Instructions to use XingChen-AGI/Xing4.0-29B-A4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XingChen-AGI/Xing4.0-29B-A4B with Transformers:

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

pipe = pipeline("text-generation", model="XingChen-AGI/Xing4.0-29B-A4B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("XingChen-AGI/Xing4.0-29B-A4B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use XingChen-AGI/Xing4.0-29B-A4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XingChen-AGI/Xing4.0-29B-A4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XingChen-AGI/Xing4.0-29B-A4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XingChen-AGI/Xing4.0-29B-A4B

SGLang

How to use XingChen-AGI/Xing4.0-29B-A4B 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 "XingChen-AGI/Xing4.0-29B-A4B" \
    --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": "XingChen-AGI/Xing4.0-29B-A4B",
		"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 "XingChen-AGI/Xing4.0-29B-A4B" \
        --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": "XingChen-AGI/Xing4.0-29B-A4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use XingChen-AGI/Xing4.0-29B-A4B with Docker Model Runner:
```
docker model run hf.co/XingChen-AGI/Xing4.0-29B-A4B
```

Question regarding active parameters, VRAM requirements

by LolerPanda - opened 8 days ago

Discussion

LolerPanda

8 days ago

Hi XingChen-AGI team,

Thanks for sharing this model! I have a few technical questions regarding deployment and architecture:

What is the recommended GPU VRAM size for running FP16 / BF16 inference?
Are there any specific recommendations or flags needed when using vLLM / SGLang for optimal throughput?

Thanks in advance for your help!

LolerPanda changed discussion title from Question regarding active parameters, VRAM requirements, and context window size to Question regarding active parameters, VRAM requirements 8 days ago

xiongji

XingChen-AGI org 7 days ago

Hi XingChen-AGI team,

Thanks for sharing this model! I have a few technical questions regarding deployment and architecture:

What is the recommended GPU VRAM size for running FP16 / BF16 inference?

Are there any specific recommendations or flags needed when using vLLM / SGLang for optimal throughput?

Thanks in advance for your help!

Since our model supports the mtp method, the basic usage method can be referred to at the following link:https://github.com/XingChen-AGI/Xing4.0-29B-A4B/blob/main/README.md

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment