| --- |
| license: apache-2.0 |
| language: |
| - en |
| - ru |
| pipeline_tag: text-generation |
| tags: |
| - education |
| - tutor |
| - edge |
| - nvidia-jetson |
| - deeptutor |
| - teatcher |
| - qwen3 |
| library_name: transformers |
| --- |
| |
| # teatcher-4B (v0.1 preview — in training) |
|
|
| **The on-device tutor of the [TEA+T+CHER](https://teatcher.com) personal AI learning workstation.** |
| A compact 4B-class model fine-tuned for patient, step-by-step tutoring: building courses from |
| documents, drilling quizzes with citations to the exact source page, correcting misconceptions, |
| and answering study questions — entirely on-device, with no usage fees and no network required. |
|
|
| > 🚧 **Weights are in training and will be published here.** This card already follows the final |
| > format; TBD values below are filled in at release. |
|
|
| ## Quick start |
|
|
| Ollama: |
|
|
| ```bash |
| ollama run qaztech-platform/teatcher-4b |
| ``` |
|
|
| Transformers: |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| m = "qaztech-platform/teatcher-4B" |
| tok = AutoTokenizer.from_pretrained(m) |
| model = AutoModelForCausalLM.from_pretrained(m, torch_dtype="auto", device_map="auto") |
| msgs = [{"role": "system", "content": "You are a patient tutor. Teach step by step, cite the source page."}, |
| {"role": "user", "content": "Quiz me on entropy from my thermodynamics notes."}] |
| ids = tok.apply_chat_template(msgs, add_generation_prompt=True, enable_thinking=False, return_tensors="pt").to(model.device) |
| print(tok.decode(model.generate(ids, max_new_tokens=400)[0][ids.shape[1]:], skip_special_tokens=True)) |
| ``` |
|
|
| Use `enable_thinking=False`: the model is trained without reasoning traces. |
|
|
| ## Training (completed at release) |
|
|
| - Base: Qwen3-4B class, Apache-2.0 (final base and version stated here at release, with attribution). |
| - Method: LoRA (rank/alpha TBD, all linear layers), merged into the base weights, bf16. |
| - Data: synthetic tutoring dialogues (counts TBD), generated from public educational material |
| and reviewed for this project. **No customer or workstation user data — ever.** |
| - Modes: course building from documents · step-by-step explanation · certification-style quiz |
| generation with page citations · misconception correction. |
| - Languages: English, Russian. |
| - Filters: question and answer in the same language, no references to unseen source text, |
| no mixed scripts, deduplication. |
| - Held-out eval: loss TBD, token accuracy TBD. |
|
|
| ## Edge deployment |
|
|
| Sized for NVIDIA Jetson Orin Nano Super 8 GB: GGUF Q4_K_M (~2.5–3 GB) leaves room for the |
| system, DeepTutor and the user's document index. Recommended `num_ctx 8192`, power mode MAXN SUPER. |
|
|
| ## Limitations |
|
|
| - Facts can be wrong. Inside the workstation the model runs with retrieval over the user's own |
| documents (DeepTutor), and answers cite the source page — use it the same way standalone (RAG). |
| - A 4B model is a tutor, not an oracle: no professional medical, legal or financial advice. |
| - Capabilities outside the tutoring modes above are untested and not promised. |
|
|
| ## Links |
|
|
| - Product: https://teatcher.com · Contact: access@teatcher.com |
| - Tutor environment (open source): https://github.com/HKUDS/DeepTutor |
| - Source & docs: https://github.com/qaztech-platform/teatcher-4b |
|
|
| TEA+T+CHER is a registered trademark of AISC Technologies Ltd (United Kingdom), UK00004283802, |
| used under licence by AL SAFI FZE. NVIDIA and Jetson are trademarks of NVIDIA Corporation. |
