Instructions to use tencent/Hunyuan-A13B-Instruct-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tencent/Hunyuan-A13B-Instruct-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="tencent/Hunyuan-A13B-Instruct-FP8", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("tencent/Hunyuan-A13B-Instruct-FP8", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tencent/Hunyuan-A13B-Instruct-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tencent/Hunyuan-A13B-Instruct-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tencent/Hunyuan-A13B-Instruct-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tencent/Hunyuan-A13B-Instruct-FP8

SGLang

How to use tencent/Hunyuan-A13B-Instruct-FP8 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 "tencent/Hunyuan-A13B-Instruct-FP8" \
    --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": "tencent/Hunyuan-A13B-Instruct-FP8",
		"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 "tencent/Hunyuan-A13B-Instruct-FP8" \
        --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": "tencent/Hunyuan-A13B-Instruct-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tencent/Hunyuan-A13B-Instruct-FP8 with Docker Model Runner:
```
docker model run hf.co/tencent/Hunyuan-A13B-Instruct-FP8
```

VLLM docker deployment

by chriswritescode - opened Jun 27, 2025

Discussion

chriswritescode

Jun 27, 2025

rank_0_0 for vLLM's torch.compile
(VllmWorker rank=0 pid=280) INFO 06-27 15:55:57 [backends.py:430] Dynamo bytecode transform time: 11.07 s
(VllmWorker rank=1 pid=281) INFO 06-27 15:56:00 [backends.py:136] Cache the graph of shape None for later use
(VllmWorker rank=2 pid=282) INFO 06-27 15:56:00 [backends.py:136] Cache the graph of shape None for later use
(VllmWorker rank=3 pid=283) INFO 06-27 15:56:00 [backends.py:136] Cache the graph of shape None for later use
(VllmWorker rank=0 pid=280) INFO 06-27 15:56:01 [backends.py:136] Cache the graph of shape None for later use
(VllmWorker rank=3 pid=283) INFO 06-27 15:56:33 [backends.py:148] Compiling a graph for general shape takes 36.04 s
(VllmWorker rank=1 pid=281) INFO 06-27 15:56:34 [backends.py:148] Compiling a graph for general shape takes 36.27 s
(VllmWorker rank=2 pid=282) INFO 06-27 15:56:34 [backends.py:148] Compiling a graph for general shape takes 36.31 s
(VllmWorker rank=0 pid=280) INFO 06-27 15:56:35 [backends.py:148] Compiling a graph for general shape takes 37.21 s
(VllmWorker rank=0 pid=280) WARNING 06-27 15:56:39 [fused_moe.py:668] Using default MoE config. Performance might be sub-optimal! Config file not found at /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/configs/E=64,N=768,device_name=NVIDIA_RTX_6000_Ada_Generation,dtype=fp8_w8a8.json
(VllmWorker rank=2 pid=282) WARNING 06-27 15:56:39 [fused_moe.py:668] Using default MoE config. Performance might be sub-optimal! Config file not found at /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/configs/E=64,N=768,device_name=NVIDIA_RTX_6000_Ada_Generation,dtype=fp8_w8a8.json
(VllmWorker rank=1 pid=281) WARNING 06-27 15:56:39 [fused_moe.py:668] Using default MoE config. Performance might be sub-optimal! Config file not found at /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/configs/E=64,N=768,device_name=NVIDIA_RTX_6000_Ada_Generation,dtype=fp8_w8a8.json
(VllmWorker rank=3 pid=283) WARNING 06-27 15:56:39 [fused_moe.py:668] Using default MoE config. Performance might be sub-optimal! Config file not found at /usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/configs/E=64,N=768,device_name=NVIDIA_RTX_6000_Ada_Generation,dtype=fp8_w8a8.json
raise RuntimeError(
ERROR 06-27 15:57:02 [core.py:396] RuntimeError: Worker failed with error 'Unknown CUDA arch (12.0+PTX) or GPU not supported', please check the stack trace above for the root cause
ERROR 06-27 15:57:04 [multiproc_executor.py:123] Worker proc VllmWorker-0 died unexpectedly, shutting down executor.

chriswritescode

Jun 27, 2025

It does have cuda 12.8, not sure why this fails.

chriswritescode changed discussion status to closed Jun 27, 2025

asherszhang

Tencent org Jun 28, 2025

Hi @getfit ,

We found the this issue too, the package build on this docker image have some issue.

you can work around this issue with:
export VLLM_USE_V1=0

or you can uninstall the and rebuild the flashinfer python package inside of image.

We're going to release a new docker image to improve compatibility.

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