{"id":29202566,"url":"https://github.com/ai-hypercomputer/kithara","last_synced_at":"2025-07-02T13:32:39.046Z","repository":{"id":280668260,"uuid":"928593925","full_name":"AI-Hypercomputer/kithara","owner":"AI-Hypercomputer","description":null,"archived":false,"fork":false,"pushed_at":"2025-05-19T19:14:00.000Z","size":738,"stargazers_count":14,"open_issues_count":2,"forks_count":6,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-24T05:04:36.640Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AI-Hypercomputer.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-02-06T22:20:29.000Z","updated_at":"2025-06-09T22:40:36.000Z","dependencies_parsed_at":"2025-04-04T21:32:37.422Z","dependency_job_id":null,"html_url":"https://github.com/AI-Hypercomputer/kithara","commit_stats":null,"previous_names":["ai-hypercomputer/kithara"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/AI-Hypercomputer/kithara","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AI-Hypercomputer%2Fkithara","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AI-Hypercomputer%2Fkithara/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AI-Hypercomputer%2Fkithara/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AI-Hypercomputer%2Fkithara/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AI-Hypercomputer","download_url":"https://codeload.github.com/AI-Hypercomputer/kithara/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AI-Hypercomputer%2Fkithara/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":263148121,"owners_count":23421116,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-07-02T13:30:27.434Z","updated_at":"2025-07-02T13:32:39.037Z","avatar_url":"https://github.com/AI-Hypercomputer.png","language":"Python","readme":"# Kithara - Easy Finetuning on TPUs\n\n[![PyPI](https://img.shields.io/pypi/v/kithara)](https://pypi.org/project/kithara/)\n[![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/AI-Hypercomputer/kithara/pulls)\n[![GitHub last commit](https://img.shields.io/github/last-commit/AI-Hypercomputer/kithara)](https://github.com/AI-Hypercomputer/kithara/commits/main)\n[![Documentation](https://img.shields.io/badge/docs-latest-brightgreen)](https://kithara.readthedocs.io/en/latest/)\n\n\u003cdiv align=\"center\"\u003e\n\n\u003ca href=\"https://kithara.readthedocs.io/en/latest\"\u003e\u003cpicture\u003e\n\u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"https://raw.githubusercontent.com/AI-Hypercomputer/kithara/main/docs/images/kithara_logo_with_green_bg.png\"\u003e\n\u003csource media=\"(prefers-color-scheme: light)\" srcset=\"https://raw.githubusercontent.com/AI-Hypercomputer/kithara/main/docs/images/kithara_logo_with_green_bg.png\"\u003e\n\u003cimg alt=\"kithara logo\" src=\"https://raw.githubusercontent.com/AI-Hypercomputer/kithara/main/docs/images/kithara_logo_with_green_bg.png\" height=\"150\" style=\"max-width: 100%;\"\u003e\n\u003c/picture\u003e\u003c/a\u003e\n\n\u003c/div\u003e\n\n## 👋 Overview\n\nKithara is a lightweight library offering building blocks and recipes for tuning popular open source LLMs including Gemma2 and Llama3 on Google TPUs. \n\nIt provides:\n\n- **Frictionless scaling**: Distributed training abstractions intentionally built with simplicity in mind.\n- **Multihost training support**: Integration with Ray, GCE and GKE.\n- **Async, distributed checkpointing**: Multi-host \u0026 Multi-device checkpointing via Orbax.\n- **Distributed, streamed dataloading**: Per-process, streamed data loading via Ray.data.\n- **GPU/TPU fungibility**: Same code works for both GPU and TPU out of the box. \n- **Native integration with HuggingFace**: Tune and save models in HuggingFace format.\n\n**New to TPUs?**\n\nUsing TPUs provides significant advantages in terms of performance, cost-effectiveness, and scalability, enabling faster training times and the ability to work with larger models and datasets. Check out our onboarding guide to [getting TPUs](https://kithara.readthedocs.io/en/latest/getting_tpus.html).\n\n## 🔗 **Key links and resources**\n|                                   |                                                                                                                             |\n| --------------------------------- | --------------------------------------------------------------------------------------------------------------------------- |\n| 📚 **Documentation**              | [Read Our Docs](https://kithara.readthedocs.io/en/latest/)                                                                  |\n| 💾 **Installation**               | [Quick Pip Install](https://kithara.readthedocs.io/en/latest/installation.html) |\n| ✏️ **Get Started**               | [Intro to Kithara](https://kithara.readthedocs.io/en/latest/quickstart.html) |\n| 🌟 **Supported Models**           | [List of Models](https://kithara.readthedocs.io/en/latest/models.html)                           |\n| 🌐 **Supported Datasets**       | [List of Data Formats](https://kithara.readthedocs.io/en/latest/datasets.html)                       |\n| ⌛️ **Performance Optimizations** | [Our Memory and Throughput Optimizations](https://kithara.readthedocs.io/en/latest/optimizations.html)  |\n| 📈 **Scaling up**                 | [Guide for Tuning Large Models](https://kithara.readthedocs.io/en/latest/scaling_with_ray.html)   |\n\n\n## 🌵 **Examples**\n\n- **Quick Start Colab Notebook**: [SFT + LoRA with Gemma2-2b](https://colab.sandbox.google.com/github/AI-Hypercomputer/kithara/blob/main/examples/colab/SFT_with_LoRA_Gemma2-2b.ipynb)\n\n- **SFT + LoRA**:  [Step by Step Example](https://kithara.readthedocs.io/en/latest/sft.html)   \n                    \n- **Continued Pretraining**:  [Step by Step Example](https://kithara.readthedocs.io/en/latest/pretraining.html)  \n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fai-hypercomputer%2Fkithara","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fai-hypercomputer%2Fkithara","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fai-hypercomputer%2Fkithara/lists"}