MODEL PROFILE

Qwen2-VL-2B

litert-community · Qwen2-VL-2B

LiteRT is Google's on-device runtime, the new name for TensorFlow Lite (Android: `com.google.ai.edge.litert:litert`), and `litert-torch`, the renamed `ai-edge-torch`, is its PyTorch converter: a PyTorch model converted unmodified with `litert_torch.convert` matched the original to 4e-7 on a Galaxy S26 (measured, LiteRT 2.2.0, Android 16, 2026-09-05). Qwen/Qwen2-VL-2B-Instruct converted to the **LiteRT-LM** (`.litertlm`) format for **on-device image+text** inference with Google's LiteRT-LM runtime — the first Qwen2-VL-family VLM in this format. Qwen2-VL is Alibaba's widely-used general-purpose vision-language model: a dynamic-resolution ViT vision encoder feeds the **Qwen2-1.5B** language decoder, giving strong general VQA, description, and document/OCR reading in a phone-sized package. This bundle runs the whole thing through LiteRT-LM's `fast_vlm` multimodal path — give it an image and a question, get a grounded answer, fully on-device.

Visual QAmultimodallicense:apache-2.0
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/11/2 19:15:39
OVERVIEW

Model overview

LiteRT is Google's on-device runtime, the new name for TensorFlow Lite (Android: `com.google.ai.edge.litert:litert`), and `litert-torch`, the renamed `ai-edge-torch`, is its PyTorch converter: a PyTorch model converted unmodified with `litert_torch.convert` matched the original to 4e-7 on a Galaxy S26 (measured, LiteRT 2.2.0, Android 16, 2026-09-05). Qwen/Qwen2-VL-2B-Instruct converted to the **LiteRT-LM** (`.litertlm`) format for **on-device image+text** inference with Google's LiteRT-LM runtime — the first Qwen2-VL-family VLM in this format. Qwen2-VL is Alibaba's widely-used general-purpose vision-language model: a dynamic-resolution ViT vision encoder feeds the **Qwen2-1.5B** language decoder, giving strong general VQA, description, and document/OCR reading in a phone-sized package. This bundle runs the whole thing through LiteRT-LM's `fast_vlm` multimodal path — give it an image and a question, get a grounded answer, fully on-device.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Source / channelInputOutputBilling unitCollected at
No verified prices yet. Check the source channel.

Estimate monthly base token costs

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Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.

INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

Are these real-time billing prices?

These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.

Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。