InternVL2-1B

Original model repository: OpenGVLab/InternVL2-1B

Model Introduction

InternVL2-1B is an instruction-tuned Vision-Language Model (VLM) for understanding images and generating text responses. It uses an InternViT-300M vision encoder, an MLP projector, and Qwen2-0.5B-Instruct as its language model. The model can be used for visual question answering, image description, OCR, document and chart understanding, and general multimodal dialogue.

Deployment Metrics

Model Parameters

Metric	Value
Total model parameters	938.2M
Vision model (ViT) parameters	308.5M
Language model (LM) parameters	629.7M

Parameter counts are calculated from the tensors stored in the upstream checkpoint.

Performance Metrics

Chips	Data Type	ViT Image Size	Sequence Length (tokens)	Maximum Context Length (tokens)	BPU Cores (ViT / Prefill / Decode)	ViT Latency (ms)	TTFT (ms)	Prefill TPS (token/s)	Decode TPS (token/s)	BPU Memory (GB)	CPU Memory (GB)
Matrix6P	W8A8	448 × 448	512	1024	4 / 4 / 4	41.451	65.766	24,955.227	159.481	1.01	0.68

Note: TTFT includes preprocessing and ViT latency. Memory values represent the peak memory usage measured during the specified performance test.