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

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README.md +1 -1
README_CN.md +1 -1

README.md CHANGED Viewed

@@ -120,7 +120,7 @@ We also continue to co-design Hy4 preview with Tencent products like CodeBuddy a
 ## Known Limitations
-This is an early version of Hy4. There is real headroom left in both pre-training and post-training, and we are shipping with known issues — among them, spending longer than necessary reasoning through complex tasks, and a tendency to over-verify its own work. As with Hy3 preview, we would rather ship early and hear what breaks — that's what made Hy3 substantially better, and it's how we will get Hy4 right. We will also keep collaborating closely with Tencent's products and in-house experts to push the boundaries of model intelligence while making it more abundant and affordable.
 ## News

 ## Known Limitations
+This is an early version of Hy4. There is real headroom left in both pre-training and post-training, and we are shipping with known issues — among them, spending longer than necessary reasoning through complex tasks, and a tendency to over-verify its own work. We'll keep iterating quickly on these. As with Hy3 preview, we would rather ship early and hear what breaks — that's what made Hy3 substantially better, and it's how we will get Hy4 right. We will also keep collaborating closely with Tencent's products and in-house experts to push the boundaries of model intelligence while making it more abundant and affordable.
 ## News

README_CN.md CHANGED Viewed

@@ -110,7 +110,7 @@ Hy4 preview 在模型尺寸、上下文长度、数据规模上都进行了显
 ## 已知局限
-Hy4 preview 是 Hy4 迭代的一个早期版本，预训练和后训练均仍有较大的提升空间，也有一些已知问题，如复杂任务上的长思考和过度自我验证倾向。如同 Hy3 preview，我们希望通过 Hy4 preview 的尽快发布获得广泛的真实反馈，从而显著提升 Hy4 正式版。同时，我们将坚持发挥与腾讯产品和专家深度合作的独特优势，持续提升生产力的普惠性和上限。
 ## 新闻

 ## 已知局限
+Hy4 preview 是 Hy4 迭代的一个早期版本，预训练和后训练均仍有较大的提升空间，也有一些已知问题，如复杂任务的长思考和过度自我验证倾向，我们将持续敏捷迭代。如同 Hy3 preview，我们希望通过 Hy4 preview 的尽快发布获得广泛的真实反馈，从而显著提升 Hy4 正式版。同时，我们将坚持发挥与腾讯产品和专家深度合作的独特优势，持续提升生产力的普惠性和上限。
 ## 新闻