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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

Run Pi

# Start Pi in your project directory:
pi

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

Hermes Agent

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

OpenClaw

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"

Request access to the BottleCap AI model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Please fill out the form below to request access. Name, Company Name, and Company Email are optional — just enter NA if you'd prefer not to share. Your email may be used to send you information about BottleCap AI model updates and early access before public release.

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) 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-Q6_K.gguf`	Q6_K	20.9 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; Q6_K is a slightly smaller near-lossless option; Q4_K_M is the recommended size/quality balance for most local setups.

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"

Speculative decoding (MTP)

llama.cpp can run MTP (multi-token-prediction) self-speculative decoding on these GGUFs for a decode speed-up — no separate draft model needed. Add --spec-type draft-mtp when serving:

llama-server -hf bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF:Q4_K_M --spec-type draft-mtp

Set the draft length with --spec-draft-n-max (e.g. 4). Requires a recent llama.cpp build with MTP support.

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.

Expected performance

Measured on our serving harness (llama.cpp, CUDA llama-server) over MMLU-Pro (reasoning) and RealWorldQA (vision), N=200/dataset × 3 seeds, sampled decoding (temp 1.0 / top_p 0.95 / top_k 20). acc is the mean ± 95% CI across seeds; task s / tok/s are measured per request during the eval at batch size 16 (a throughput setting — a single local user decoding one request at a time will see meaningfully higher tok/s, but the per-row comparison is unaffected). For the full multi-benchmark evaluation see the main model card.

Accuracy is statistically identical across f16, the quantized variants, and the base model — every 95% CI overlaps (MMLU-Pro ≈0.89–0.91, RealWorldQA ≈0.78–0.82). Quantization is near-lossless, and the finetune matches base accuracy. What these GGUFs buy you is brevity → speed: the finetune reasons ~2× shorter than base (≈1000 vs ≈2100 MMLU-Pro tokens; ≈330 vs ≈700 RealWorldQA), so at equal accuracy it finishes each task far faster. MTP self-speculative decoding (--spec-type draft-mtp, n=4) accepts ≈3.3–3.7 tokens per verify step on top. The bolded row — Q4_K_M + MTP — is the fastest per task and the recommended size/quality balance; Q8_0 and Q6_K are near-lossless alternatives at larger sizes. For reference we also list unsloth's Dynamic GGUFs of the base model (UD-*): same llama.cpp path and same accuracy, but base-model quants reason ≈2× longer (none of the finetune's token savings), so they finish only ~1.2–2.2× faster than base-std vs our ~2.6–3.9×.

median tokens = median completion length; task s = measured end-to-end wall-clock per request (bs=16); tok/s = per-request decode rate under that load; speedup = base·bf16·std task s ÷ row task s — same llama.cpp path for every row, so the within-table comparison is apples-to-apples.

MMLU-Pro (reasoning)

config	acc (mean ± 95% CI)	median tokens	tok/s	task s	speedup	accept_len (n=4)
Qwen3.6-27B base (bf16 GGUF) · standard	0.908 ± 0.007	2098	16.0	138.1	1.00×	—
f16 · standard	0.908 ± 0.038	1027	16.0	67.0	2.06×	—
f16 · MTP	0.903 ± 0.007	1045	25.9	43.3	3.19×	3.69
Q8_0 · standard	0.897 ± 0.044	990	21.0	49.6	2.78×	—
Q8_0 · MTP	0.900 ± 0.012	1043	31.3	36.7	3.77×	3.69
Q6_K · standard	0.908 ± 0.007	1011	22.1	49.2	2.81×	—
Q6_K · MTP	0.910 ± 0.012	992	30.4	36.6	3.77×	3.69
Q4_K_M · standard	0.903 ± 0.019	996	25.5	42.3	3.27×	—
Q4_K_M · MTP	0.910 ± 0.012	1054	32.8	35.5	3.89×	3.65
unsloth UD-Q8_K_XL (base) · standard	0.892 ± 0.014	2139	19.6	112.9	1.22×	—
unsloth UD-Q8_K_XL (base) · MTP	0.905 ± 0.000	2133	31.2	72.3	1.91×	3.66
unsloth UD-Q4_K_XL (base) · standard	0.900 ± 0.025	2172	26.0	86.3	1.60×	—
unsloth UD-Q4_K_XL (base) · MTP	0.903 ± 0.014	2094	35.4	63.4	2.18×	3.61

RealWorldQA (vision)

config	acc (mean ± 95% CI)	median tokens	tok/s	task s	speedup	accept_len (n=4)
Qwen3.6-27B base (bf16 GGUF) · standard	0.797 ± 0.019	700	15.1	50.9	1.00×	—
f16 · standard	0.815 ± 0.045	340	13.4	29.3	1.74×	—
f16 · MTP	0.818 ± 0.031	331	16.1	26.6	1.92×	3.29
Q8_0 · standard	0.815 ± 0.054	318	16.5	23.8	2.14×	—
Q8_0 · MTP	0.797 ± 0.019	335	21.2	21.0	2.42×	3.32
Q6_K · standard	0.813 ± 0.007	359	17.0	25.0	2.04×	—
Q6_K · MTP	0.817 ± 0.031	355	20.4	23.3	2.19×	3.32
Q4_K_M · standard	0.808 ± 0.014	334	19.2	21.7	2.35×	—
Q4_K_M · MTP	0.810 ± 0.045	315	21.3	19.8	2.57×	3.26
unsloth UD-Q8_K_XL (base) · standard	0.795 ± 0.000	662	18.2	45.0	1.13×	—
unsloth UD-Q8_K_XL (base) · MTP	0.782 ± 0.073	672	25.6	35.5	1.44×	3.43
unsloth UD-Q4_K_XL (base) · standard	0.792 ± 0.031	727	23.5	40.2	1.27×	—
unsloth UD-Q4_K_XL (base) · MTP	0.785 ± 0.081	760	29.1	35.3	1.44×	3.38

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GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

6-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

(51)

this model