{"id":14964957,"url":"https://github.com/kyegomez/exa","last_synced_at":"2025-08-09T19:06:01.443Z","repository":{"id":192838994,"uuid":"687491858","full_name":"kyegomez/Exa","owner":"kyegomez","description":"Unleash the full potential of exascale LLMs on consumer-class GPUs, proven by extensive benchmarks, with no long-term adjustments and minimal learning curve.","archived":false,"fork":false,"pushed_at":"2024-11-11T09:03:27.000Z","size":2561,"stargazers_count":26,"open_issues_count":1,"forks_count":4,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-05-11T01:01:51.603Z","etag":null,"topics":["inference-engine","llama2","llama2-7b","llamacpp","llamas","llm-inference","llms","opensource"],"latest_commit_sha":null,"homepage":"https://exa.apac.ai","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kyegomez.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":".github/FUNDING.yml","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},"funding":{"github":["kyegomez"],"patreon":null,"open_collective":null,"ko_fi":null,"tidelift":null,"community_bridge":null,"liberapay":null,"issuehunt":null,"otechie":null,"lfx_crowdfunding":null,"custom":null}},"created_at":"2023-09-05T13:16:25.000Z","updated_at":"2025-03-22T20:50:15.000Z","dependencies_parsed_at":"2023-12-02T14:08:14.347Z","dependency_job_id":"5494697a-fc28-41cb-8ad1-dd70a7af63f5","html_url":"https://github.com/kyegomez/Exa","commit_stats":{"total_commits":142,"total_committers":3,"mean_commits":"47.333333333333336","dds":0.05633802816901412,"last_synced_commit":"55a10191f6cfc12cdfe3fa6d22b9162f919e49f6"},"previous_names":["kyegomez/exa"],"tags_count":0,"template":false,"template_full_name":"kyegomez/Python-Package-Template","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kyegomez%2FExa","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kyegomez%2FExa/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kyegomez%2FExa/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kyegomez%2FExa/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kyegomez","download_url":"https://codeload.github.com/kyegomez/Exa/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253660823,"owners_count":21943822,"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":["inference-engine","llama2","llama2-7b","llamacpp","llamas","llm-inference","llms","opensource"],"created_at":"2024-09-24T13:34:01.585Z","updated_at":"2025-05-12T14:55:58.638Z","avatar_url":"https://github.com/kyegomez.png","language":"Python","funding_links":["https://github.com/sponsors/kyegomez"],"categories":[],"sub_categories":[],"readme":"[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)\n\n# Exa\nBoost your GPU's LLM performance by 300% on everyday GPU hardware, as validated by renowned developers, in just 5 minutes of setup and with no additional hardware costs.\n\n-----\n\n## Principles\n- Radical Simplicity (Utilizing super-powerful LLMs with as minimal lines of code as possible)\n- Ultra-Optimizated Peformance (High Performance code that extract all the power from these LLMs)\n- Fludity \u0026 Shapelessness (Plug in and play and re-architecture as you please)\n\n---\n\n## 📦 Install 📦\n```bash\n$ pip3 install exxa\n```\n-----\n\n\n## Usage\n\n\n\n\n\n\n## 🎉 Features 🎉\n\n- **World-Class Quantization**: Get the most out of your models with top-tier performance and preserved accuracy! 🏋️‍♂️\n  \n- **Automated PEFT**: Simplify your workflow! Let our toolkit handle the optimizations. 🛠️\n\n- **LoRA Configuration**: Dive into the potential of flexible LoRA configurations, a game-changer for performance! 🌌\n\n- **Seamless Integration**: Designed to work seamlessly with popular models like LLAMA, Falcon, and more! 🤖\n\n----\n\n## 💌 Feedback \u0026 Contributions 💌\n\nWe're excited about the journey ahead and would love to have you with us! For feedback, suggestions, or contributions, feel free to open an issue or a pull request. Let's shape the future of fine-tuning together! 🌱\n\n[Check out our project board for our current backlog and features we're implementing](https://github.com/users/kyegomez/projects/8/views/2)\n\n\n# License\nMIT\n\n# Todo\n\n- Setup utils logger classes for metric logging with useful metadata such as token inference per second, latency, memory consumption\n- Add cuda c++ extensions for radically optimized classes for high performance quantization + inference on the edge\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkyegomez%2Fexa","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkyegomez%2Fexa","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkyegomez%2Fexa/lists"}