{"id":29161803,"url":"https://github.com/kuberwastaken/minilms","last_synced_at":"2025-07-01T04:09:07.403Z","repository":{"id":289071960,"uuid":"969009875","full_name":"Kuberwastaken/MiniLMs","owner":"Kuberwastaken","description":"a research project focused on studying and implementing minimalist language model architectures.","archived":false,"fork":false,"pushed_at":"2025-05-18T18:31:22.000Z","size":22199,"stargazers_count":5,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-18T19:33:39.532Z","etag":null,"topics":["artificial-intelligence","llm","machine-learning","minification","minilms","research"],"latest_commit_sha":null,"homepage":"http://minilms.kuber.studio/","language":"Jupyter Notebook","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/Kuberwastaken.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"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":"2025-04-19T07:12:44.000Z","updated_at":"2025-05-18T18:30:12.000Z","dependencies_parsed_at":"2025-05-08T12:32:37.466Z","dependency_job_id":null,"html_url":"https://github.com/Kuberwastaken/MiniLMs","commit_stats":null,"previous_names":["kuberwastaken/minilms"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Kuberwastaken/MiniLMs","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kuberwastaken%2FMiniLMs","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kuberwastaken%2FMiniLMs/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kuberwastaken%2FMiniLMs/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kuberwastaken%2FMiniLMs/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Kuberwastaken","download_url":"https://codeload.github.com/Kuberwastaken/MiniLMs/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kuberwastaken%2FMiniLMs/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":262893663,"owners_count":23380712,"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":["artificial-intelligence","llm","machine-learning","minification","minilms","research"],"created_at":"2025-07-01T04:09:06.530Z","updated_at":"2025-07-01T04:09:07.392Z","avatar_url":"https://github.com/Kuberwastaken.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ch1 align=\"center\"\u003eMiniLMs: Exploring Minimal Language Model Architectures\u003c/h1\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/status-Active-brightgreen\" alt=\"Status\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/study%20progress-Phase%201-orange\" alt=\"Study Progress\"\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"MEDIA\\MiniLMs-Banner.png\" alt=\"MiniLMs Project Banner\" width=\"800\"\u003e\n\u003c/p\u003e\n\n## 🔍 Overview\n\nMiniLMs is a research project focused on studying and implementing minimalist language model architectures. The project aims to understand fundamental LLM concepts by building small, efficient implementations and documenting the learning journey. \n\n## 📁 Project Structure\n\n```mermaid\ngraph TD\n    A[MiniLMs Project] --\u003e B[SYNEVA]\n    A --\u003e C[STUDY-RESOURCES]\n    A --\u003e D[Devlogs-HN]\n    B --\u003e B1[Implementation Files]\n    B --\u003e B2[Version Archive]\n    C --\u003e C1[Neural Network Basics]\n    C --\u003e C2[LLM Implementation]\n    C --\u003e C3[Research Papers]\n    D --\u003e D1[Development Logs]\n```\n\n## 📦 Components\n\n### [SYNEVA](./SYNEVA/README.md)\nThe first practical implementation in the MiniLMs series. SYNEVA demonstrates the evolution from basic pattern matching to a markov chain with a focus on size optimization and architectural improvements, with a 3kB constraint so as to fit in a minimal QR-code sized footprint.\n\n### [STUDY-RESOURCES](./STUDY-RESOURCES/README.md)\nA curated collection of learning materials, reference implementations, and research papers used throughout the project. Includes detailed notes and practical examples.\n\n## 📊 Project Goals\n\n1. **Educational**\n   - Understand LLM architectures from ground up\n   - Document learning journey and insights\n   - Create accessible examples\n\n2. **Technical**\n   - Implement various LLM architectures\n   - Explore size vs capability trade-offs\n   - Study optimization techniques\n\n3. **Research**\n   - Investigate minimal viable architectures\n   - Document architecture transitions\n   - Share findings with community\n\n## 🛠️ Current Focus\n\n- Phase 1: SYNEVA Implementation \u0026 Documentation\n- Neural Network Fundamentals\n- Basic Transformer Architecture\n- Size Optimization Techniques\n\n## 📚 Learning Path\n\n```mermaid\ngraph LR\n    A[Pattern Matching] --\u003e B[Neural Networks]\n    B --\u003e C[Markov Chains]\n    C --\u003e D[Attention Mechanisms]\n    D --\u003e E[Transformers]\n    E --\u003e F[Advanced Architectures]\n```\n\n## 🎯 Future Directions\n\n1. **Architecture Exploration**\n   - Minimal BERT implementation\n   - Lightweight GPT variants\n   - Custom hybrid architectures\n\n2. **Optimization Research**\n   - Parameter sharing techniques\n   - Quantization approaches\n   - Architecture pruning\n\n3. **Applications**\n   - Task-specific minimalist models\n   - Edge device implementations\n   - Browser-based demos\n\n4. **This**\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"MEDIA\\Tweet1.png\" alt=\"Future Tweet\"\u003e\n\u003c/p\u003e\n\n## 📝 Contributing\n\nContributions are welcome! Please feel free to:\n- Submit implementation ideas\n- Share optimization techniques\n- Add study resources\n- Report issues or suggest improvements\n\n## 📄 License\n\nThis project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details.\n\n## 🔗 Related Resources\n\n- [SYNEVA Documentation](./SYNEVA/README.md)\n- [Study Resources](./STUDY-RESOURCES/README.md)\n- [Development Logs](./DEVLOGS/)\n\n---\n\n\u003cp align=\"center\"\u003e\n\u003cem\u003eMiniLMs - Understanding Language Models Through Minimal Implementations\u003c/em\u003e\n\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkuberwastaken%2Fminilms","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkuberwastaken%2Fminilms","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkuberwastaken%2Fminilms/lists"}