| --- |
| license: apache-2.0 |
| base_model: |
| - Qwen/Qwen3.6-27B |
| library_name: transformers |
| pipeline_tag: image-text-to-text |
| tags: |
| - tess |
| - agentic |
| - reasoning |
| - thinking |
| - long-context |
| - tool-use |
| - qwen3 |
| - multimodal |
| --- |
| |
| # Tess-4-27B |
|
|
| > **Reasoning that scales with the problem.** An agentic, thinking-native model that deliberates *harder exactly when it matters* — and gets out of its own way when it doesn't. |
|
|
| **Tess-4-27B** is the first Tess release in two years, and the first that *reasons*. Built on **[Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B)** by **[Migel Tissera](https://huggingface.co/migtissera)**, it's post-trained on a deliberate blend: 64K-token long-context agentic traces — real engineering work done with **Fable-5**, not synthetic generations — with a reasoning style approximated from Fable-5 by a three-model teacher ensemble (**Opus-4.8**, **GPT-5.5**, and **GLM-5.2**) fused into one coherent voice. |
|
|
| The result is a 27B model that thinks like a senior engineer: form a hypothesis, act, verify, and reason with real density on the turns that actually deserve it — **not a model that narrates its way to an answer it already had.** |
|
|
| --- |
|
|
| ## Community Performed Benchmarks |
|
|
| **Currently best-in-class for BenchLocal** |
|
|
| | Rank | Model | Score | Result | |
| |------|-------|:-----:|:------:| |
| | 1 | Tess-4-27B (Q8) | 81% | 122/150 | |
| | 2 | Qwen3.6-35B-A3B (UD-Q8_K_XL) | 78% | 117/150 | |
| | 3 | Gemma-4-31B (Q6 · 180k ctx) | 78% | 117/150 | |
| | 4 | Qwopus3.6-27B Coder-Compat (Q6_K) | 77% | 116/150 | |
| | 5 | Qwen3.6-27B pi-tune (Q8) | 77% | 115/150 | |
| |
| ### References |
| |
| 1. https://huggingface.co/migtissera/Tess-4-27B/discussions/2#6a4ff70af13ec7012fb149f0 |
| 2. https://gist.github.com/everson/261fdef8a3d35298b36a07f436e407f6 |
| |
| |
| ## Why Tess-4 is different |
| |
| - 🧠 **Weight-scaled reasoning.** Tess-4 keeps routine steps tight and pours deliberation into the hard ones — planning, debugging, synthesis, judgment calls. It doesn't ramble; it thinks *proportionally* to the difficulty of the moment. |
| - 🛠️ **Agentic by design.** Native, parallel tool use and disciplined multi-step problem solving. It reads a codebase, builds a real mental model, and acts on it. |
| - 📏 **Long-context, trained at 64K.** Post-trained on **64K-token long-context agentic traces**, so it holds a large working set without losing the thread. |
| - 👁️ **Multimodal.** Inherits Qwen3.6's vision tower — text **and** image in. (For GGUF, pair with the included vision projector.) |
| - 🤝 **Honest, not sycophantic.** Trained to give grounded, evidence-based pushback instead of flattery. |
| |
| ## The reasoning traces |
| |
| Tess-4's signature is *how it thinks*. The reasoning/thinking traces used to train it were a **best-case approximation of Fable-5**, produced by a combination of **Opus-4.8, GPT-5.5, and GLM-5.2** working together as a team — a multi-model teacher ensemble distilled into a single, coherent reasoning style. |
| |
| The result is a model that reasons **prospectively** — predicting, verifying, and weighing alternatives *before* acting — rather than narrating after the fact. |
| |
| ## Prompt format & thinking |
| |
| Tess-4 uses the Qwen3.5-family chat template with explicit `<think> … </think>` reasoning blocks. The model reasons privately, then produces its visible answer: |
| |
| ``` |
| <|im_start|>user |
| Your prompt here<|im_end|> |
| <|im_start|>assistant |
| <think> |
| … the model's private reasoning … |
| </think> |
|
|
| … the model's answer …<|im_end|> |
| ``` |
| |
| Apply it automatically via `tokenizer.apply_chat_template(messages, add_generation_prompt=True)`, or `--jinja` in llama.cpp. |
| |
| ## Available formats |
| |
| **This repo — full-precision weights:** |
| |
| | Format | ~Size | Best for | |
| |---|---|---| |
| | BF16 safetensors | 52 GB | transformers · vLLM · SGLang | |
| |
| **GGUF quants → [`migtissera/Tess-4-27B-GGUF`](https://huggingface.co/migtissera/Tess-4-27B-GGUF)** |
| |
| | File | Format | ~Size | Best for | |
| |---|---|---|---| |
| | `Tess-4-27B-Q4_K_M.gguf` | Q4_K_M | 16.5 GB | smallest — great quality/size · most popular | |
| | `Tess-4-27B-Q6_K.gguf` | Q6_K | 22 GB | near-lossless | |
| | `Tess-4-27B-Q8_0.gguf` | Q8_0 | 28 GB | effectively lossless | |
| | `mmproj-Tess-4-27B-F16.gguf` | vision projector | 0.9 GB | pair with any text GGUF for image input | |
| |
| ## Faster inference |
| |
| - ⚡ **[Tess-4-27B-EAGLE3](https://huggingface.co/migtissera/Tess-4-27B-EAGLE3)** — a speculative-decoding draft trained on Tess-4's own outputs: **1.76× average decode speedup, up to 2.4× on reasoning** (measured on H100; lossless — outputs are identical). SGLang: `--speculative-algorithm EAGLE3 --speculative-draft-model-path migtissera/Tess-4-27B-EAGLE3`; vLLM: `--speculative-config '{"method":"eagle3","model":"migtissera/Tess-4-27B-EAGLE3","num_speculative_tokens":4}'`. |
| - 🧮 **[Tess-4-27B-NVFP4](https://huggingface.co/migtissera/Tess-4-27B-NVFP4)** — 4-bit NVFP4 (19 GB, −63%), Blackwell-native W4A4, calibrated on Tess-4's own generations. Quantization and speculative decoding **stack**. |
| |
| ## Quickstart |
| |
| ### llama.cpp / LM Studio (GGUF) |
| |
| Grab the quant(s) from [`migtissera/Tess-4-27B-GGUF`](https://huggingface.co/migtissera/Tess-4-27B-GGUF): |
| |
| ```bash |
| hf download migtissera/Tess-4-27B-GGUF \ |
| Tess-4-27B-Q4_K_M.gguf mmproj-Tess-4-27B-F16.gguf \ |
| --local-dir ./tess-4-27b |
| ``` |
| |
| ```bash |
| # text |
| llama-cli -m Tess-4-27B-Q4_K_M.gguf --jinja -p "Refactor this function and explain your reasoning." |
| |
| # with images (multimodal) |
| llama-mtmd-cli -m Tess-4-27B-Q4_K_M.gguf \ |
| --mmproj mmproj-Tess-4-27B-F16.gguf \ |
| --image photo.png -p "What's in this image?" |
| ``` |
| |
| **LM Studio:** put `mmproj-Tess-4-27B-F16.gguf` in the **same folder** as the model file — LM Studio auto-detects it and enables image input. (Use a recent runtime; older llama.cpp builds won't recognize the architecture.) |
| |
| ### transformers |
| |
| ```python |
| from transformers import AutoProcessor, AutoModelForImageTextToText |
| import torch |
| |
| model_id = "migtissera/Tess-4-27B" |
| processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) |
| model = AutoModelForImageTextToText.from_pretrained( |
| model_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True |
| ) |
|
|
| messages = [{"role": "user", "content": "Explain the tradeoffs of LoRA vs full fine-tuning."}] |
| inputs = processor.apply_chat_template( |
| messages, add_generation_prompt=True, return_tensors="pt" |
| ).to(model.device) |
| |
| out = model.generate(inputs, max_new_tokens=1024) |
| print(processor.decode(out[0], skip_special_tokens=True)) |
| ``` |
| |
| *(Requires a recent `transformers` with Qwen3.5/3.6 support.)* |
| |
| ## What it's good at |
| |
| - **Agentic coding** — exploring unfamiliar repos, planning changes, and executing multi-step work with tools. |
| - **Long-context work** — reasoning over large codebases and documents without dropping context. |
| - **Technical & product judgment** — honest, structured analysis that pushes back with evidence rather than agreeing by default. |
| |
| ## Credits |
| |
| Tess-4-27B is built on **[Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B)** by the **Qwen team** — full credit to them for an outstanding base model. Tess-4 inherits its Qwen3.5-family vision-language architecture and its **Apache 2.0** license. |
| |
| ## License |
| |
| Released under the **Apache License 2.0**, inherited from the base model. See [`LICENSE`](./LICENSE). |
| |
| ## Citation |
| |
| ```bibtex |
| @misc{tissera2026tess4, |
| title = {Tess-4-27B}, |
| author = {Migel Tissera}, |
| year = {2026}, |
| howpublished = {\url{https://huggingface.co/migtissera/Tess-4-27B}}, |
| note = {Built on Qwen/Qwen3.6-27B} |
| } |
| ``` |
| |
| --- |
| |
| *Tess-4-27B — part of the **Tess** series by [Migel Tissera](https://huggingface.co/migtissera). Evaluations forthcoming.* |
| |
