Instructions to use maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF", filename="ThinkingCap-Qwen3.6-27B-TQ3_4S.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-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 maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF # Run inference directly in the terminal: llama cli -hf maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF # Run inference directly in the terminal: llama cli -hf maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF
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 maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF # Run inference directly in the terminal: ./llama-cli -hf maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF
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 maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF
Use Docker
docker model run hf.co/maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF
- LM Studio
- Jan
- Ollama
How to use maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF with Ollama:
ollama run hf.co/maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF
- Unsloth Studio
How to use maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF to start chatting
- Pi
How to use maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-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 maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF
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 maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF
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 "maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF" \ --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"
- Docker Model Runner
How to use maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF with Docker Model Runner:
docker model run hf.co/maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF
- Lemonade
How to use maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull maczzzzzz/ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF
Run and chat with the model
lemonade run user.ThinkingCap-Qwen3.6-27B-TQ3_4S-GGUF-{{QUANT_TAG}}List all available models
lemonade list
AEON Bench — ThinkingCap-Qwen3.6-27B-TQ3_4S (75 cases @6144)
Date: 2026-07-09 20:09 EDT
Framework: AEON-Bench-Pod v1 (self-reported mode)
Hardware: NVIDIA RTX 5060 Ti 16 GB (turbo-tan/llama.cpp-tq3 77dd77473, TQ3_4S)
Budget: --max-tokens 6144 blanket
Run shape: --limit 75 --fast (no agentic harness)
Results
Overall mean: 0.480 across 75 scored / 75 cases
| Category | Mean | N |
|---|---|---|
| math | 0.400 | 30 |
| instruction | 0.300 | 30 |
| reasoning | 1.000 | 15 |
| Difficulty | Mean | N |
|---|---|---|
| easy | 1.000 | 6 |
| medium | 0.778 | 9 |
| hard | 0.733 | 15 |
| expert | 0.429 | 21 |
| frontier | 0.125 | 24 |
Comparison vs sibling Tess-4-27B-TQ3_4S (same harness)
| Model | math | instruction | reasoning | mean |
|---|---|---|---|---|
| thinkingcap-27b-tq3-4s.gguf (this) | 0.400 | 0.300 | 1.000 | 0.480 |
| tess-4-27b-tq3-4s.gguf | 0.500 | 0.333 | 0.933 | 0.560 |
ThinkingCap vs Tess profile (both 27B-TQ3_4S on node-d):
- Both struggle on instruction (ThinkingCap 0.300, Tess 0.333)
- Both dominate reasoning — ThinkingCap scores a perfect 1.000, Tess 0.933
- Math is the differentiator: Tess wins at 0.500 vs ThinkingCap 0.400
Notes
- 30 prose cases (not in this 75-case run) require a frontier judge endpoint — excluded.
- Bench run in self-reported mode. Mothership returned HTTP 403 (expected).