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
Test Configuration
| Metric | Value |
|---|---|
| Platform | Matrix6P |
| Data type | W8A8 |
| ViT image size | 448 × 448 |
| Sequence length | 512 |
| Maximum context length | 1024 |
| BPU cores (ViT / Prefill / Decode) | 4 / 4 / 4 |
Performance Results
| Metric | Value |
|---|---|
| ViT latency | 41.984 ms |
| Time to first token (TTFT) | 82.650 ms |
| Prefill throughput | 14,105.694 tokens/s |
| Decode throughput | 71.620 tokens/s |
Memory Usage
| Metric | Value |
|---|---|
| BPU memory | 2.4 GB |
| CPU memory | 0.79 GB |
Note: TTFT includes preprocessing and ViT latency. Memory values represent the peak memory usage measured during the specified performance test.
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