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 Desktop

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"

ThinkingCap-Qwen3.6-27B-GGUF / README.md

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metadata

base_model: bottlecapai/ThinkingCap-Qwen3.6-27B
base_model_relation: quantized
library_name: gguf
tags:
  - qwen3_6
  - gguf
  - llama.cpp
  - token-efficient
  - efficient-thinking
pipeline_tag: image-text-to-text
extra_gated_heading: Request access to the BottleCap AI model
extra_gated_prompt: >-
  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.
extra_gated_fields:
  Name: text
  Company Name: text
  Company Email: text
extra_gated_button_content: Agree and request access

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