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README.md

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+# InternVL2-2B
+
+**Original model repository:** [OpenGVLab/InternVL2-2B](https://huggingface.co/OpenGVLab/InternVL2-2B)
+
+## Model Introduction
+
+InternVL2-2B is an instruction-tuned Vision-Language Model (VLM) for image understanding and text generation. It combines an InternViT-300M vision encoder, an MLP projector, and InternLM2-Chat-1.8B as its language model. Typical applications include visual question answering, OCR, image description, document and chart understanding, and 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 | 41.535 ms |
+| Time to first token (TTFT) | 84.448 ms |
+| Prefill throughput | 13,265.273 tokens/s |
+| Decode throughput | 70.473 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.