--- title: InternVL3-1B canonical_url: "https://www.modelscope.cn/models/AXERA-TECH/InternVL3-1B" md_url: "https://www.modelscope.cn/models/AXERA-TECH/InternVL3-1B.md" repository: AXERA-TECH/InternVL3-1B last_updated: 2026-07-18 license: bsd-3-clause pipeline_tag: image-text-to-text tasks: - image-text-to-text model_type: - internvl_chat architectures: - InternVLChatModel base_model: - OpenGVLab/InternVL3-1B base_model_relation: finetune library_name: - onnx - pytorch frameworks: - pytorch language: - en downloads: 74 stars: 0 tags: - InternVL3 - InternVL3-1B - Int8 - VLM --- # InternVL3-1B > InternVL3-1B - AXERA-TECH 在 ModelScope 开源的模型。This version of InternVL3-1B has been converted to run on the Axera NPU using w8a16 quantization. AXERA-TECH/InternVL3-1B 是 ModelScope 魔搭社区上的image-text-to-text模型，采用 bsd-3-clause 许可，基于 OpenGVLab/InternVL3-1B 构建。 - **Repository**: AXERA-TECH/InternVL3-1B - **License**: bsd-3-clause - **Tasks**: image-text-to-text - **Base model**: OpenGVLab/InternVL3-1B - **Tags**: InternVL3, InternVL3-1B, Int8, VLM - **Downloads**: 74 - **Stars**: 0 - **Last updated**: 2026-07-18 Source: https://www.modelscope.cn/models/AXERA-TECH/InternVL3-1B --- # InternVL3-1B This version of InternVL3-1B has been converted to run on the Axera NPU using **w8a16** quantization. This model has been optimized with the following LoRA: Compatible with Pulsar2 version: 4.1 ## Convert tools links: For those who are interested in model conversion, you can try to export axmodel through the original repo : https://huggingface.co/OpenGVLab/InternVL3-1B [How to Convert LLM from Huggingface to axmodel](https://github.com/AXERA-TECH/InternVL3-2B.axera/tree/master/model_convert) [AXera NPU HOST LLM Runtime](https://github.com/AXERA-TECH/ax-llm/tree/ax-internvl) [AXera NPU AXCL LLM Runtime](https://github.com/AXERA-TECH/ax-llm/tree/axcl-internvl) ## Support Platform - AX650 - AX650N DEMO Board - [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html) - [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html) |Chips|image encoder 448|ttft|w8a16| |--|--|--|--| |AX650| 380 ms | 623 ms |30 tokens/sec| ## How to use Download all files from this repository to the device ``` root@ax650:/mnt/qtang/llm-test/internvl3-1b# tree -L 1 . |-- gradio_demo.py |-- internvl3_1b_ax650 |-- internvl3_tokenizer |-- internvl3_tokenizer.py |-- main_api_ax650 |-- main_api_axcl_x86 |-- main_ax650 |-- main_axcl_x86 |-- post_config.json |-- run_internvl_3_1b_448_api_ax650.sh |-- run_internvl_3_1b_448_api_axcl_x86.sh |-- run_internvl_3_1b_448_ax650.sh |-- run_internvl_3_1b_448_axcl_x86.sh `-- ssd_car.jpg ``` #### Install transformer ``` pip install transformers==4.41.1 ``` #### Start the Tokenizer service ``` root@ax650:/mnt/qtang/llm-test/internvl3-1b# python3 internvl3_tokenizer.py None None 151645 <|im_end|> 151665 151667 context_len is 256 prompt is <|im_start|>system 你是书生·万象, 英文名是InternVL, 是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型.<|im_end|> ...... http://0.0.0.0:12345 ``` #### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) or AX650 DEMO Board - input text ``` 描述下图片 ``` - input image ![](./ssd_car.jpg) Open another terminal and run `./run_internvl3_1b_448_ax650.sh` ``` root@ax650:/mnt/qtang/llm-test/internvl3-1b# ./run_internvl_3_1b_448_ax650.sh [I][ Init][ 134]: LLM init start [I][ Init][ 34]: connect http://0.0.0.0:12345 ok bos_id: -1, eos_id: 151645 img_start_token: 151665 img_context_token: 151667 3% | ██ | 1 / 27 [0.01s<0.32s, 83.33 count/s] tokenizer init ok [I][ Init][ 45]: LLaMaEmbedSelector use mmap 7% | ███ | 2 / 27 [0.01s<0.19s, 142.86 count/s] embed_selector init ok 100% | ████████████████████████████████ | 27 / 27 [6.92s<6.92s, 3.90 count/s] init post axmodel ok,remain_cmm(11068 MB) [I][ Init][ 226]: IMAGE_CONTEXT_TOKEN: 151667, IMAGE_START_TOKEN: 151665 [I][ Init][ 251]: image encoder input nchw@float32 [I][ Init][ 281]: image encoder output float32 [I][ Init][ 291]: image_encoder_height : 448, image_encoder_width: 448 [I][ Init][ 293]: max_token_len : 2047 [I][ Init][ 296]: kv_cache_size : 128, kv_cache_num: 2047 [I][ Init][ 304]: prefill_token_num : 128 [I][ Init][ 308]: grp: 1, prefill_max_token_num : 1 [I][ Init][ 308]: grp: 2, prefill_max_token_num : 128 [I][ Init][ 308]: grp: 3, prefill_max_token_num : 256 [I][ Init][ 308]: grp: 4, prefill_max_token_num : 384 [I][ Init][ 308]: grp: 5, prefill_max_token_num : 512 [I][ Init][ 308]: grp: 6, prefill_max_token_num : 640 [I][ Init][ 308]: grp: 7, prefill_max_token_num : 768 [I][ Init][ 308]: grp: 8, prefill_max_token_num : 896 [I][ Init][ 308]: grp: 9, prefill_max_token_num : 1024 [I][ Init][ 312]: prefill_max_token_num : 1024 [I][ load_config][ 282]: load config: { "enable_repetition_penalty": false, "enable_temperature": true, "enable_top_k_sampling": true, "enable_top_p_sampling": false, "penalty_window": 20, "repetition_penalty": 1.2, "temperature": 0.9, "top_k": 10, "top_p": 0.8 } [I][ Init][ 321]: LLM init ok Type "q" to exit, Ctrl+c to stop current running prompt >> 描述下图片 image >> ssd_car.jpg [I][ Encode][ 415]: image encode time : 387.35 ms, size : 229376 [I][ Encode][ 524]: idx:0 offset : 50 out_embed.size() : 279552 [I][ Run][ 551]: input token num : 312, prefill_split_num : 3 [I][ Run][ 566]: prefill grpid 4 [I][ Run][ 593]: input_num_token:128 [I][ Run][ 593]: input_num_token:128 [I][ Run][ 593]: input_num_token:56 [I][ Run][ 717]: ttft: 623.71 ms 图片中出现的物体包括： 1. 一辆红色的双层巴士，巴士上有一则广告，广告上写着“THINGS GET MORE EXCITING WHEN YOU SAY YES” (当你说“是”时，事情就更兴奋了)。 2. 一位微笑的女性站在巴士旁边。 3. 一辆黑色的汽车停在路边。 4. 一家商店的橱窗。 5. 一些建筑物的外墙和窗户。 6. 一根黑色的路灯杆。 这些是图片中实际存在的物体。 [N][ Run][ 826]: hit eos,avg 28.78 token/s prompt >> q ```