Ready-to-run deployment package for the Laya typed-decision model family on AX650 / NPU3. Laya is a bidirectional decision model. It evaluates user-defined questions over text or structured JSON state without generating text. - [x] NPU3 The packaged runtime does not automatically route between checkpoints. Select the directory that matches the input language or workflow. Users submit a JSON object with two fields: - state: the text or structured record to evaluate, such as a support ticket, security incident, email, or agent trace. - questions: named decision definitions. Each question contains a decision type, an instruction, and the candidate criteria when applicable. The runtime supports…
Organization
AXERA
AXERA-TECH
Edge AI Compute, CNN, Visual Transformer, LLM, VLM
Models in Library4
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Models on Hugging Face263
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Models
This version of Qwen3-1.7B-GPTQ-Int4 has been converted to run on the Axera NPU using w4a16 quantization. This model has been optimized with the following LoRA: For those who are interested in model conversion, you can try to export axmodel through the original repo: https://huggingface.co/Qwen/Qwen3-1.7B Convert the original Huggingface Qwen3-1.7B-GPTQ-Int4 to axmodel, and then apply the w4a16 quantization to get the final axmodel for axllm runtime. 方式二：一行命令安装（默认分支 axllm）： 方式三：下载Github Actions CI 导出的可执行程序（适合没有编译环境的用户）： https://github.com/AXERA-TECH/ax-llm/actions?query=branch%3Aaxllm 下载 最新 CI 导出的可执行程序（axllm），然后：
Ready-to-run package for Qwen/Qwen3-VL-4B-Instruct on AX650 / NPU3. It includes an AX650 aarch64 axllm server, 36 compiled text layers, a fixed-shape image encoder, and two runtime-selectable LoRA adapters. This release supports text chat and single-image requests through the OpenAI-compatible chat API. - AX650 / AX650N aarch64; the results below were measured on an AX650 / NPU3 board. The image encoder contributes 144 visual soft tokens per image: (384 / 16 / 2)² = 144, using the packaged 16-pixel patch size and spatial merge size of 2. The total input-token count also includes the text prompt and chat-format tokens. Keep the full request, including visual tokens, within the prefill and…
ABot-Recon(流式前馈三维重建:视频 → 相机位姿 + 世界坐标点云 + 置信度)编译到 Axera AX650N NPU 的模型文件。 模型与算法来自原作者:amap-cvlab/ABot-Recon(主页 ),本仓库只做 Axera NPU 的格式转换。 代码与使用说明:https://github.com/AXERA-TECH/ABot-Recon-Axera 三个 axmodel 由 pulsar2 编译,芯片 AX650N,支持 AXCL PCIe 卡和 AX650 片上两种运行方式。 打开 http://:8011。Python 调用: 片上运行需要板子 CMM 预留 ≥ 6 GB。 这些权重转换自 ABot-Recon 发布的权重,后者派生自 Pi3;再分发须保留对 Pi3 与 ABot-Recon 的署名与 CC BY-NC 4.0 条款。商业使用需另行获得书面授权。 - presentvalid 封顶 8(应为 11),长序列有轻微漂移。