/nqwen27 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tchbcb/nqwen27:IQ2_XS
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": "tchbcb/nqwen27:IQ2_XS" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use tchbcb/nqwen27 with Docker Model Runner:
docker model run hf.co/tchbcb/nqwen27:IQ2_XS
- Lemonade
How to use tchbcb/nqwen27 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tchbcb/nqwen27:IQ2_XS
Run and chat with the model
lemonade run user.nqwen27-IQ2_XS
List all available models
lemonade list
- Hermes Agent
How to use tchbcb/nqwen27 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tchbcb/nqwen27:IQ2_XS
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 tchbcb/nqwen27:IQ2_XS
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use tchbcb/nqwen27 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tchbcb/nqwen27:IQ2_XS
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 "tchbcb/nqwen27:IQ2_XS" \ --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"
Commit History
22 days ago
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perf: 2xT4 bench results (layer optimal, MTP n/a in quant) + SPEC_FLAGS passthrough (start_server.sh)
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perf: 2xT4 bench results (layer optimal, MTP n/a in quant) + SPEC_FLAGS passthrough (README.md)
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docs: engine tarball fast path (~10-15 min total deploy)
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fast path: prefer repo prebuilt T4 engine tarball; libcuda fix first
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add prebuilt T4 engine: llama.cpp b11175 cu128 sm75 (skip 40min source compile)
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fix: locate downloaded gguf robustly (weights/ subdir from hf_hub_download)
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fix: auto-link libcuda driver lib (Kaggle CUDA::cuda_driver cmake error)
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128k context upgrade: KV q8_0/q4_0 + FA + auto fallback (kaggle_oneclick_cell.py)
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128k context upgrade: KV q8_0/q4_0 + FA + auto fallback (rescue_restart.sh)
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128k context upgrade: KV q8_0/q4_0 + FA + auto fallback (start_server.sh)
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128k context upgrade: KV q8_0/q4_0 + FA + auto fallback (deploy_oneclick.sh)
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128k context upgrade: KV q8_0/q4_0 + FA + auto fallback (README.md)
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Upload README.md with huggingface_hub
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Upload kaggle_oneclick_cell.py with huggingface_hub
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Upload weights/Qwen3.8-27B-GSQ-RCO-IQ2_XS.gguf with huggingface_hub
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initial commit
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