Instructions to use tencent/Hy3-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tencent/Hy3-preview with Transformers:

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

pipe = pipeline("text-generation", model="tencent/Hy3-preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tencent/Hy3-preview")
model = AutoModelForCausalLM.from_pretrained("tencent/Hy3-preview")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tencent/Hy3-preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tencent/Hy3-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/Hy3-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tencent/Hy3-preview

SGLang

How to use tencent/Hy3-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/Hy3-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/Hy3-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/Hy3-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/Hy3-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tencent/Hy3-preview with Docker Model Runner:
```
docker model run hf.co/tencent/Hy3-preview
```

russellfeng commited on 15 days ago

Commit

35418b7

1 Parent(s): bda30e4

update readme.

Browse files

Files changed (2) hide show

README.md +2 -2
README_CN.md +1 -1

README.md CHANGED Viewed

@@ -162,7 +162,7 @@ from openai import OpenAI
 client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
 response = client.chat.completions.create(
-    model="tencent/Hy3-preview",
     messages=[
         {"role": "user", "content": "Hello! Can you briefly introduce yourself?"},
     ],
@@ -259,4 +259,4 @@ If you would like to leave a message for our R&D and product teams, welcome to c
 <p align="center">
   <i>Hy3 preview is developed by the Tencent Hy Team.</i>
-</p>

 client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
 response = client.chat.completions.create(
+    model="hy3-preview",
     messages=[
         {"role": "user", "content": "Hello! Can you briefly introduce yourself?"},
     ],
 <p align="center">
   <i>Hy3 preview is developed by the Tencent Hy Team.</i>
+</p>

README_CN.md CHANGED Viewed

@@ -157,7 +157,7 @@ from openai import OpenAI
 client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
 response = client.chat.completions.create(
-    model="tencent/Hy3-preview",
     messages=[
         {"role": "user", "content": "你好！请简单介绍一下你自己。"},
     ],

 client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
 response = client.chat.completions.create(
+    model="hy3-preview",
     messages=[
         {"role": "user", "content": "你好！请简单介绍一下你自己。"},
     ],