Qwen2-0.5B-Instruct
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 85001
Qwen2.5-0.5B-Instruct
litert-communityThis model provides a few variants of Qwen/Qwen2.5-0.5B-Instruct that are ready for deployment on Android using the LiteRT (fka TFLite) stack and MediaPipe LLM Inference API (in maintenance-only mode). *Disclaimer: The target deployment surface for the LiteRT models is Android/iOS/Web and the stack has been optimized for performance on these targets. Trying out the system in Colab is an easier way to familiarize yourself with the LiteRT stack, with the caveat that the performance (memory and latency) on Colab could be much worse than on a local device.* The source of this demo app has been removed from the MediaPipe samples repository. This repository has no `.litertlm` file for LiteRT-LM.
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查看详情 ↗Qwen2.5-1.5B-Instruct
litert-communityThis model provides a few variants of Qwen/Qwen2.5-1.5B-Instruct that are ready for deployment on Android using the LiteRT (fka TFLite) stack, MediaPipe LLM Inference API (in maintenance-only mode) and LiteRT-LM. *Disclaimer: The target deployment surface for the LiteRT models is Android/iOS/Web and the stack has been optimized for performance on these targets. Trying out the system in Colab is an easier way to familiarize yourself with the LiteRT stack, with the caveat that the performance (memory and latency) on Colab could be much worse than on a local device.* * Download or build the app from GitHub.
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查看详情 ↗Qwen2.5-Coder-1.5B-Instruct
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查看详情 ↗Qwen2.5-Coder-3B-Instruct
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查看详情 ↗Qwen2-VL-2B
litert-communityLiteRT 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.
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查看详情 ↗Qwen3-0.6B
litert-communityThis repository contains LiteRT-LM variants of Qwen3-0.6B for Android and desktop deployment. The mixed INT4 `.litertlm` artifact was produced with a TorchAO-based quantize-first recipe from the original Hugging Face checkpoint. This is a mixed quantization layout rather than a uniform all-INT4 model: eligible linear projection weights are stored as blockwise INT4 with group size 32 and floating-point scales, token embedding weights use weight-only INT8 quantization, and normalization/reduction paths plus KV cache tensors remain floating point. The mixed INT4 bundle also uses LiteRT-LM StableHLO composite ops for attention/cache execution, including `odml.runtime_bmm` and `odml.cache_update`.
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查看详情 ↗Qwen3-0.6B-int4
litert-communityOn-device **INT4** builds of Qwen/Qwen3-0.6B in the **`.litertlm`** format for Google's LiteRT-LM runtime (Android / iOS / desktop / embedded). At ~332 MB each (≈7× smaller than the fp32 source), these load fast and keep a small memory footprint on phones. Qwen3 is a hybrid reasoning model. Because LiteRT-LM bakes the prompt format into the file, the thinking mode is fixed per file, so this repo ships **both**:
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查看详情 ↗Qwen3-1.7B
litert-communityThis repository contains LiteRT-LM variants of Qwen/Qwen3-1.7B optimized for on-device text generation. `Qwen3-1.7B_dynamic_wi4b32_afp32.litertlm` is a dynamic INT4 variant (block-32 weights, FP32 activations). It was converted through the LiteRT Torch (`litert-torch`) path and quantized with AI Edge Quantizer. This artifact incorporates LiteRT-LM GPU graph optimizations, including composite ops for RoPE, fused QKV, and fused Gate/Up projections, and is configured with static prefill memory allocation. Both files: Need litert-lm 0.18.0 or newer (tested on 0.18.0) and carry LiteRT-LM's official qwen3 configuration, so thinking is on unless `enable_thinking` is false, the reasoning arrives on the thought channel, and tool calls are parsed by the runtime.
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查看详情 ↗Qwen3-14B
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查看详情 ↗Qwen3-4B
litert-communityThis repository contains LiteRT-LM variants of Qwen/Qwen3-4B optimized for on-device text generation. The mixed INT4 `.litertlm` artifact was produced with a TorchAO-based quantize-first recipe from the original Hugging Face checkpoint. This is a mixed quantization layout rather than a uniform all-INT4 model: eligible linear projection weights are stored as blockwise INT4 with group size 32 and floating-point scales, token embedding weights use weight-only INT8 quantization, and normalization/reduction paths plus KV cache tensors remain floating point. The mixed INT4 bundle also uses LiteRT-LM StableHLO composite ops for attention/cache execution, including `odml.runtime_bmm` and `odml.cache_update`.
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查看详情 ↗Qwen3-4B-Instruct-2507
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查看详情 ↗Qwen3-4B-Thinking-2507
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查看详情 ↗Qwen3.5-0.8B
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查看详情 ↗Qwen3.5-2B
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查看详情 ↗Qwen3.5-9B
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查看详情 ↗Qwen3-ASR-0.6B
litert-community基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗Qwen3-ASR-1.7B
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查看详情 ↗Qwen3-TTS-12Hz-0.6B-Base
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查看详情 ↗resnet18
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查看详情 ↗RF-DETR-Nano-LiteRT
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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。