Instructions to use tencent/Hunyuan-7B-Instruct-0124 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tencent/Hunyuan-7B-Instruct-0124 with Transformers:

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

pipe = pipeline("text-generation", model="tencent/Hunyuan-7B-Instruct-0124")
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-0124")
model = AutoModelForCausalLM.from_pretrained("tencent/Hunyuan-7B-Instruct-0124")
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/Hunyuan-7B-Instruct-0124 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-0124"
# 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-0124",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tencent/Hunyuan-7B-Instruct-0124

SGLang

How to use tencent/Hunyuan-7B-Instruct-0124 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-0124" \
    --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-0124",
		"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-0124" \
        --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-0124",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tencent/Hunyuan-7B-Instruct-0124 with Docker Model Runner:
```
docker model run hf.co/tencent/Hunyuan-7B-Instruct-0124
```

Request for GGUF support through llama.cpp

by Doctor-Chad-PhD - opened Jan 24, 2025

Discussion

Doctor-Chad-PhD

Jan 24, 2025

Dear Tencent Team,

I would like to request the support of GGUF quantization through the llama.cpp library.
As this will allow more users to use your new model.
The repo for llama.cpp can be found here: https://github.com/ggerganov/llama.cpp.
Thank you for considering this request.

mpasila

Jan 25, 2025

•

edited Jan 25, 2025

Have you created an issue on the Github repo? Since if you do that there will be a bigger chance of it being implemented.

J22

Mar 3, 2025

You can try with chatllm.cpp now.

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