OpenExplorer/InternVL3_5-1B

InternVL3.5-1B-Instruct

Original model repository: OpenGVLab/InternVL3_5-1B-Instruct

Model Introduction

InternVL3.5-1B-Instruct is an instruction-tuned Vision-Language Model (VLM) for multimodal perception and reasoning. It uses a vision encoder, an MLP projector, and an autoregressive language model to understand images and generate text. The model is designed for tasks such as OCR, document and chart understanding, visual question answering, multimodal reasoning, spatial understanding, and visual-agent applications.

Deployment Metrics

Model Parameters

Metric	Value
Total model parameters	1.061B
Vision model (ViT) parameters	309.3M
Language model (LM) parameters	751.6M

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	28.201	64.765	15,652.844	104.543	1.6	0.76

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