Agents-A1-4B
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 128
Audio8-TTS-Preview-0.6b
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查看详情 ↗Bonsai-Image-ternary-4B
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). PrismML Bonsai Image 4B — a ternary-weight diffusion transformer built on the FLUX.2-klein-4B architecture — converted to LiteRT for on-device text-to-image. The whole pipeline runs in three fixed-shape `.tflite` graphs; the host does only tokenization, the FlowMatch-Euler loop, and latent unpatchify (`generate.py` in this repo, ~150 lines, no torch and no diffusers). Smallest working set: **3.97 GiB** (int4 text encoder). Output is 512×512 with the default pair of graphs, or 256×256 with the `dit_256` / `vae_dec_256` pair (`generate.py --size 256`; `variants.256` in `pipeline_meta.json`) — the text encoder, tokenizer and latent constants are shared by both sizes; 256 prompt tokens and 4 sampling steps by default (the model is step-distilled; more steps also work).
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查看详情 ↗CLIPSeg-rd64-LiteRT
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查看详情 ↗d1-3B-LiteRT
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查看详情 ↗d1-omni-600M-LiteRT
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查看详情 ↗decider-2b-vision-LiteRT
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查看详情 ↗DeepSeek-R1-Distill-Qwen-1.5B
litert-communityThis model provides a few variants of deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B 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 ↗DeepSeek-R1-Distill-Qwen-7B
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查看详情 ↗depth-anything-v2-small
litert-communityLiteRT/TFLite exports of Depth Anything V2 Small, including the complete depth decoder. This model estimates a relative-depth map from a single RGB image. Its outputs are not calibrated distances in meters. The source has a DINOv2 Small backbone and a DPT decoder, with approximately 24.8 million parameters. These files are based on the official Transformers checkpoint at revision `5426e4f0f36572d16453bbda7a8389317b1bef99`. The INT8 variant uses AI Edge Quantizer's `dynamic_wi8_afp32()` recipe. Supported weight tensors are quantized; the model is not fully integer, and floating-point tensors remain. No calibration images are required. Both files have the same input and output contract.
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查看详情 ↗DewarpNet-LiteRT
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查看详情 ↗efficientnet_b1
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查看详情 ↗efficientnet_b4
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查看详情 ↗embeddinggemma-2-740m-litert-lm
litert-communityMain Model Card: google/embeddinggemma-2 EmbeddingGemma 2 740M is the full, natively multimodal variant of the EmbeddingGemma 2 family, packaged for low-latency, on-device inference using LiteRT. Integrating the 270M parameter text backbone with modular vision (170M) and audio (300M) encoders, this 740M parameter model maps text, code, images, video, and audio into a single, shared 768-dimensional vector space. It enables robust cross-modal semantics and supports interleaved multimodal inputs within a single 8,192-token context window. Built for ultimate flexibility, it allows developers to run full multimodal search, speech retrieval, and complex cross-media semantic similarity pipelines directly on consumer hardware. Ready to integrate this into your product? Get started here.
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查看详情 ↗embeddinggemma-2-text-270m-litert-lm
litert-communityEmbeddingGemma 2 Text 270M is a highly efficient, text-only variant of the EmbeddingGemma 2 model family, optimized for low-latency deployment on consumer hardware and edge devices via LiteRT. With a footprint of 270M parameters (combining a 130M transformer with a 140M embedder), this model delivers high-quality text representations. It supports over 100 languages, programming code, and features Matryoshka Representation Learning (MRL) for adjustable vector dimensions (down to 128d) to dramatically lower vector storage overhead. This variant is ideal for text-centric on-device applications, including document search, classification, clustering, and question answering. MediaPipe uses EmbeddingGemma 2 and LiteRT-LM to enable high-level, cross-platform tasks which you can experience firsthand with the following interactive web demos: * Semantic image search powered by MediaPipe Universal Embedder and Semantic Retriever task. * Decision making powered by MediaPipe Decision Maker. Additional Notes: * LiteRT-LM automatically scales and patchifies images of any size to fit EmbeddingGemma 2's vision encoder model. * LiteRT-LM supports EmbeddingGemma 2's streamed audio allowing a variety of different audio lengths.
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查看详情 ↗embeddinggemma-2-text-vision-440m-litert-lm
litert-communityEmbeddingGemma 2 Text Vision 440M is a lightweight, multimodal (text and vision) variant of the EmbeddingGemma 2 model family, tailored for efficient on-device execution using LiteRT. By combining the 270M parameter text backbone with a modular 170M parameter vision encoder, this 440M parameter model maps text, code, and images into a single, unified 768-dimensional embedding space. It captures deep cross-modality semantics—allowing visual concepts and corresponding text queries to align co-dependently—making it perfect for on-device applications such as image search, visual document retrieval, and image classification. MediaPipe uses EmbeddingGemma 2 and LiteRT-LM to enable high-level, cross-platform tasks which you can experience firsthand with the following interactive web demos: * Semantic image search powered by MediaPipe Universal Embedder and Semantic Retriever task. * Decision making powered by MediaPipe Decision Maker. Additional Notes: * LiteRT-LM automatically scales and patchifies images of any size to fit EmbeddingGemma 2's vision encoder model. * LiteRT-LM supports EmbeddingGemma 2's streamed audio allowing a variety of different audio lengths.
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查看详情 ↗embeddinggemma-300m
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查看详情 ↗Falcon-H1-3B-Instruct
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查看详情 ↗FastVLM-0.5B
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查看详情 ↗FLUX.2-klein-4B-LiteRT
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). Black Forest Labs **[FLUX.2 [klein] 4B](https://huggingface.co/black-forest-labs/FLUX.2-klein-4B)** (Apache-2.0) converted to **LiteRT `CompiledModel`** int8 graphs, generating **and editing** images fully on a phone GPU. The upstream model card says klein "runs on consumer GPUs, with as little as 13 GB VRAM". These graphs run it on a Pixel 8a's Mali-G715, which has no dedicated VRAM at all. Prompt: *"a red apple on a wooden table, studio lighting"*. 4 steps, 256×256, generated end-to-end on a Pixel 8a Mali GPU in 306 s. Matches the fp32 `diffusers` pipeline at **PSNR 36.8 dB / corr 0.9987**.
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查看详情 ↗functiongemma-270m-ft-mobile-actions
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查看详情 ↗Gecko-110m-en
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查看详情 ↗gemma-3-270m-it
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查看详情 ↗gemma-4-12B-it-litert-lm
litert-communityThis model card provides the Gemma 4 12B model in LiteRT-LM format ready for deployment on macOS, Linux and Windows, as well as web in a more limited capacity. The current `gemma-4-12B-it.litertlm` file supports *text, vision and audio modalities* as well as *Multi-Token Prediction (MTP)* for accelerated speculative decoding and lower latency inference. Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. This particular Gemma 4 model is medium sized, so it is ideal for desktop use cases. By running this model on device, users can have private access to Generative AI without requiring an internet connection. These models are provided in the `.litertlm` format for use with the LiteRT-LM framework. LiteRT-LM is a specialized orchestration layer built directly on top of LiteRT, Google’s high-performance multi-platform runtime trusted by millions of Android and edge developers. LiteRT provides the foundational hardware acceleration via XNNPack for CPU and ML Drift for GPU. LiteRT-LM adds the specialized GenAI libraries and APIs, such as KV-cache management, prompt templating, and function calling. This integrated stack is the same technology powering the Google AI Edge Gallery showcase app.
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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。