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 by Migel Tissera, 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.

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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

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

Quickstart

llama.cpp / LM Studio (GGUF)

Grab the quant(s) from migtissera/Tess-4-27B-GGUF:

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

# 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

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 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.

Citation

@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}
}

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Tess-4-27B — part of the Tess series by Migel Tissera. Evaluations forthcoming.