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TeichAI

About TeichAI

A small lab with a simple loop: ask the best models, teach the open ones, publish everything.

Frontier models from Anthropic, OpenAI, Google, DeepSeek and others are extraordinary, but they live behind APIs. We think their reasoning style should be something you can run on your own hardware. So we capture it, distill it into open-weight bases, and give the whole chain away.

Model releases
66
Datasets
54
All-time downloads
2.2M
HF followers
1.5K

How we work

One pipeline, end to end.

  1. 01

    Curate prompts

    Coding, math, science and open-ended tasks. For agent traces we point real repositories at the teacher and let it work.

    prompts.jsonl

  2. 02

    Generate traces

    Frontier models answer with maximum reasoning effort. Thinking, tool calls and results are captured with teich and published as a dataset.

    teich generate

  3. 03

    Fine-tune an open base

    Qwen, Gemma, GLM, gpt-oss and friends are trained with Unsloth. Response-only masking keeps the loss on what the teacher actually said.

    mask_data()

  4. 04

    Quantize and ship

    Safetensors plus imatrix GGUF from Q2 to Q8, vision projectors when the base sees images, and an Ollama Modelfile. Apache-2.0.

    llama-server -hf

What we stand for

Principles we try not to break.

Open weights, open data

Every model ships with the dataset that made it. If you can download the distill, you can download what it learned from and retrain it yourself.

Open tooling

The generation, extraction and masking pipeline is published as teich on PyPI. Our recipes are reproducible, not folklore.

Honest model cards

Base model, dataset, training setup and known limitations are in every card. We would rather be boring than overclaim.

Community-directed

Distill requests come in through Discord. A lot of what we ship started as someone asking for it.

Get involved

Join the community or fund the next run.

Support the lab

Every coffee turns into API credits and GPU hours.

We are a small team paying for frontier-model generation and training runs out of pocket. A single high-reasoning dataset can cost more than a month of rent in tokens. If our models save you time, consider chipping in, or drop a distill request in the Discord and tell us what to build next.