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"
Its just too random
Half of time response isnt even releated to conversation, for simple hi it may say "i love you" or "im not sure"... Feels like an old llama 8b finetune
This is the command i used
"/home/kyo/llama.cpp-latest/build/bin/llama-server" -m "/media/kyo/66BC284DBC281A58/ai/models/Qwen3.8-27B-Humanlike-Chat-Q8_0.gguf" -fa on -ngl 99 -b 2048 -ub 2048 -t 1 -c 200000 --host 0.0.0.0 --port 5001 --jinja --reasoning-preserve -np 1 --chat-template-kwargs "{"reasoning_effort":"medium"}" -sm tensor --fit off -lv 4
Tested in llama webui
For example
Tried to use it in rp too but its not much better than base model, but from the other end... Just too dumb, logical reasoning broken completely
Tried one more time right when im writing this and wow, this is strangest hi response i ever got from it
Thanks very much for the feedback. I am working on a next version of Humanlike which should address these problems at the core. Aiming for release this week!
What you sent here really helps in this process π
Glad if this helps
While qwen 3.8 27b is amazing model, it sucks in conversations, so it'd be nice to have more human-like finetune that retains original model's outstanding reasoning and agentic capabilities, at least part of them
And multilingual support too, unlike the current uncensored release


