# ThinkingCap Qwen3.6 27B GGUF

URL: https://interfaze.ai/models/bottlecapaithinkingcap-qwen36-27b-gguf

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ThinkingCap Qwen3.6 27B GGUF by bottlecapai, a image-text-to-text model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.

## Comparison

| Feature | ThinkingCap Qwen3.6 27B GGUF | Interfaze |
| --- | --- | --- |
| Input Modalities | text, image | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | unknown | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | 32K | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | ThinkingCap Qwen3.6 27B GGUF | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF)

GGUF / [llama.cpp](https://github.com/ggml-org/llama.cpp) quantizations of [bottlecapai/ThinkingCap-Qwen3.6-27B](https://huggingface.co/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)](https://huggingface.co/Qwen/Qwen3.6-27B) 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](https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B).

## About GGUF and quantization

[GGUF](https://github.com/ggml-org/ggml/blob/master/docs/gguf.md) 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.

## Usage (llama.cpp)

```
llama-cli -hf bottlecapai/ThinkingCap-Qwen3.6-27B-GGUF:Q4_K_M -p "Hi"


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

From our internal serving-validation harness (llama.cpp, single-stream, temperature 0) on a fast **N=100/dataset** subset of MMLU-Pro (reasoning) and RealWorldQA (vision) — a quick **quant-parity + decode-speed** check, _not_ the headline accuracy evals (for the multi-seed, statistically-tested results see the [main model card](https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B)).

Our three quants (`f16`/`Q8_0`/`Q4_K_M`) stay within subset noise of `f16` on accuracy, and MTP self-speculative decoding (`--spec-type draft-mtp`, `n=4`) accepts ≈3.75 tokens per verify step — a ≈1.4–1.7× per-token decode speed-up on top of the finetune's ≈50% token savings. `Q4_K_M` + MTP (bold) is the recommended local config. For reference we also list **[unsloth](https://huggingface.co/unsloth/Qwen3.6-27B-MTP-GGUF)'s Dynamic GGUFs of the _base_ model** (`UD-*`): same llama.cpp path, but base-model quants — so they match base accuracy and reason ≈2× longer (none of the finetune's token savings).

`median tokens` = median completion length; `task s` = median tokens ÷ single-stream tok/s (real per-request time); `speedup` is vs the unquantized base model in standard decoding.

**MMLU-Pro (reasoning)**

| config | acc | median tokens | tok/s | task s | speedup | accept\_len (n=4) |
| --- | --- | --- | --- | --- | --- | --- |
| Qwen3.6-27B base · standard | 0.85 | 1890 | 57.4 | 32.9 | 1.00× | — |
| f16 · standard | 0.89 | 884 | 50.4 | 17.5 | 1.88× | — |
| f16 · MTP | 0.88 | 870 | 86.7 | 10.0 | 3.28× | 3.78 |
| Q8\_0 · standard | 0.88 | 890 | 57.2 | 15.6 | 2.12× | — |
| Q8\_0 · MTP | 0.86 | 856 | 99.4 | 8.6 | 3.82× | 3.77 |
| Q4\_K\_M · standard | 0.86 | 814 | 61.8 | 13.2 | 2.50× | — |
| Q4\_K\_M · MTP | 0.85 | 848 | 89.2 | 9.5 | 3.46× | 3.74 |
| unsloth UD-Q8\_K\_XL (base) · standard | 0.85 | 1896 | 54.5 | 34.8 | 0.95× | — |
| unsloth UD-Q8\_K\_XL (base) · MTP | 0.86 | 1925 | 98.2 | 19.6 | 1.68× | 3.74 |
| unsloth UD-Q4\_K\_XL (base) · standard | 0.84 | 1976 | 62.1 | 31.8 | 1.03× | — |
| unsloth UD-Q4\_K\_XL (base) · MTP | 0.83 | 1928 | 87.1 | 22.1 | 1.49× | 3.72 |

**RealWorldQA (vision)**

| config | acc | median tokens | tok/s | task s | speedup | accept\_len (n=4) |
| --- | --- | --- | --- | --- | --- | --- |
| Qwen3.6-27B base · standard | 0.74 | 556 | 57.4 | 9.7 | 1.00× | — |
| f16 · standard | 0.79 | 271 | 50.4 | 5.4 | 1.80× | — |
| f16 · MTP | 0.79 | 271 | 86.7 | 3.1 | 3.10× | 3.78 |
| Q8\_0 · standard | 0.79 | 270 | 57.2 | 4.7 | 2.05× | — |
| Q8\_0 · MTP | 0.78 | 273 | 99.4 | 2.7 | 3.53× | 3.77 |
| Q4\_K\_M · standard | 0.78 | 283 | 61.8 | 4.6 | 2.11× | — |
| Q4\_K\_M · MTP | 0.78 | 274 | 89.2 | 3.1 | 3.15× | 3.74 |
| unsloth UD-Q8\_K\_XL (base) · standard | 0.68 | 530 | 54.5 | 9.7 | 1.00× | — |
| unsloth UD-Q8\_K\_XL (base) · MTP | 0.69 | 550 | 98.2 | 5.6 | 1.73× | 3.74 |
| unsloth UD-Q4\_K\_XL (base) · standard | 0.65 | 655 | 62.1 | 10.5 | 0.92× | — |
| unsloth UD-Q4\_K\_XL (base) · MTP | 0.70 | 564 | 87.1 | 6.5 | 1.49× | 3.72 |

## Want more deterministic results?

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)