Instructions to use amd/tiny-qwen3-moe-w8a8-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amd/tiny-qwen3-moe-w8a8-int8 with Transformers:

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

pipe = pipeline("text-generation", model="amd/tiny-qwen3-moe-w8a8-int8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("amd/tiny-qwen3-moe-w8a8-int8")
model = AutoModelForCausalLM.from_pretrained("amd/tiny-qwen3-moe-w8a8-int8", 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 amd/tiny-qwen3-moe-w8a8-int8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amd/tiny-qwen3-moe-w8a8-int8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amd/tiny-qwen3-moe-w8a8-int8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amd/tiny-qwen3-moe-w8a8-int8

SGLang

How to use amd/tiny-qwen3-moe-w8a8-int8 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 "amd/tiny-qwen3-moe-w8a8-int8" \
    --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": "amd/tiny-qwen3-moe-w8a8-int8",
		"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 "amd/tiny-qwen3-moe-w8a8-int8" \
        --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": "amd/tiny-qwen3-moe-w8a8-int8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amd/tiny-qwen3-moe-w8a8-int8 with Docker Model Runner:
```
docker model run hf.co/amd/tiny-qwen3-moe-w8a8-int8
```

tiny-qwen3-moe-w8a8-int8 / README.md

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	---
	library_name: transformers
	license: apache-2.0
	pipeline_tag: text-generation
	tags: [vllm_ci]
	---

	# Model Overview

	- Model Architecture: Qwen3MoeForCausalLM (tiny, randomly initialized)
	- Input: Text
	- Output: Text
	- Supported Hardware Microarchitecture: AMD MI300 / MI350 / MI355 (gfx942 / gfx950), NVIDIA (SM ≥ 7.5)
	- Inference Engine: [vLLM](https://docs.vllm.ai/en/latest/)
	- Model Optimizer: [AMD-Quark](https://quark.docs.amd.com/latest/index.html)
	- Weight quantization: INT8, per-channel, static, symmetric
	- Activation quantization: INT8, per-token, dynamic, symmetric
	- Quantized layers: attention (`self_attn`) and the routed MoE experts.
	The MoE router (`mlp.gate`) and `lm_head` are kept in the original precision.

	This is a tiny, randomly-initialized Qwen3-MoE model quantized to W8A8-INT8,
	used purely as vLLM CI coverage for the Quark INT8 fused-MoE path
	(`QuarkW8A8Int8MoEMethod`), which dispatches through the modular Triton INT8 MoE
	kernel. It is not intended to produce meaningful text.

	> Note: the router (`mlp.gate`) is intentionally left in bf16. Its weight is
	> `[num_experts, hidden]` and `num_experts` (8) is not a multiple of 16, which
	> would trip AITER's INT8 linear-kernel shape assertion. Routers are normally
	> left in high precision anyway.

	# Model Creation

	Built and quantized with [AMD-Quark](https://quark.docs.amd.com/latest/index.html):
	per-channel INT8 weights (static) + per-token INT8 activations (dynamic),
	symmetric, on a tiny `qwen3_moe` model. Attention and routed experts are
	quantized; `mlp.gate` and `lm_head` are excluded. `vocab_size` matches the
	tokenizer so token ids stay within the embedding table.

	# Usage in vLLM

	```bash
	vllm serve amd/tiny-qwen3-moe-w8a8-int8 --enforce-eager
	```

	Because the weights are random, outputs are not meaningful — this model is a
	structural / smoke-test fixture only.

	# License

	Apache-2.0. The tiny model is randomly initialized and derives no weights from
	any base model.

	Modifications Copyright(c) 2026 Advanced Micro Devices, Inc. All rights reserved.