v0.57.0

LICENSE

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+The license of the original trained model can be found at https://www.apache.org/licenses/LICENSE-2.0.

README.md

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+---
+library_name: pytorch
+license: other
+tags:
+- llm
+- vlm
+- generative_ai
+- android
+pipeline_tag: text-generation
+
+---
+
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/qwen3_vl_2b_instruct/web-assets/model_demo.png)
+
+# Qwen3-VL-2B-Instruct: Optimized for Qualcomm Devices
+
+Qwen3-VL is a vision-language model from Alibaba Cloud capable of understanding both text and images for multimodal reasoning tasks such as visual question answering and image captioning.
+
+This is based on the implementation of Qwen3-VL-2B-Instruct found [here](https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct).
+This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.57.0/src/qai_hub_models/models/qwen3_vl_2b_instruct) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
+
+Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
+
+## Deploying Qwen3-VL-2B-Instruct on-device
+
+Follow the [GenieX quickstart](https://geniex.aihub.qualcomm.com/en/get-started/quickstart) to install GenieX and deploy the model on a target device.
+
+## Getting Started
+There are two ways to deploy this model on your device:
+
+### Option 1: Download Pre-Exported Models
+
+Below are pre-exported model assets ready for deployment.
+
+| Runtime | Precision | Chipset | SDK Versions | Download |
+|---|---|---|---|---|
+| GENIEX_LLAMACPP | q4_0 | Universal |  | [Download](https://huggingface.co/unsloth/Qwen3-VL-2B-Instruct-GGUF/resolve/main/Qwen3-VL-2B-Instruct-Q4_0.gguf)
+
+For more device-specific assets and performance metrics, visit **[Qwen3-VL-2B-Instruct on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/qwen3_vl_2b_instruct)**.
+
+
+### Option 2: Export with Custom Configurations
+
+Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.57.0/src/qai_hub_models/models/qwen3_vl_2b_instruct) Python library to compile and export the model with your own:
+- Custom weights (e.g., fine-tuned checkpoints)
+- Custom input shapes
+- Target device and runtime configurations
+
+This option is ideal if you need to customize the model beyond the default configuration provided here.
+
+See our repository for [Qwen3-VL-2B-Instruct on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.57.0/src/qai_hub_models/models/qwen3_vl_2b_instruct) for usage instructions.
+
+## Model Details
+
+**Model Type:** Model_use_case.text_generation
+
+**Model Stats:**
+- Model architecture: Transformer with ViT Vision Encoder, Grouped Query Attention (GQA), and SwiGLU activation.
+- Supported languages: 100+ languages and dialects
+- TTFT: Time To First Token is the time it takes to generate the first response token. This is expressed as a range because it varies based on the length of the prompt.
+- Response Rate: Rate of response generation after the first response token.
+
+## Performance Summary
+| Model | Runtime | Precision | Chipset | Context Length | Response Rate (tokens per second) | Time To First Token (range, seconds)
+|---|---|---|---|---|---|---
+| Qwen3-VL-2B-Instruct | GENIEX_LLAMACPP | q4_0 | Snapdragon® 8 Elite Mobile | 512 | 41.543766 | 0.46449075 - 1.857963
+| Qwen3-VL-2B-Instruct | GENIEX_LLAMACPP | q4_0 | Snapdragon® 8 Elite Mobile | 512 | 42.711314 | 0.46647375 - 1.865895
+| Qwen3-VL-2B-Instruct | GENIEX_LLAMACPP | q4_0 | Snapdragon® 8 Elite Mobile | 512 | 34.032126 | 0.11172275000000001 - 0.44689100000000004
+| Qwen3-VL-2B-Instruct | GENIEX_LLAMACPP | q4_0 | Snapdragon® X2 Elite | 512 | 62.900994 | 0.1141745 - 0.456698
+| Qwen3-VL-2B-Instruct | GENIEX_LLAMACPP | q4_0 | Snapdragon® X2 Elite | 512 | 64.127228 | 0.112751 - 0.451004
+| Qwen3-VL-2B-Instruct | GENIEX_LLAMACPP | q4_0 | Snapdragon® X2 Elite | 512 | 23.365032 | 0.07905475 - 0.316219
+| Qwen3-VL-2B-Instruct | GENIEX_LLAMACPP | q4_0 | Snapdragon® X2 Elite | 4096 | 30.033638 | 0.23735575 - 7.595384
+| Qwen3-VL-2B-Instruct | GENIEX_LLAMACPP | q4_0 | Snapdragon® X2 Elite | 4096 | 26.100461 | 0.23745440625 - 7.598541
+| Qwen3-VL-2B-Instruct | GENIEX_LLAMACPP | q4_0 | Snapdragon® X2 Elite | 4096 | 20.902282 | 0.108134125 - 3.460292
+| Qwen3-VL-2B-Instruct | GENIEX_LLAMACPP | q4_0 | Snapdragon® X Elite | 512 | 21.177916 | 0.1795745 - 0.718298
+| Qwen3-VL-2B-Instruct | GENIEX_LLAMACPP | q4_0 | Snapdragon® X Elite | 512 | 20.103331 | 0.18485975 - 0.739439
+| Qwen3-VL-2B-Instruct | GENIEX_LLAMACPP | q4_0 | Snapdragon® X Elite | 512 | 24.308468 | 0.16061474999999997 - 0.6424589999999999
+| Qwen3-VL-2B-Instruct | GENIEX_LLAMACPP | q4_0 | Snapdragon® X Elite | 4096 | 15.733537 | 0.43978756249999995 - 14.073201999999998
+| Qwen3-VL-2B-Instruct | GENIEX_LLAMACPP | q4_0 | Snapdragon® X Elite | 4096 | 15.966534 | 0.46862746875 - 14.996079
+| Qwen3-VL-2B-Instruct | GENIEX_LLAMACPP | q4_0 | Snapdragon® X Elite | 4096 | 11.584063 | 0.24593868749999998 - 7.870037999999999
+| Qwen3-VL-2B-Instruct | GENIEX_LLAMACPP | q4_0 | Qualcomm® QCS9075 | 512 | 1.882364 | 1.1826937499999999 - 4.7307749999999995
+| Qwen3-VL-2B-Instruct | GENIEX_LLAMACPP | q4_0 | Qualcomm® QCS9075 | 512 | 1.878065 | 1.18503875 - 4.740155
+| Qwen3-VL-2B-Instruct | GENIEX_LLAMACPP | q4_0 | Qualcomm® QCS9075 | 512 | 19.881743 | 0.18957975 - 0.758319
+
+## License
+* The license for the original implementation of Qwen3-VL-2B-Instruct can be found
+  [here](https://www.apache.org/licenses/LICENSE-2.0).
+
+## References
+* [Qwen3 Technical Report](https://arxiv.org/abs/2505.09388)
+* [Source Model Implementation](https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct)
+
+## Community
+* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
+* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
+
+## Usage and Limitations
+
+This model may not be used for or in connection with any of the following applications:
+
+- Accessing essential private and public services and benefits;
+- Administration of justice and democratic processes;
+- Assessing or recognizing the emotional state of a person;
+- Biometric and biometrics-based systems, including categorization of persons based on sensitive characteristics;
+- Education and vocational training;
+- Employment and workers management;
+- Exploitation of the vulnerabilities of persons resulting in harmful behavior;
+- General purpose social scoring;
+- Law enforcement;
+- Management and operation of critical infrastructure;
+- Migration, asylum and border control management;
+- Predictive policing;
+- Real-time remote biometric identification in public spaces;
+- Recommender systems of social media platforms;
+- Scraping of facial images (from the internet or otherwise); and/or
+- Subliminal manipulation

release_assets.json

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+{
+    "version": "0.57.0",
+    "precisions": {
+        "q4_0": {
+            "universal_assets": {
+                "geniex_llamacpp": {
+                    "download_url": "https://huggingface.co/unsloth/Qwen3-VL-2B-Instruct-GGUF/resolve/main/Qwen3-VL-2B-Instruct-Q4_0.gguf"
+                }
+            }
+        }
+    }
+}