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
```

aqqu-ai commited on 3 days ago

Commit

10ffed0

verified ·

1 Parent(s): 5594790

Card: consistent name MiniMax M3.1-Flash

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -65,7 +65,7 @@ The held-out set comes from the same distribution as the training data, so these
 - 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.
@@ -78,7 +78,7 @@ The held-out set comes from the same distribution as the training data, so these
 ## 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.
 Part of the synthetic training data (598 examples) was generated with MiniMax M3.1-Flash via the Ollama Cloud API during a developer trial. No MiniMax weights were used or modified.

 - 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.
 ## 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.
 Part of the synthetic training data (598 examples) was generated with MiniMax M3.1-Flash via the Ollama Cloud API during a developer trial. No MiniMax weights were used or modified.