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

Libraries

How to use qaztech-platform/QAZTECH-Azure-Engineer-8B with Transformers:

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

pipe = pipeline("text-generation", model="qaztech-platform/QAZTECH-Azure-Engineer-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-Azure-Engineer-8B")
model = AutoModelForCausalLM.from_pretrained("qaztech-platform/QAZTECH-Azure-Engineer-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-Azure-Engineer-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-Azure-Engineer-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-Azure-Engineer-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/qaztech-platform/QAZTECH-Azure-Engineer-8B

SGLang

How to use qaztech-platform/QAZTECH-Azure-Engineer-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-Azure-Engineer-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-Azure-Engineer-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-Azure-Engineer-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-Azure-Engineer-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use qaztech-platform/QAZTECH-Azure-Engineer-8B with Docker Model Runner:
```
docker model run hf.co/qaztech-platform/QAZTECH-Azure-Engineer-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

QAZTECH® Azure Engineer 8B

Open multilingual assistant for Azure engineers. It writes, fixes, explains and converts infrastructure code, migrates ARM templates to Bicep or Terraform, reviews deployments for cost (FinOps tags Environment, CostCenter, Owner; lower-cost dev SKUs) and troubleshoots failed deployments from their error logs. Prose in English, Russian, Kazakh, Arabic and Spanish; code, commands and comments in English.

Tools: Azure CLI, Azure PowerShell, Bicep, ARM JSON, Terraform (azurerm, azapi), Azure Policy and custom RBAC roles, KQL, Azure Pipelines and GitHub Actions YAML.

Try it

ollama run qaztech-platform/azure-engineer

from transformers import AutoModelForCausalLM, AutoTokenizer
m = "qaztech-platform/QAZTECH-Azure-Engineer-8B"
tok = AutoTokenizer.from_pretrained(m)
model = AutoModelForCausalLM.from_pretrained(m, torch_dtype="auto", device_map="auto")
msgs = [{"role": "system", "content": "You are QAZTECH Azure Engineer, an assistant for Azure engineers. Reply in the user's language; keep code, commands and code comments in English."},
        {"role": "user", "content": "Migrate this ARM template to Bicep: ..."}]
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=1500)[0][ids.shape[1]:], skip_special_tokens=True))

Use enable_thinking=False: the model was trained without reasoning traces.

Evaluation

200 held-out examples (not used in training), same system prompt, temperature 0.3. Code blocks are checked with real tools: Azure CLI 2.89.1 (--help for every command and flag), Az PowerShell modules (cmdlets and parameters), Bicep CLI 0.48.1 (bicep build), Terraform 1.16.6 with azurerm 5.10.0 and azapi 2.13.0 (terraform validate).

Model	Answer in the right language	Reasoning-trace leaks	Answers with all code valid	Code blocks with errors
QAZTECH Azure Engineer 8B	99%	0	76%	21%
Qwen3-8B (base, same prompt)	98%	0	65%	22%

The held-out set comes from the same distribution as the training data, so these numbers measure in-distribution quality. Tool checks verify that commands, flags, parameters, resource types and properties exist; they do not verify values (for example VM size names).

Training

Base: Qwen/Qwen3-8B (Apache-2.0). LoRA r=16, alpha=32, dropout 0.05, all linear layers; 3 epochs, lr 1e-4 cosine, effective batch 32, max length 3,072; 1x A100 40 GB; merged into bf16 weights.
Held-out eval loss 0.461, token accuracy 0.865.
Data: 4,716 synthetic examples (4,516 train, 200 held out) generated from code samples in Microsoft Learn documentation (CC BY 4.0) by teacher models Qwen3-30B-A3B-Instruct-2507 (4,118 examples, run locally) and MiniMax M3.1 Flash via Ollama Cloud (598 examples). Terraform prompts included the authoritative azurerm 5.10 resource schema.
Every code block was checked with the tools listed above; failing examples were repaired with tool-grounded hints (913) or dropped. Language match, leak and duplicate filters applied.
Modes: troubleshoot 951, finops 868, fix 824, write 726, explain 717, convert 353, migrate 277. Languages: ar 996, ru 995, es 987, en 939, kk 799.

Limitations

Facts can be wrong: invented flags or parameters and wrong values (for example B2s instead of Standard_B2s) were observed. Verify every command against Microsoft Learn and run what-if / terraform plan before applying.
Terraform (54 examples) and Bicep (226) are less represented than Azure CLI, PowerShell and ARM.
Kazakh and Arabic output has not been reviewed by native speakers.
Not affiliated with or endorsed by Microsoft or HashiCorp.

Licence and attribution

Weights: Apache-2.0. Base model: Qwen3-8B by Alibaba Cloud (Apache-2.0). Teacher models: Qwen3-30B-A3B-Instruct-2507 (Apache-2.0) and MiniMax M3.1 Flash (accessed through Ollama Cloud). Source material: Microsoft Learn documentation, CC BY 4.0. Microsoft and Azure are trademarks of the Microsoft group of companies; Terraform is a trademark of HashiCorp.

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 Azure Engineer".