Instructions to use PerRing/InternVL-MoonViT-V2-MiniCPM5-2B-HF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PerRing/InternVL-MoonViT-V2-MiniCPM5-2B-HF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="PerRing/InternVL-MoonViT-V2-MiniCPM5-2B-HF", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModelForImageTextToText model = AutoModelForImageTextToText.from_pretrained("PerRing/InternVL-MoonViT-V2-MiniCPM5-2B-HF", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use PerRing/InternVL-MoonViT-V2-MiniCPM5-2B-HF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PerRing/InternVL-MoonViT-V2-MiniCPM5-2B-HF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PerRing/InternVL-MoonViT-V2-MiniCPM5-2B-HF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/PerRing/InternVL-MoonViT-V2-MiniCPM5-2B-HF
- SGLang
How to use PerRing/InternVL-MoonViT-V2-MiniCPM5-2B-HF 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 "PerRing/InternVL-MoonViT-V2-MiniCPM5-2B-HF" \ --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": "PerRing/InternVL-MoonViT-V2-MiniCPM5-2B-HF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "PerRing/InternVL-MoonViT-V2-MiniCPM5-2B-HF" \ --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": "PerRing/InternVL-MoonViT-V2-MiniCPM5-2B-HF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use PerRing/InternVL-MoonViT-V2-MiniCPM5-2B-HF with Docker Model Runner:
docker model run hf.co/PerRing/InternVL-MoonViT-V2-MiniCPM5-2B-HF
InternVL-MoonViT-V2-MiniCPM5-2B-HF
This model combines the MoonViT V2 vision encoder from Kimi K3 with the MiniCPM5-2B language model.
The multimodal projector (MLP) was not trained. Please keep this in mind when evaluating or using the model.
Model architecture
- Vision encoder: MoonViT V2 from Kimi K3
- Language model: MiniCPM5-2B
- Multimodal projector (MLP): not trained
Loading the model
import torch
from transformers import AutoModelForImageTextToText, AutoProcessor
model_id = "PerRing/InternVL-MoonViT-V2-MiniCPM5-2B-HF"
processor = AutoProcessor.from_pretrained(
model_id,
trust_remote_code=True,
)
model = AutoModelForImageTextToText.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
).eval()
Note: This repository uses custom modeling and processing code, so
trust_remote_code=Trueis required.
- Downloads last month
- 115