Instructions to use qaztech-platform/teatcher-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qaztech-platform/teatcher-4B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="qaztech-platform/teatcher-4B")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("qaztech-platform/teatcher-4B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use qaztech-platform/teatcher-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qaztech-platform/teatcher-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qaztech-platform/teatcher-4B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/qaztech-platform/teatcher-4B

SGLang

How to use qaztech-platform/teatcher-4B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "qaztech-platform/teatcher-4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qaztech-platform/teatcher-4B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "qaztech-platform/teatcher-4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qaztech-platform/teatcher-4B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use qaztech-platform/teatcher-4B with Docker Model Runner:
```
docker model run hf.co/qaztech-platform/teatcher-4B
```

teatcher-4B / README.md

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metadata

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 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:

ollama run qaztech-platform/teatcher-4b

Transformers:

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.

qaztech-platform
/

teatcher-4B

teatcher-4B (v0.1 preview — in training)

Quick start

Training (completed at release)

Edge deployment

Limitations

Links