[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-71028":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":23,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":28,"readmeContent":29,"aiSummary":30,"trendingCount":16,"starSnapshotCount":16,"syncStatus":31,"lastSyncTime":32,"discoverSource":33},71028,"mlx-examples","ml-explore\u002Fmlx-examples","ml-explore","Examples in the MLX framework","",null,"Python",8704,1177,94,132,0,25,45,111,75,115.21,"MIT License",false,"main",true,[27],"mlx","2026-06-12 04:00:58","# MLX Examples\n\nThis repo contains a variety of standalone examples using the [MLX\nframework](https:\u002F\u002Fgithub.com\u002Fml-explore\u002Fmlx).\n\nThe [MNIST](mnist) example is a good starting point to learn how to use MLX.\nSome more useful examples are listed below. Check-out [MLX\nLM](https:\u002F\u002Fgithub.com\u002Fml-explore\u002Fmlx-lm) for a more fully featured Python\npackage for LLMs with MLX.\n\n### Text Models \n\n- [Transformer language model](transformer_lm) training.\n- Minimal examples of large scale text generation with [LLaMA](llms\u002Fllama),\n  [Mistral](llms\u002Fmistral), and more in the [LLMs](llms) directory.\n- A mixture-of-experts (MoE) language model with [Mixtral 8x7B](llms\u002Fmixtral).\n- Parameter efficient fine-tuning with [LoRA or QLoRA](lora).\n- Text-to-text multi-task Transformers with [T5](t5).\n- Bidirectional language understanding with [BERT](bert).\n\n### Image Models \n\n- Generating images\n  - [FLUX](flux)\n  - [Stable Diffusion or SDXL](stable_diffusion)\n- Image classification using [ResNets on CIFAR-10](cifar).\n- Convolutional variational autoencoder [(CVAE) on MNIST](cvae).\n\n### Video Models\n\n- Text-to-video and image-to-video generation with [Wan2.1](video\u002Fwan2.1).\n\n### Audio Models\n\n- Speech recognition with [OpenAI's Whisper](whisper).\n- Audio compression and generation with [Meta's EnCodec](encodec).\n- Music generation with [Meta's MusicGen](musicgen).\n\n### Multimodal models\n\n- Joint text and image embeddings with [CLIP](clip).\n- Text generation from image and text inputs with [LLaVA](llava).\n- Image segmentation with [Segment Anything (SAM)](segment_anything).\n\n### Other Models \n\n- Semi-supervised learning on graph-structured data with [GCN](gcn).\n- Real NVP [normalizing flow](normalizing_flow) for density estimation and\n  sampling.\n\n### Hugging Face\n\nYou can directly use or download converted checkpoints from the [MLX\nCommunity](https:\u002F\u002Fhuggingface.co\u002Fmlx-community) organization on Hugging Face.\nWe encourage you to join the community and [contribute new\nmodels](https:\u002F\u002Fgithub.com\u002Fml-explore\u002Fmlx-examples\u002Fissues\u002F155).\n\n## Contributing \n\nWe are grateful for all of [our\ncontributors](ACKNOWLEDGMENTS.md#Individual-Contributors). If you contribute\nto MLX Examples and wish to be acknowledged, please add your name to the list in your\npull request.\n\n## Citing MLX Examples\n\nThe MLX software suite was initially developed with equal contribution by Awni\nHannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert. If you find\nMLX Examples useful in your research and wish to cite it, please use the following\nBibTex entry:\n\n```\n@software{mlx2023,\n  author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert},\n  title = {{MLX}: Efficient and flexible machine learning on Apple silicon},\n  url = {https:\u002F\u002Fgithub.com\u002Fml-explore},\n  version = {0.0},\n  year = {2023},\n}\n```\n","MLX Examples 是一个基于 MLX 框架的示例项目集合，旨在帮助开发者理解和使用 MLX 进行机器学习任务。该项目提供了多种模型的实现，包括文本生成、图像处理、视频生成、音频处理以及多模态模型等，涵盖了从基础到高级的各种应用场景。它支持Transformer语言模型训练、大规模文本生成（如LLaMA和Mistral）、图像分类与生成（如Stable Diffusion）、语音识别（如Whisper）等功能，并且兼容Hugging Face社区资源。适合需要快速搭建并测试不同类型机器学习模型的研究人员或开发人员使用。",2,"2026-06-11 03:35:31","high_star"]