Instructions to use tencent/Hunyuan-7B-Instruct-AWQ-Int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tencent/Hunyuan-7B-Instruct-AWQ-Int4 with Transformers:

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

pipe = pipeline("text-generation", model="tencent/Hunyuan-7B-Instruct-AWQ-Int4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tencent/Hunyuan-7B-Instruct-AWQ-Int4")
model = AutoModelForCausalLM.from_pretrained("tencent/Hunyuan-7B-Instruct-AWQ-Int4", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tencent/Hunyuan-7B-Instruct-AWQ-Int4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tencent/Hunyuan-7B-Instruct-AWQ-Int4"
# 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/Hunyuan-7B-Instruct-AWQ-Int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tencent/Hunyuan-7B-Instruct-AWQ-Int4

SGLang

How to use tencent/Hunyuan-7B-Instruct-AWQ-Int4 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/Hunyuan-7B-Instruct-AWQ-Int4" \
    --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/Hunyuan-7B-Instruct-AWQ-Int4",
		"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/Hunyuan-7B-Instruct-AWQ-Int4" \
        --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/Hunyuan-7B-Instruct-AWQ-Int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tencent/Hunyuan-7B-Instruct-AWQ-Int4 with Docker Model Runner:
```
docker model run hf.co/tencent/Hunyuan-7B-Instruct-AWQ-Int4
```

yuhaozhangz commited on Aug 4, 2025

Commit

6142f56

verified ·

1 Parent(s): 76d528d

Update README.md

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README.md +9 -14

README.md CHANGED Viewed

@@ -4,10 +4,6 @@ base_model:
 library_name: transformers
 ---
-<p align="left">
-    <a href="README_CN.md">中文</a>&nbsp ｜ English</a>
-</p>
-<br><br>
 <p align="center">
  <img src="https://dscache.tencent-cloud.cn/upload/uploader/hunyuan-64b418fd052c033b228e04bc77bbc4b54fd7f5bc.png" width="400"/> <br>
@@ -15,21 +11,20 @@ library_name: transformers
 <p align="center">
-    🤗&nbsp;<a href="https://huggingface.co/tencent/"><b>Hugging Face</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
-    <img src="https://avatars.githubusercontent.com/u/109945100?s=200&v=4" width="16"/>&nbsp;<a href="https://modelscope.cn/models/Tencent-Hunyuan/Hunyuan-A13B-Instruct"><b>ModelScope</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
-<img src="https://cdn-avatars.huggingface.co/v1/production/uploads/6594d0c6c5f1cd69a48b261d/04ZNQlAfs08Bfg4B1o3XO.png" width="14"/>&nbsp;<a href="https://github.com/Tencent/AngelSlim/tree/main"><b>AngelSlim</b></a>
-</p>
-<p align="center">
-    🖥️&nbsp;<a href="https://hunyuan.tencent.com" style="color: red;"><b>Official Website</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
     🕖&nbsp;<a href="https://cloud.tencent.com/product/hunyuan"><b>HunyuanAPI</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
-    🕹️&nbsp;<a href="https://hunyuan.tencent.com/"><b>Demo</b></a>&nbsp;&nbsp;&nbsp;&nbsp;
 </p>
 <p align="center">
     <a href="https://github.com/Tencent-Hunyuan/Hunyuan-7B"><b>GITHUB</b></a> |
     <a href="https://cnb.cool/tencent/hunyuan/Hunyuan-7B"><b>cnb.cool</b></a> |
-    <a href="https://github.com/Tencent-Hunyuan/Hunyuan-7B/blob/main/LICENSE"><b>LICENSE</b></a>
 </p>
@@ -47,7 +42,7 @@ We have released a series of Hunyuan dense models, comprising both pre-trained a
 - **Efficient Inference**: Utilizes Grouped Query Attention (GQA) and supports multiple quantization formats, enabling highly efficient inference.
 ## Related News
-* 2025.7.30 We have open-sourced  **Hunyuan-0.5B-Pretrain** , **Hunyuan-1.8B-Pretrain** , **Hunyuan-4B-Pretrain** , **Hunyuan-7B-Pretrain** , **Hunyuan-0.5B-Instruct** , **Hunyuan-1.8B-Instruct** , **Hunyuan-4B-Instruct** , **Hunyuan-7B-Instruct** on Hugging Face.
 <br>

 library_name: transformers
 ---
 <p align="center">
  <img src="https://dscache.tencent-cloud.cn/upload/uploader/hunyuan-64b418fd052c033b228e04bc77bbc4b54fd7f5bc.png" width="400"/> <br>
 <p align="center">
+    🤗&nbsp;<a href="https://huggingface.co/tencent/Hunyuan-7B-Instruct-AWQ-Int4"><b>Hugging Face</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
+     🖥️&nbsp;<a href="https://hunyuan.tencent.com" style="color: red;"><b>Official Website</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
     🕖&nbsp;<a href="https://cloud.tencent.com/product/hunyuan"><b>HunyuanAPI</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
+    🕹️&nbsp;<a href="https://hunyuan.tencent.com/"><b>Demo</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
+    🤖&nbsp;<a href="https://www.modelscope.cn/models/Tencent-Hunyuan/Hunyuan-7B-Instruct-AWQ-Int4"><b>ModelScope</b></a>
 </p>
 <p align="center">
     <a href="https://github.com/Tencent-Hunyuan/Hunyuan-7B"><b>GITHUB</b></a> |
     <a href="https://cnb.cool/tencent/hunyuan/Hunyuan-7B"><b>cnb.cool</b></a> |
+    <a href="https://github.com/Tencent-Hunyuan/Hunyuan-7B/blob/main/LICENSE.txt"><b>LICENSE</b></a> |
+    <a href="https://raw.githubusercontent.com/Tencent-Hunyuan/Hunyuan-A13B/main/assets/1751881231452.jpg"><b>WeChat</b></a> |
+    <a href="https://discord.gg/bsPcMEtV7v"><b>Discord</b></a>
 </p>
 - **Efficient Inference**: Utilizes Grouped Query Attention (GQA) and supports multiple quantization formats, enabling highly efficient inference.
 ## Related News
+* 2025.7.30 We have open-sourced  **Hunyuan-0.5B-Pretrain** ,  **Hunyuan-0.5B-Instruct** , **Hunyuan-1.8B-Pretrain** ,  **Hunyuan-1.8B-Instruct** , **Hunyuan-4B-Pretrain** ,  **Hunyuan-4B-Instruct** , **Hunyuan-7B-Pretrain** ,**Hunyuan-7B-Instruct** on Hugging Face.
 <br>