Instructions to use qaztech-platform/QAZTECH-AZ104-Tutor-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qaztech-platform/QAZTECH-AZ104-Tutor-8B with Transformers:

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

pipe = pipeline("text-generation", model="qaztech-platform/QAZTECH-AZ104-Tutor-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("qaztech-platform/QAZTECH-AZ104-Tutor-8B")
model = AutoModelForCausalLM.from_pretrained("qaztech-platform/QAZTECH-AZ104-Tutor-8B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use qaztech-platform/QAZTECH-AZ104-Tutor-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qaztech-platform/QAZTECH-AZ104-Tutor-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qaztech-platform/QAZTECH-AZ104-Tutor-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/qaztech-platform/QAZTECH-AZ104-Tutor-8B

SGLang

How to use qaztech-platform/QAZTECH-AZ104-Tutor-8B 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/QAZTECH-AZ104-Tutor-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qaztech-platform/QAZTECH-AZ104-Tutor-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/QAZTECH-AZ104-Tutor-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qaztech-platform/QAZTECH-AZ104-Tutor-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use qaztech-platform/QAZTECH-AZ104-Tutor-8B with Docker Model Runner:
```
docker model run hf.co/qaztech-platform/QAZTECH-AZ104-Tutor-8B
```

QAZTECH® AZ-104 Tutor 8B (v0.2 preview)

Multilingual engineering tutor for Microsoft Azure topics. It explains concepts, corrects misconceptions and writes certification-style practice questions with explanations, in English, Russian, Kazakh, Arabic and Spanish.

Quick start

Ollama: ollama run qaztech-platform/az104-tutor

Transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer
m = "qaztech-platform/QAZTECH-AZ104-Tutor-8B"
tok = AutoTokenizer.from_pretrained(m)
model = AutoModelForCausalLM.from_pretrained(m, torch_dtype="auto", device_map="auto")
msgs = [{"role": "system", "content": "You are an expert engineering tutor. Reply in the student's language."},
        {"role": "user", "content": "Give me a practice question on Azure RBAC"}]
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 was trained without reasoning traces.

Training

Base: Qwen/Qwen3-8B (Apache-2.0). LoRA (r=16, alpha=32, all linear layers), 3 epochs, lr 1e-4, bf16, merged into the base weights.
Data: 5,107 synthetic tutoring dialogues (4,907 train / 200 held out), generated by Qwen3-30B-A3B-Instruct-2507 from Microsoft Azure documentation on role-based access control, virtual networks, Azure Resource Manager and App Service.
Modes: explanation 1,804; misconception correction 1,613; certification-style practice questions 1,690.
Languages: ru 1,149; kk 1,023; es 1,011; en 980; ar 944.
Filters: question and answer in the same language, no references to unseen source text, no mixed scripts, deduplication.
Held-out eval loss 0.795, token accuracy 0.773.

Limitations

Facts can be wrong: incorrect permission names and behaviours were observed in testing. Use with retrieval over official documentation (RAG) and verify against Microsoft Learn.
Kazakh and Arabic output has not yet been reviewed by native speakers; Kazakh phrasing can be awkward.
Coverage is limited to the four Azure documentation areas above.
Contains no real Microsoft exam questions. Not affiliated with or endorsed by Microsoft.

Licence and attribution

Weights: Apache-2.0. Base model: Qwen3-8B by Alibaba Cloud (Apache-2.0). Training data was derived from Microsoft Azure documentation (github.com/MicrosoftDocs/azure-docs), licensed under CC BY 4.0. Microsoft and Azure are trademarks of the Microsoft group of companies.

Trademark

QAZTECH is a registered trade mark of AISC Technologies LTD in the United Kingdom (UK IPO nos. UK00004263700, UK00004317681, UK00004373195). The Apache-2.0 licence does not grant rights to use the QAZTECH name or logo (section 6). Derivative models must not use "QAZTECH" in their name; describe them as "based on QAZTECH AZ-104 Tutor".