---
license: apache-2.0
base_model: bottlecapai/ThinkingCap-Qwen3.6-27B
tags:
- gguf
- tq3
- qwen35
- quantization
- llama-cpp
- blackwell
- cuda
base_model_relation: quantized
quantized_by: maczzzzzz (via turbo-tan/llama.cpp-tq3)
---

# ThinkingCap-Qwen3.6-27B TQ3_4S — GGUF

**TQ3_4S quant of bottlecapai/ThinkingCap-Qwen3.6-27B (Apache 2.0), produced via turbo-tan's llama.cpp-tq3 CUDA fork.** ThinkingCap is a Qwen3.6-27B fine-tune optimized for reasoning-heavy tasks. Benchmarked on a Blackwell RTX 5060 Ti (16 GB). Mean AEON score: **0.480** (75 cases, 6144 budget).

## File

| File | Size | Quant | BPW |
|---|---|---|---|
| `ThinkingCap-Qwen3.6-27B-TQ3_4S.gguf` | 14 GB | TQ3_4S (turbo-tan) | ~3.8 bpw |

## NOT a stock llama.cpp quant

TQ3_4S (TurboQuant 3-bit 4-state) is a custom weight format unique to [turbo-tan/llama.cpp-tq3](https://github.com/turbo-tan/llama.cpp-tq3). Stock llama.cpp and nixpkgs `llama-cpp` will exit with `unknown quantization` at load time. Use the `llama-server`/`llama-cli` from the tq3 fork.

## Scope of these benchmarks — read this first

**These numbers are a light baseline, not a thorough TQ3 evaluation.** The mesh's bench framework is built for production agent workload regression-detection on the local stack, not for the kind of multi-axis sweep that upstream quant maintainers typically publish. Specifically:

- **Harness scope is bounded.** The numbers below come from the mesh's Aeon-Bench-Pod (self-reported mode, 75-case `--fast` shape, text-only, no agentic harness). That's a regression suite, not a quality benchmark.
- **Sample sizes are small.** 75 cases on a single GPU, single rep. None are powered for statistical significance.
- **No perplexity / wikitext / MMLU / GSM8K.** The mesh's stack isn't a quality benchmark — those are upstream's territory.
- **Single GPU class (Blackwell 16 GB).** All measurements are on an NVIDIA RTX 5060 Ti 16 GB (CUDA 13.2, turbo-tan/llama.cpp-tq3 77dd77473). No RDNA4, no multi-GPU, no Vulkan. Cross-hardware generalization is NOT implied.
- **No human eval.** "0.480 mean on the AEON suite" is not a quality verdict on this specific quant.
- **30 prose cases unscored** — no frontier judge endpoint configured, so the prose category is excluded from the mean.

**What this IS good for:** a quick signal that the quant (a) loads, (b) runs at sane throughput, (c) doesn't break the mesh's agent tool-calling. **What this is NOT good for:** claiming "this is the best quant of this model," reproducing academic benchmark results, or substituting for upstream's validation work.

For a rigorous view, see [bottlecapai/ThinkingCap-Qwen3.6-27B](https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B) (parent model) and [turbo-tan/llama.cpp-tq3](https://github.com/turbo-tan/llama.cpp-tq3) (quantizer). Raw bench reports are attached as `BENCH-*.md` files in this repo.

## What we measured

### AEON v3 — 75 cases (self-reported, 6144 budget)

Benchmarked on Blackwell RTX 5060 Ti 16 GB, `--limit 75 --fast --max-tokens 6144`.

| Category | Mean | N |
|---|---|---|
| **Overall** | **0.480** | 75 |
| 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 |

**ThinkingCap's profile:** pure reasoning specialist (1.000 — perfect on reasoning). Weak on instruction (0.300) and math (0.400). Best suited for roles where reasoning depth is the priority.

### Comparison vs. sibling quants (75-case @6144, same harness, node-d)

| Model | mean | math | instruction | reasoning |
|---|---|---|---|---|
| Tess-4-27B-TQ3_4S | 0.560 | 0.500 | 0.333 | 0.933 |
| **ThinkingCap-Qwen3.6-27B-TQ3_4S** | **0.480** | 0.400 | 0.300 | 1.000 |
| Qwen3.6-27B-TQ3_4S (prod baseline) | 0.520 | 0.500 | 0.333 | 0.933 |

## Quick start

```bash
# Build turbo-tan's tq3 fork
git clone https://github.com/turbo-tan/llama.cpp-tq3
cd llama.cpp-tq3
mkdir build && cd build
cmake .. -DGGML_CUDA=ON -DCUDA_DOCKER_ARCH=sm_120
make -j$(nproc)

# Serve
llama-server \
  -m ThinkingCap-Qwen3.6-27B-TQ3_4S.gguf \
  --host 0.0.0.0 --port 8081 \
  -ngl 99 -c 131072 -t 12 \
  -ctk q4_0 -ctv q4_0 \
  -np 1 --batch-size 512 --ubatch-size 128 \
  --jinja --metrics -rea off
```

## Reproduce the quant

```bash
# Requires the tq3 fork and the F16 source GGUF
llama-quantize --allow-requantize ThinkingCap-Qwen3.6-27B-f16.gguf \
  ThinkingCap-Qwen3.6-27B-TQ3_4S.gguf TQ3_4S
```

## Files in this repo

| File | Description |
|---|---|
| `ThinkingCap-Qwen3.6-27B-TQ3_4S.gguf` | The quantized model (LFS-tracked) |
| `README.md` | This model card |
| `BENCH-aeon-75-case-6144.md` | AEON bench results (75 cases, 6144 budget) |

## What's NOT in this repo (caveats)

- **Stock llama.cpp will not load this file.** TQ3_4S is a custom weight format unique to turbo-tan/llama.cpp-tq3.
- **No AMD GPU bench.** All measurements are RTX 5060 Ti (CUDA). No RDNA4, no ROCm.
- **No quality benchmark** (perplexity, MMLU, GSM8K). The custom 3-bit quant works on the mesh's regression tests; whether it's "the best TQ3 quant" needs upstream validation.
- **No MTP / speculative-decode bench.** ThinkingCap was tested without MTP.
- **No prose/creativity evaluation.** 30 prose cases were unscored (no judge endpoint configured).

## Provenance

- **Source model:** [bottlecapai/ThinkingCap-Qwen3.6-27B](https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B) — Qwen3.6-27B fine-tune
- **Source model license:** Apache 2.0
- **Quantizer:** [turbo-tan/llama.cpp-tq3](https://github.com/turbo-tan/llama.cpp-tq3) @ 77dd77473
- **Quantizer license:** MIT
- **Build hardware:** NVIDIA RTX 5060 Ti 16 GB (Blackwell), CUDA 13.2, NixOS 25.11
- **Bench harness:** Aeon-Bench-Pod v1 (self-reported mode)
- **Original bench report:** `raw/benchmarks/2026-07-09-node-d-thinkingcap-tq3/` in the meshina repo

## License

- **ThinkingCap-Qwen3.6-27B** is Apache 2.0 (per its HF model card).
- **turbo-tan/llama.cpp-tq3** is MIT.
- The GGUF in this repo is a derivative of the Apache 2.0-licensed parent, produced with the MIT-licensed quantizer. The Apache 2.0 license is preserved.