Instructions to use bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF",
	filename="ThinkingCap-Qwen3.6-27B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use bottlecapai/ThinkingCap-Qwen3.6-27B-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 bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bottlecapai/ThinkingCap-Qwen3.6-27B-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 bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bottlecapai/ThinkingCap-Qwen3.6-27B-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 bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bottlecapai/ThinkingCap-Qwen3.6-27B-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 bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bottlecapai/ThinkingCap-Qwen3.6-27B-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": "bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF:Q4_K_M

Ollama
How to use bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF with Ollama:
```
ollama run hf.co/bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF:Q4_K_M
```

Unsloth Studio

How to use bottlecapai/ThinkingCap-Qwen3.6-27B-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 bottlecapai/ThinkingCap-Qwen3.6-27B-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 bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF to start chatting

How to use bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF:Q4_K_M

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": "bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use bottlecapai/ThinkingCap-Qwen3.6-27B-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 bottlecapai/ThinkingCap-Qwen3.6-27B-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 bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bottlecapai/ThinkingCap-Qwen3.6-27B-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 "bottlecapai/ThinkingCap-Qwen3.6-27B-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"

Docker Model Runner
How to use bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF with Docker Model Runner:
```
docker model run hf.co/bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF:Q4_K_M
```

Lemonade

How to use bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.ThinkingCap-Qwen3.6-27B-GGUF-Q4_K_M

List all available models

lemonade list

bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF

GGUF / llama.cpp quantizations of bottlecapai/ThinkingCap-Qwen3.6-27B — capability of Qwen3.6-27B with 50% less thinking tokens on average, achieved by finetuning Qwen3.6-27B (Qwen Team, 2026) with online reinforcement learning while preserving the original answer quality and style.

➡️ Full model description, evaluation results (multi-seed, statistically tested), recommended sampling params, and citation: see the main model card at bottlecapai/ThinkingCap-Qwen3.6-27B.

About GGUF and quantization

GGUF is a single-file model format for running LLMs locally with llama.cpp and compatible runtimes (Ollama, LM Studio, …). The quantized variants below store weights at reduced precision — e.g. ~4.7 bits per weight for Q4_K_M instead of the 16-bit f16 source — cutting download size and memory severalfold at a small, measured quality cost.

Files

File	Quant	Size
`ThinkingCap-Qwen3.6-27B-Q4_K_M.gguf`	Q4_K_M	15.7 GB
`ThinkingCap-Qwen3.6-27B-Q8_0.gguf`	Q8_0	27.1 GB
`ThinkingCap-Qwen3.6-27B-f16.gguf`	f16	50.9 GB
`mmproj-ThinkingCap-Qwen3.6-27B-f16.gguf`	mmproj (vision)	0.9 GB

f16 is the unquantized source; Q8_0 is near-lossless; Q4_K_M is the recommended size/quality balance for most local setups.

When shown, Mean KLD is the mean KL divergence of the quant vs the f16 model over a held-out corpus (lower = closer to full precision).

Usage (llama.cpp)

# pull a specific quant straight from the Hub and chat
llama-cli -hf bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF:Q4_K_M -p "Hi"

# or download one file and run it
huggingface-cli download bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF ThinkingCap-Qwen3.6-27B-Q4_K_M.gguf --local-dir .
llama-cli -m ThinkingCap-Qwen3.6-27B-Q4_K_M.gguf -p "Hi"

Vision (image input)

ThinkingCap is a vision-language model. Image input needs the multimodal projector mmproj-ThinkingCap-Qwen3.6-27B-f16.gguf (in this repo) loaded alongside a text GGUF — the single f16 mmproj pairs with any of the quants above.

LM Studio / Jan / Ollama, …: download the mmproj-*.gguf from this repo; LM Studio auto-detects it and enables the image (🖼️) button.
llama.cpp CLI:

huggingface-cli download bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF \
  ThinkingCap-Qwen3.6-27B-Q4_K_M.gguf mmproj-ThinkingCap-Qwen3.6-27B-f16.gguf --local-dir .
llama-mtmd-cli -m ThinkingCap-Qwen3.6-27B-Q4_K_M.gguf \
  --mmproj mmproj-ThinkingCap-Qwen3.6-27B-f16.gguf --image photo.jpg -p "Describe this image."

llama-server: add --mmproj mmproj-ThinkingCap-Qwen3.6-27B-f16.gguf to expose an OpenAI-compatible vision endpoint.

Downloads last month: 34

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

8-bit

16-bit

Model tree for bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF

Base model

Qwen/Qwen3.6-27B

Finetuned

bottlecapai/ThinkingCap-Qwen3.6-27B

Quantized

(2)

this model