---

license: apache-2.0

base_model: migtissera/Tess-4-27B

tags:

• gguf
• tq3
• qwen35
• quantization
• llama-cpp
• blackwell
• cuda

base_model_relation: quantized

quantized_by: maczzzzzz (via turbo-tan/llama.cpp-tq3)

---

Tess-4-27B TQ3_4S — GGUF

TQ3_4S quant of migtissera/Tess-4-27B (Apache 2.0), produced via turbo-tan's llama.cpp-tq3 CUDA fork. Tess-4-27B is a Qwen3.6-27B fine-tune. Benchmarked on a Blackwell RTX 5060 Ti (16 GB). Mean AEON score: 0.560 (75 cases, 6144 budget).

File

| File | Size | Quant | BPW |

|---|---|---|---|

| Tess-4-27B-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. 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. "0.560 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 (30/150 cases) is excluded from the mean. The real score, including prose, would likely differ.

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, (d) produces coherent output across math, instruction, reasoning, and coding. 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 migtissera/Tess-4-27B (parent model) and 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 | 0.560 | 75 |

| math | 0.500 | 30 |

| instruction | 0.333 | 30 |

| reasoning | 0.933 | 15 |

| Difficulty | Mean | N |

|---|---|---|

| easy | 1.000 | 6 |

| medium | 0.889 | 9 |

| hard | 0.667 | 15 |

| expert | 0.524 | 21 |

| frontier | 0.292 | 24 |

Tess-4's profile: reasoning specialist (0.933), weak on instruction (0.333). Math is mid-range (0.500). This aligns with Tess-4 being optimized for deep reasoning and code review rather than chat/instruction-following.

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 |

| Qwen3.6-27B-TQ3_4S (prod baseline) | 0.520 | 0.500 | 0.333 | 0.933 |

| ThinkingCap-Qwen3.6-27B-TQ3_4S | 0.480 | 0.400 | 0.300 | 1.000 |

Quick start

# 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 with production-equivalent flags
llama-server \
  -m Tess-4-27B-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

# Requires the tq3 fork and the F16 source GGUF
llama-quantize --allow-requantize tess-4-27b-f16.gguf \
  Tess-4-27B-TQ3_4S.gguf TQ3_4S

Files in this repo

| File | Description |

|---|---|

| Tess-4-27B-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 still works on the mesh's regression tests; whether it's "the best TQ3 quant" needs upstream validation.
• No MTP / speculative-decode bench. Tess-4 has native MTP support in some variants; this quant was tested without MTP.
• No vision/multimodal test. This variant is text-only.
• No prose/creativity evaluation. 30 prose cases were unscored (no judge endpoint configured).

Provenance

• Source model: migtissera/Tess-4-27B — Qwen3.6-27B fine-tune
• Source model license: Apache 2.0
• Quantizer: 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-tess4-tq3-rerun/ in the meshina repo

License

• Tess-4-27B 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.