metadata
license: other
tags:
- heal
- horizon
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) |
|---|---|---|---|---|---|---|---|---|---|---|---|
| J6P | 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.