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"
Request to add an IQ4_XS quantization.
After loading the Q4KM model on my hardware, the remaining resources make it difficult to enable a long context.
Hey, thanks for trying it out! I'm planning to quantize IQ4_XS on the next training run, thanks for flagging the memory issue.
What GPU and context length are you trying to run?
We’re also considering a smaller Humanlike model on Gemma for lower-VRAM systems in the near future.
Feel free to join our discord for discussions, it'd be great to have you there.
Hey, thanks for trying it out! I'm planning to quantize IQ4_XS on the next training run, thanks for flagging the memory issue.
What GPU and context length are you trying to run?
We’re also considering a smaller Humanlike model on Gemma for lower-VRAM systems in the near future.
Feel free to join our discord for discussions, it'd be great to have you there.
My hardware is an RTX 3080 20G, with KV cache using Q8 quantization and a context length of 80000. If I switch to IQ4_XS, the context length can reach 120000, which is more than enough for me. I mainly do novel writing, but after the recent model update, it's been hard to generate long texts. And the old version of the model made me delete it directly.
@yybl IQ4_XS is now uploaded! It’s about 15.1 GB versus 16.56 GB for Q4_K_M, so that should free up some room for your KV cache.
The new release is more optimised and balanced, and should follow instructions and context better. Let me know if it still doesn't give you the prose you are looking for.