Instructions to use LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF 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 LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF 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 LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:Q4_K_M
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 LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:Q4_K_M
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 LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:Q4_K_M
Use Docker
docker model run hf.co/LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:Q4_K_M
- Ollama
How to use LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF with Ollama:
ollama run hf.co/LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:Q4_K_M
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": "LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF with Docker Model Runner:
docker model run hf.co/LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:Q4_K_M
- Lemonade
How to use LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3.8-27B-Humanlike-Chat-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:Q4_K_M
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 LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:Q4_K_M
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 "LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:Q4_K_M" \ --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"
Lower variants?
I only have 16gb of ram and this model looks fun :/
Yes, they are coming. We are doing compressions now. Will try to make it within <16gb too. Will update the page soon.
Thanks!!
Hey, you can check out the 3-bit option. Or you can also use the adapter with vLLM with some 1, 2-bit base of qwen3.8 you like.
fyi, I noticed there are issues with thinking modes on lower than 8-bit checkpoints. And am working on a fix regarding that.
for 16gb cards this particular qwen model is best served at iq4_xs rather than q3km
Any plans on even more lower variations?
IQ4_XS would be nice
for 16gb cards this particular qwen model is best served at iq4_xs rather than q3km
Thanks for the recommendation.
@JasonGWarrior IQ4_XS is now available. Itβs about 15.1 GB. The model checkpoints have also been updated and optimised.
Let me know how it works if you try it!
Thanks for your suggestion.