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

Libraries

How to use tencent/Hy4-preview with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tencent/Hy4-preview")
model = AutoModelForCausalLM.from_pretrained("tencent/Hy4-preview", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tencent/Hy4-preview with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

yiqichen01 commited on Aug 28

Commit

215d117

verified ·

1 Parent(s): 3595342

update

Browse files

Files changed (2) hide show

README.md +2 -2
README_CN.md +2 -2

README.md CHANGED Viewed

@@ -42,7 +42,7 @@ tags:
 - [Model Introduction](#model-introduction)
 - [A New Flagship Generation](#a-new-flagship-generation)
-- [Built for Real-World Productivity](#built-for-real-world-productivity)
 - [Benchmark Appendix](#benchmark-appendix)
 - [Known Limitations](#known-limitations)
 - [News](#news)
@@ -98,7 +98,7 @@ We scaled Hy4 preview on three fronts: model size, context length, and training
   <img src="assets/benchmark.jpg" width="100%"/>
 </p>
-## Built for Real-World Productivity
 We partnered with top experts inside Tencent — such as software engineers, game developers, finance analysts, and security experts — and built training data around the work they ship. The result is a model that gets meaningfully further on the tasks these teams run every day:

 - [Model Introduction](#model-introduction)
 - [A New Flagship Generation](#a-new-flagship-generation)
+- [Built for Productivity](#built-for-productivity)
 - [Benchmark Appendix](#benchmark-appendix)
 - [Known Limitations](#known-limitations)
 - [News](#news)
   <img src="assets/benchmark.jpg" width="100%"/>
 </p>
+## Built for Productivity
 We partnered with top experts inside Tencent — such as software engineers, game developers, finance analysts, and security experts — and built training data around the work they ship. The result is a model that gets meaningfully further on the tasks these teams run every day:

README_CN.md CHANGED Viewed

@@ -32,7 +32,7 @@
 - [模型介绍](#模型介绍)
 - [新一代旗舰模型](#新一代旗舰模型)
-- [为真实生产力而生](#为真实生产力而生)
 - [Benchmark 附录](#benchmark-附录)
 - [已知局限](#已知局限)
 - [新闻](#新闻)
@@ -88,7 +88,7 @@ Hy4 preview 在模型尺寸、上下文长度、数据规模上都进行了显
   <img src="assets/benchmark.jpg" width="100%"/>
 </p>
-## 为真实生产力而生
 通过与腾讯内部软件工程师、游戏开发者、金融分析师、安全专家等各领域顶尖专家的高质量数据共建，Hy4 preview 在各类真实生产力任务上取得显著进步：

 - [模型介绍](#模型介绍)
 - [新一代旗舰模型](#新一代旗舰模型)
+- [为生产力而生](#为生产力而生)
 - [Benchmark 附录](#benchmark-附录)
 - [已知局限](#已知局限)
 - [新闻](#新闻)
   <img src="assets/benchmark.jpg" width="100%"/>
 </p>
+## 为生产力而生
 通过与腾讯内部软件工程师、游戏开发者、金融分析师、安全专家等各领域顶尖专家的高质量数据共建，Hy4 preview 在各类真实生产力任务上取得显著进步：