metadata
license: other
tags:
- heal
- horizon
Qwen2.5-VL-7B-Instruct
Original model repository: Qwen/Qwen2.5-VL-7B-Instruct
Model Introduction
Qwen2.5-VL-7B-Instruct is an instruction-tuned Vision-Language Model (VLM) for understanding images, videos, and text. It combines a vision transformer with the Qwen2.5 language model and supports visual question answering, OCR, document and chart analysis, visual localization, image description, video understanding, and visual-agent tasks.
Deployment Metrics
Model Parameters
| Metric | Value |
|---|---|
| Total model parameters | 8.292B |
| Vision model (ViT) parameters | 676.6M |
| Language model (LM) parameters | 7.616B |
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 | 43.234 | 200.656 | 3,336.058 | 21.190 | 8.3 | 2.2 |
Note: TTFT includes preprocessing and ViT latency. Memory values represent the peak memory usage measured during the specified performance test.