Instructions to use tencent/Hy4-preview-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tencent/Hy4-preview-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="tencent/Hy4-preview-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("tencent/Hy4-preview-FP8", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tencent/Hy4-preview-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tencent/Hy4-preview-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/Hy4-preview-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tencent/Hy4-preview-FP8

SGLang

How to use tencent/Hy4-preview-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/Hy4-preview-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/Hy4-preview-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/Hy4-preview-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/Hy4-preview-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tencent/Hy4-preview-FP8 with Docker Model Runner:
```
docker model run hf.co/tencent/Hy4-preview-FP8
```

jeeejeee commited on 2 days ago

Commit

a87903e

verified ·

1 Parent(s): e5d67eb

Update vLLM recipe

Browse files

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README.md +1 -25

README.md CHANGED Viewed

@@ -169,31 +169,7 @@ For production serving, we recommend using [vLLM](https://github.com/vllm-projec
 ### vLLM
-Build vLLM from source:
-```bash
-uv venv --python 3.12 --seed --managed-python
-source .venv/bin/activate
-git clone https://github.com/vllm-project/vllm.git
-cd vllm
-uv pip install --editable . --torch-backend=auto
-```
-Start the vLLM server with MTP enabled:
-```bash
-vllm serve tencent/Hy4-preview-FP8 \
-  --tensor-parallel-size 8 \
-  --speculative-config.method mtp \
-  --speculative-config.num_speculative_tokens 3 \
-  --attention-backend FLASHMLA_SPARSE \
-  --tool-call-parser hy_v4 \
-  --reasoning-parser hy_v4 \
-  --enable-auto-tool-choice \
-  --port 8000 \
-  --served-model-name hy4-preview
-```
-Or use official prebuilt image `vllm/vllm-openai:hy4-preview`:
 ```bash
 docker run --gpus all \

 ### vLLM
+Use official prebuilt image `vllm/vllm-openai:hy4-preview`:
 ```bash
 docker run --gpus all \