Instructions to use basecompute/Qwen3.8-27B-BaseQuant-V1 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 basecompute/Qwen3.8-27B-BaseQuant-V1 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 basecompute/Qwen3.8-27B-BaseQuant-V1 # Run inference directly in the terminal: llama cli -hf basecompute/Qwen3.8-27B-BaseQuant-V1
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf basecompute/Qwen3.8-27B-BaseQuant-V1 # Run inference directly in the terminal: llama cli -hf basecompute/Qwen3.8-27B-BaseQuant-V1
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 basecompute/Qwen3.8-27B-BaseQuant-V1 # Run inference directly in the terminal: ./build/bin/llama cli -hf basecompute/Qwen3.8-27B-BaseQuant-V1
Use Docker
docker model run hf.co/basecompute/Qwen3.8-27B-BaseQuant-V1
- Llama new
- LM Studio
- Jan
- vLLM
How to use basecompute/Qwen3.8-27B-BaseQuant-V1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "basecompute/Qwen3.8-27B-BaseQuant-V1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "basecompute/Qwen3.8-27B-BaseQuant-V1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/basecompute/Qwen3.8-27B-BaseQuant-V1
- Ollama
How to use basecompute/Qwen3.8-27B-BaseQuant-V1 with Ollama:
ollama run hf.co/basecompute/Qwen3.8-27B-BaseQuant-V1
- Unsloth Desktop
- Pi
How to use basecompute/Qwen3.8-27B-BaseQuant-V1 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf basecompute/Qwen3.8-27B-BaseQuant-V1
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": "basecompute/Qwen3.8-27B-BaseQuant-V1" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use basecompute/Qwen3.8-27B-BaseQuant-V1 with Docker Model Runner:
docker model run hf.co/basecompute/Qwen3.8-27B-BaseQuant-V1
- Lemonade
How to use basecompute/Qwen3.8-27B-BaseQuant-V1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull basecompute/Qwen3.8-27B-BaseQuant-V1
Run and chat with the model
lemonade run user.Qwen3.8-27B-BaseQuant-V1-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use basecompute/Qwen3.8-27B-BaseQuant-V1 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf basecompute/Qwen3.8-27B-BaseQuant-V1
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 basecompute/Qwen3.8-27B-BaseQuant-V1
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use basecompute/Qwen3.8-27B-BaseQuant-V1 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf basecompute/Qwen3.8-27B-BaseQuant-V1
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 "basecompute/Qwen3.8-27B-BaseQuant-V1" \ --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"
Memory needed for Small / Medium / Large at 8K context
A fit table from the three file sizes, for people deciding between Small, Medium and Large.
Need β file + KV at 8K (~0.54 GB; only 16 of 64 layers hold a full KV cache) + ~0.8 GB. Estimates.
| File | Size | Need @8K (est.) | 8 GB | 12 GB | 16 GB | 24 GB | 32 GB | Mac (unified) |
|---|---|---|---|---|---|---|---|---|
| Small | 10.93 GB | ~12.3 GB | β | β | β | β | β | 24 GB+ |
| Medium | 16.41 GB | ~17.7 GB | β | β | β | β | β | 32 GB+ |
| Large | 20.82 GB | ~22.2 GB | β | β | β | β | β | 36 GB+ |
So Small is the 16 GB option, and all three fit a 24 GB card at 8K (Large with about 1 GB to spare). Before pulling GGUF weights, you can also test 10 open models side by side for free in the browser (needs no account) on localLLMrig's / page. Corrections welcome.
considering adding a βTinyβ quant
@Lukas-bc a Tiny would fill the gap below Small. Rough targets with the same math (need β file + ~1.3 GB at 8K, estimates): a file of about 9β10 GB would bring 12 GB cards into range at 8K. For 8 GB cards the file would have to be ~6 GB, which is under 2 bits per weight for a 27B, so probably not worth it. Happy to add a row to the table if you publish it.