migtissera/Tess-4-27B · Hugging Face

Source: https://huggingface.co/migtissera/Tess-4-27B

https://huggingface.co/migtissera/Tess-4-27B#tess-4-27bTess-4-27B

**Reasoning that scales with the problem.**An agentic, thinking-native model that deliberatesharder exactly when it matters— and gets out of its own way when it doesn’t.

Tess-4-27Bis the first Tess release in two years, and the first thatreasons. Built on**Qwen/Qwen3.6-27BbyMigel Tissera, it’s post-trained on a deliberate blend: 64K-token long-context agentic traces — real engineering work done withFable-5**, not synthetic generations — with a reasoning style approximated from Fable-5 by a three-model teacher ensemble (Opus-4.8,GPT-5.5, andGLM-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.

https://huggingface.co/migtissera/Tess-4-27B#why-tess-4-is-differentWhy 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 thinksproportionallyto 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 on64K-token long-context agentic traces, so it holds a large working set without losing the thread.
• 👁️Multimodal.Inherits Qwen3.6’s vision tower — textandimage in. (For GGUF, pair with the included vision projector.)
• 🤝**Honest, not sycophantic.**Trained to give grounded, evidence-based pushback instead of flattery.

https://huggingface.co/migtissera/Tess-4-27B#the-reasoning-tracesThe reasoning traces

Tess-4’s signature ishow it thinks. The reasoning/thinking traces used to train it were abest-case approximation of Fable-5, produced by a combination ofOpus-4.8, GPT-5.5, and GLM-5.2working together as a team — a multi-model teacher ensemble distilled into a single, coherent reasoning style.

The result is a model that reasonsprospectively— predicting, verifying, and weighing alternativesbeforeacting — rather than narrating after the fact.

https://huggingface.co/migtissera/Tess-4-27B#prompt-format–thinkingPrompt 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 viatokenizer\.apply\_chat\_template\(messages, add\_generation\_prompt=True\), or\-\-jinjain llama.cpp.

https://huggingface.co/migtissera/Tess-4-27B#available-formatsAvailable formats

This repo — full-precision weights:

Format~SizeBest forBF16 safetensors52 GBtransformers · vLLM · SGLang GGUF quants →migtissera/Tess\-4\-27B\-GGUF

FileFormat~SizeBest forTess\-4\-27B\-Q4\_K\_M\.ggufQ4_K_M16.5 GBsmallest — great quality/size · most popularTess\-4\-27B\-Q6\_K\.ggufQ6_K22 GBnear-losslessTess\-4\-27B\-Q8\_0\.ggufQ8_028 GBeffectively losslessmmproj\-Tess\-4\-27B\-F16\.ggufvision projector0.9 GBpair with any text GGUF for image input

https://huggingface.co/migtissera/Tess-4-27B#quickstartQuickstart

https://huggingface.co/migtissera/Tess-4-27B#llamacpp–lm-studio-ggufllama.cpp / LM Studio (GGUF)

Grab the quant(s) frommigtissera/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:putmmproj\-Tess\-4\-27B\-F16\.ggufin thesame folderas 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.)

https://huggingface.co/migtissera/Tess-4-27B#transformerstransformers

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 recenttransformerswith Qwen3.5/3.6 support.)

https://huggingface.co/migtissera/Tess-4-27B#what-its-good-atWhat 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.

https://huggingface.co/migtissera/Tess-4-27B#creditsCredits

Tess-4-27B is built on**Qwen/Qwen3.6-27Bby theQwen team**— full credit to them for an outstanding base model. Tess-4 inherits its Qwen3.5-family vision-language architecture and itsApache 2.0license.

https://huggingface.co/migtissera/Tess-4-27B#licenseLicense

Released under theApache License 2.0, inherited from the base model. SeeLICENSE.

https://huggingface.co/migtissera/Tess-4-27B#citationCitation

@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 theTessseries byMigel Tissera. Evaluations forthcoming.