Instructions to use amd/tiny-qwen3-moe-w8a8-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| { | |
| "architectures": [ | |
| "Qwen3MoeForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "decoder_sparse_step": 1, | |
| "dtype": "bfloat16", | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 2048, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2048, | |
| "max_position_embeddings": 2048, | |
| "mlp_only_layers": [], | |
| "model_type": "qwen3_moe", | |
| "moe_intermediate_size": 1024, | |
| "norm_topk_prob": false, | |
| "num_attention_heads": 16, | |
| "num_experts": 8, | |
| "num_experts_per_tok": 2, | |
| "num_hidden_layers": 2, | |
| "num_key_value_heads": 2, | |
| "output_router_logits": false, | |
| "quantization_config": { | |
| "algo_config": null, | |
| "exclude": [ | |
| "model.layers.0.mlp.gate", | |
| "model.layers.1.mlp.gate", | |
| "lm_head" | |
| ], | |
| "export": { | |
| "kv_cache_group": [], | |
| "min_kv_scale": 0.0, | |
| "pack_method": "reorder", | |
| "weight_format": "real_quantized", | |
| "weight_merge_groups": null | |
| }, | |
| "global_quant_config": { | |
| "bias": null, | |
| "input_tensors": { | |
| "ch_axis": -1, | |
| "dtype": "int8", | |
| "group_size": null, | |
| "is_dynamic": true, | |
| "is_scale_quant": false, | |
| "mx_element_dtype": null, | |
| "observer_cls": "PerChannelMinMaxObserver", | |
| "qscheme": "per_channel", | |
| "round_method": "half_even", | |
| "scale_calculation_mode": null, | |
| "scale_format": null, | |
| "scale_type": "float", | |
| "symmetric": true | |
| }, | |
| "output_tensors": null, | |
| "target_device": null, | |
| "weight": { | |
| "ch_axis": 0, | |
| "dtype": "int8", | |
| "group_size": null, | |
| "is_dynamic": false, | |
| "is_scale_quant": false, | |
| "mx_element_dtype": null, | |
| "observer_cls": "PerChannelMinMaxObserver", | |
| "qscheme": "per_channel", | |
| "round_method": "half_even", | |
| "scale_calculation_mode": null, | |
| "scale_format": null, | |
| "scale_type": "float", | |
| "symmetric": true | |
| } | |
| }, | |
| "kv_cache_post_rope": false, | |
| "kv_cache_quant_config": {}, | |
| "layer_quant_config": {}, | |
| "layer_type_quant_config": {}, | |
| "quant_method": "quark", | |
| "quant_mode": "eager_mode", | |
| "softmax_quant_spec": null, | |
| "version": "0.11.2" | |
| }, | |
| "rms_norm_eps": 1e-06, | |
| "rope_scaling": null, | |
| "rope_theta": 10000.0, | |
| "router_aux_loss_coef": 0.001, | |
| "sliding_window": null, | |
| "tie_word_embeddings": false, | |
| "transformers_version": "4.57.1", | |
| "use_cache": true, | |
| "use_sliding_window": false, | |
| "vocab_size": 151646 | |
| } | |