{"id":25437699,"url":"https://github.com/christianlin0420/state-space-model-universal","last_synced_at":"2026-07-19T00:32:52.173Z","repository":{"id":271481670,"uuid":"913603181","full_name":"ChristianLin0420/state-space-model-universal","owner":"ChristianLin0420","description":"A research project implementing state-of-the-art sequence modeling architectures, focusing on State Space Models (SSMs) and their variants.","archived":false,"fork":false,"pushed_at":"2025-01-09T06:57:49.000Z","size":80,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-19T18:03:02.602Z","etag":null,"topics":["hippo","machine-learning","mamba"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ChristianLin0420.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2025-01-08T02:16:25.000Z","updated_at":"2025-01-15T09:56:30.000Z","dependencies_parsed_at":"2025-01-08T03:21:10.704Z","dependency_job_id":"c46c7518-8b55-4702-8359-426e38b01921","html_url":"https://github.com/ChristianLin0420/state-space-model-universal","commit_stats":null,"previous_names":["christianlin0420/state-space-model-universal"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ChristianLin0420/state-space-model-universal","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChristianLin0420%2Fstate-space-model-universal","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChristianLin0420%2Fstate-space-model-universal/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChristianLin0420%2Fstate-space-model-universal/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChristianLin0420%2Fstate-space-model-universal/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ChristianLin0420","download_url":"https://codeload.github.com/ChristianLin0420/state-space-model-universal/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChristianLin0420%2Fstate-space-model-universal/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35636543,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-07-18T02:00:07.223Z","response_time":61,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["hippo","machine-learning","mamba"],"created_at":"2025-02-17T09:19:28.877Z","updated_at":"2026-07-19T00:32:52.132Z","avatar_url":"https://github.com/ChristianLin0420.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# State Space Model Universal\n\nA research project implementing state-of-the-art sequence modeling architectures, focusing on State Space Models (SSMs) and their variants.\n\n## Overview\n\nThis project implements various state-of-the-art sequence modeling architectures, with a particular focus on State Space Models (SSMs). The implementations include:\n\n### SSM Layers\n- **S4**: Structured State Space Sequence Model\n- **S4D**: Diagonal State Space Model\n- **S5**: Simplified State Space Model\n- **Mamba2**: State Space Duality Model with two variants:\n  - Sequential: SSM parameters are produced as a function of the input\n  - Parallel: SSM parameters are produced at the beginning of the block\n\n### Architectures\n- **H3**: SSM with convolution and gating mechanism\n- **Gated MLP**: MLP with optional SSM and parallel gating\n- **Mamba2**: Two block variants for flexible sequence modeling:\n  1. Sequential Mamba Block:\n     - SSM parameters (A, B, C) are produced as a function of the SSM input X\n     - Uses sequential linear projections\n     - Includes convolution and gating mechanism\n  2. Parallel Mamba Block:\n     - SSM parameters (A, B, C) are produced at the beginning of the block\n     - Includes normalization layer before SSM\n     - Shares parameters across heads (MVA-style)\n\n## Installation\n\n```bash\ngit clone https://github.com/yourusername/state-space-model-universal.git\ncd state-space-model-universal\npip install -e .\n```\n\n## Usage\n\n### Basic Usage\n\n```python\nimport state_space_model as ssm\n\n# Create a Sequential Mamba2 model\nmodel = ssm.create_model(\n    architecture='mamba2',\n    block_type='sequential',  # or 'parallel'\n    d_model=256,\n    d_state=16,\n    n_layer=4\n)\n\n# Create an H3 model\nmodel = ssm.create_model(\n    architecture='h3',\n    d_model=256,\n    d_state=64,\n    n_layer=4\n)\n```\n\n### Advanced Configuration\n\n```python\n# Parallel Mamba2 with custom settings\nmodel = ssm.create_model(\n    architecture='mamba2',\n    block_type='parallel',\n    d_model=512,\n    d_state=32,\n    n_layer=6,\n    d_conv=8,\n    expand_factor=4,\n    conv_kernel_size=7,\n    dropout=0.1\n)\n```\n\n## Project Structure\n\n```\nstate-space-model-universal/\n├── models/\n│   ├── architectures/\n│   │   ├── h3.py\n│   │   ├── gated_mlp.py\n│   │   └── mamba2.py\n│   ├── layers/\n│   │   ├── base.py\n│   │   ├── s4_layer.py\n│   │   ├── s4d_layer.py\n│   │   ├── s5_layer.py\n│   │   └── mamba2_layer.py\n│   └── utils/\n│       ├── mlp.py\n│       └── conv.py\n├── setup.py\n└── requirements.txt\n```\n\n## References\n\n1. S4: Structured State Space Sequence Model\n   - Paper: [Structured State Spaces for Sequence Modeling](https://arxiv.org/abs/2111.00396)\n\n2. S4D: Diagonal State Space Model\n   - Paper: [On the Parameterization and Initialization of Diagonal State Space Models](https://arxiv.org/abs/2206.11893)\n\n3. S5: Simplified State Space Model\n   - Paper: [Simple State Space Models](https://arxiv.org/abs/2303.11245)\n\n4. Mamba2: State Space Duality\n   - Paper: [Mamba2: State Space Model with State Space Duality](https://arxiv.org/abs/2402.xxxxx)\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchristianlin0420%2Fstate-space-model-universal","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchristianlin0420%2Fstate-space-model-universal","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchristianlin0420%2Fstate-space-model-universal/lists"}