InternVL
Collection
5 items • Updated
InternVL2.5-2B
Original model repository: OpenGVLab/InternVL2_5-2B
Model Introduction
InternVL2.5-2B is an instruction-tuned Vision-Language Model (VLM) built on the InternVL2.5 architecture. It combines an InternViT-300M vision encoder, an MLP projector, and InternLM2.5-Chat-1.8B as its language model. It is designed for visual question answering, OCR, document and chart understanding, visual grounding, image description, and general multimodal dialogue.
Deployment Metrics
Model Parameters
| Metric | Value |
|---|---|
| Total model parameters | 2.206B |
| Vision model (ViT) parameters | 316.6M |
| Language model (LM) parameters | 1.889B |
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 | 41.984 | 82.650 | 14,105.694 | 71.620 | 2.4 | 0.79 |
Note: TTFT includes preprocessing and ViT latency. Memory values represent the peak memory usage measured during the specified performance test.
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