Instructions to use AIDXteam/Qwen3-Coder-30B-A3B-Instruct-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AIDXteam/Qwen3-Coder-30B-A3B-Instruct-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="AIDXteam/Qwen3-Coder-30B-A3B-Instruct-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AIDXteam/Qwen3-Coder-30B-A3B-Instruct-AWQ")
model = AutoModelForCausalLM.from_pretrained("AIDXteam/Qwen3-Coder-30B-A3B-Instruct-AWQ", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AIDXteam/Qwen3-Coder-30B-A3B-Instruct-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AIDXteam/Qwen3-Coder-30B-A3B-Instruct-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AIDXteam/Qwen3-Coder-30B-A3B-Instruct-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AIDXteam/Qwen3-Coder-30B-A3B-Instruct-AWQ

SGLang

How to use AIDXteam/Qwen3-Coder-30B-A3B-Instruct-AWQ 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 "AIDXteam/Qwen3-Coder-30B-A3B-Instruct-AWQ" \
    --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": "AIDXteam/Qwen3-Coder-30B-A3B-Instruct-AWQ",
		"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 "AIDXteam/Qwen3-Coder-30B-A3B-Instruct-AWQ" \
        --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": "AIDXteam/Qwen3-Coder-30B-A3B-Instruct-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AIDXteam/Qwen3-Coder-30B-A3B-Instruct-AWQ with Docker Model Runner:
```
docker model run hf.co/AIDXteam/Qwen3-Coder-30B-A3B-Instruct-AWQ
```

Qwen3-Coder-30B-A3B-Instruct-AWQ

File size: 970 Bytes

{
  "quantization_method": "awq",
  "optimization": "moe_optimized",
  "model_architecture": {
    "model_type": "qwen3_moe",
    "total_params": "30.5B",
    "active_params": "3.3B",
    "num_experts": 128,
    "experts_per_token": 8,
    "layers": 48,
    "hidden_size": 2048,
    "attention_heads": 32,
    "kv_heads": 4,
    "context_length": 262144
  },
  "awq_config": {
    "method": "awq",
    "bits": 4,
    "group_size": 128,
    "zero_point": true,
    "version": "gemm",
    "activation_aware": true,
    "auto_scale": true,
    "auto_clip": true
  },
  "protected_layers": [
    "gate",
    "router",
    "shared_expert",
    "layernorm",
    "embedding"
  ],
  "calibration": {
    "num_samples": 512,
    "sequence_length": 4096,
    "shuffle": true,
    "expert_balanced": true
  },
  "performance_notes": {
    "expected_speedup": "3-4x",
    "memory_reduction": "~75%",
    "quality_retention": ">95%"
  },
  "timestamp": "2025-08-07T10:24:34.453585"
}