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
```

yiqichen01 commited on 3 days ago

Commit

e5d67eb

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1 Parent(s): 1f90e61

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README.md +3 -1
README_CN.md +4 -1

README.md CHANGED Viewed

@@ -163,7 +163,9 @@ See the [Deployment](#deployment) section below for how to start the API server.
 ## Deployment
-For production serving, we recommend using [vLLM](https://github.com/vllm-project/vllm) or [SGLang](https://docs.sglang.io/).
 ### vLLM

 ## Deployment
+For production serving, we recommend using [vLLM](https://github.com/vllm-project/vllm) or [SGLang](https://docs.sglang.io/). Please refer to the recipes:
+- [Hy4-Preview vLLM Recipe](https://recipes.vllm.ai/tencent/Hy4-preview)
+- [Hy4-Preview SGLang Cookbook](https://lmsysorg.mintlify.app/cookbook/autoregressive/Tencent/Hy4-Preview)
 ### vLLM

README_CN.md CHANGED Viewed

@@ -151,7 +151,10 @@ print(response.choices[0].message.content)
 ## 推理和部署
-对于生产环境部署，我们建议使用 [vLLM](https://github.com/vllm-project/vllm) 或 [SGLang](https://docs.sglang.io/)。
 ### vLLM

 ## 推理和部署
+对于生产环境部署，我们建议使用 [vLLM](https://github.com/vllm-project/vllm) 或 [SGLang](https://docs.sglang.io/)。请参考部署方案：
+- [Hy4-Preview vLLM Recipe](https://recipes.vllm.ai/tencent/Hy4-preview)
+- [Hy4-Preview SGLang Cookbook](https://lmsysorg.mintlify.app/cookbook/autoregressive/Tencent/Hy4-Preview)
 ### vLLM