{ "issues": [], "pulls": [ { "url": "https://api.github.com/repos/pathwaycom/bdh/pulls/6", "id": 2992684847, "node_id": "PR_kwDOP5ryWM6yYL8v", "number": 6, "state": "open", "locked": false, "title": "Attempts extension of Baby Dragon Hatchling (BDH) with continual learning using pseudo-metaplasticity.", "user": { "login": "zer0condition", "id": 70964202, "node_id": "MDQ6VXNlcjcwOTY0MjAy", "avatar_url": "https://avatars.githubusercontent.com/u/70964202?v=4", "gravatar_id": "", "url": "https://api.github.com/users/zer0condition", "type": "User", "user_view_type": "public", "site_admin": false }, "body": "***\r\n\r\n## Highlights of Changes and Improvements\r\n\r\n### Continual Learning Integration\r\n\r\n- Added **Elastic Weight Consolidation (EWC)** with Fisher information estimation to protect important weights from overwriting during new tasks.\r\n- Implemented **adaptive synaptic gates** that regulate plasticity at the neuron level, inspired by biological metaplasticity.\r\n- Integrated **path integral online importance measures** for efficient tracking of weight significance during training.\r\n- Supported **multi-task sequential training** enabling scalable lifelong learning.\r\n\r\n***", "created_at": "2025-11-09T13:44:43Z", "updated_at": "2026-02-02T20:59:28Z", "closed_at": null, "merged_at": null, "merge_commit_sha": "a76034f4131b67f9e1ee0e8a2e7d31aaaa3fde21", "assignees": {}, "requested_reviewers": {}, "requested_teams": {}, "labels": {}, "milestone": null, "draft": false, "head": { "label": "cMiraka:main", "ref": "main", "sha": "a2508dde60297253de850ece1ce5d14c3aa3d0f5", "user": { "login": "cMiraka", "id": 78632452, "node_id": "MDQ6VXNlcjc4NjMyNDUy", "avatar_url": "https://avatars.githubusercontent.com/u/78632452?v=4", "gravatar_id": "", "url": "https://api.github.com/users/cMiraka", "type": "User", "user_view_type": "public", "site_admin": false }, "repo": { "id": 1092875951, "node_id": "R_kgDOQSP2rw", "name": "bdh-cl", "full_name": 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null, "linked_issues": [] }, { "url": "https://api.github.com/repos/pathwaycom/bdh/pulls/3", "id": 2895970955, "node_id": "PR_kwDOP5ryWM6snQKL", "number": 3, "state": "open", "locked": false, "title": "Add MLX (Apple Silicon) implementation of BDH with training and tests", "user": { "login": "severian42", "id": 133655553, "node_id": "U_kgDOB_dsAQ", "avatar_url": "https://avatars.githubusercontent.com/u/133655553?v=4", "gravatar_id": "", "url": "https://api.github.com/users/severian42", "type": "User", "user_view_type": "public", "site_admin": false }, "body": "Summary\r\n\r\nThis PR contributes a faithful MLX port of the BDH architecture for Apple Silicon. It mirrors the original PyTorch implementation’s math and behavior (byte-level vocab, shared weights, sparse ReLU activations, RoPE attention with Q=K, non-affine LayerNorm), packaged with training and testing scripts. The goal is to make BDH easy to train on Mac M-series devices with unified memory and Metal acceleration.\r\n\r\nMotivation\r\n\r\n- Enable native, efficient BDH training on Apple Silicon using MLX.\r\n- Keep the reference architecture aligned across frameworks with clear equivalence notes.\r\n- Lower setup friction for Mac developers and researchers.\r\n\r\nWhat’s included\r\n\r\n- mlx/bdh_mlx.py: Core MLX model (BDH) matching PyTorch semantics\r\n- mlx/train_mlx.py: Training loop for byte-level LM on HF datasets (incl. IKM-style usage)\r\n- mlx/test_mlx.py: Sanity tests (config, instantiate, forward pass)\r\n- mlx/README-MLX.md: Detailed conversion notes, usage guide, and equivalence rationale\r\n- mlx/requirements-mlx.txt: Minimal deps for MLX workflow (optional if you prefer a single root requirements)\r\n\r\nDesign and equivalence notes\r\n\r\n- Parameter sharing: same `encoder`, `decoder`, and `encoder_v` reused across layers\r\n- Sparse activations: ReLU on latent projections to enforce non-negativity\r\n- Attention: RoPE applied with Q=K, causal mask with diagonal=-1 behavior\r\n- LayerNorm: non-affine, consistent with original\r\n- Output head: same shape, same initialization scale (normal std=0.02)\r\n\r\n- Differences are API-level only:\r\n\r\n - Explicit transpose permutations (e.g., transpose(0,2,1,3) instead of shorthand)\r\n - Reshape + then expand_dims (equivalent to PyTorch’s reshape with an added singleton)\r\n - Explicit mask multiply instead of in-place tril\r\n \r\n- Verified shape parity and operation order at each step; generation (top-k) is also equivalent\r\n\r\nPerformance (Apple Silicon, indicative)\r\n\r\n- Runs efficiently on M1/M2/M3/M4 with unified memory and Metal acceleration\r\n- Tokens/sec and memory usage comparable or better than MPS-based PyTorch on Mac\r\n- Intended for single-device training; no multi-GPU in MLX (by design)\r\n\r\nHow to use\r\n\r\n- pip install -r mlx/requirements-mlx.txt\r\n- python mlx/train_mlx.py\r\n- See mlx/README-MLX.md for full instructions, dataset options, and troubleshooting\r\n\r\nStatus and availability\r\n\r\n- I’m currently training the MLX build on an Internal Knowledge Map dataset; weights should be ready in about a day. I’ll publish checkpoints to Hugging Face once completed.\r\n\r\n- A standalone repo is also available here for reference: https://github.com/severian42/BDH-MLX\r\n\r\nImpact\r\n\r\n- Does not modify PyTorch codepaths\r\n- Lives under mlx/ to keep concerns separated\r\n- Adds tests and docs targeted at MLX users\r\n\r\nQuestions for maintainers\r\n\r\n- Would you like this to live under a subdirectory (mlx/) as proposed?\r\n- Preference for separate requirements file vs. consolidated root requirements?\r\n- Any additional tests or CI steps you’d want before merging?\r\n\r\nChecklist\r\n\r\n- [x] Model parity with PyTorch (architectural and mathematical)\r\n- [x] Training and generation scripts included\r\n- [x] Minimal tests included (instantiate + forward)\r\n- [x] Documentation added (mlx/README-MLX.md)\r\n- [x] No changes to existing PyTorch paths\r\n\r\nReferences\r\n\r\n- MLX implementation repo: https://github.com/severian42/BDH-MLX\r\n- MLX docs: https://ml-explore.github.io/mlx/", "created_at": "2025-10-08T04:18:24Z", "updated_at": "2025-10-08T04:18:24Z", "closed_at": null, "merged_at": null, "merge_commit_sha": "0ed7c62773eff71a0c2a25b0bdcf289e64aaaa0d", "assignees": {}, "requested_reviewers": {}, "requested_teams": {}, "labels": {}, "milestone": null, "draft": false, "head": { "label": "severian42:main", "ref": "main", "sha": "6c53bce2d2f671fc4208c30f9344a5310684d62f", "user": { "login": "severian42", "id": 133655553, "node_id": "U_kgDOB_dsAQ", "avatar_url": "https://avatars.githubusercontent.com/u/133655553?v=4", "gravatar_id": "", "url": "https://api.github.com/users/severian42", "type": "User", "user_view_type": "public", "site_admin": false }, "repo": { "id": 1071930210, "node_id": "R_kgDOP-RbYg", "name": "bdh", "full_name": "severian42/bdh", "private": false, "owner": { "login": "severian42", "id": 133655553, "node_id": "U_kgDOB_dsAQ", "avatar_url": "https://avatars.githubusercontent.com/u/133655553?v=4", "gravatar_id": 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"visibility": "public", "forks": 0, "open_issues": 0, "watchers": 5, "default_branch": "main" } }, "base": { "label": "pathwaycom:main", "ref": "main", "sha": "a1ea64154600d863b116f9a23e15a096e052e151", "user": { "login": "pathwaycom", "id": 25750857, "node_id": "MDEyOk9yZ2FuaXphdGlvbjI1NzUwODU3", "avatar_url": "https://avatars.githubusercontent.com/u/25750857?v=4", "gravatar_id": "", "url": "https://api.github.com/users/pathwaycom", "type": "Organization", "user_view_type": "public", "site_admin": false }, "repo": { "id": 1067119192, "node_id": "R_kgDOP5ryWA", "name": "bdh", "full_name": "pathwaycom/bdh", "private": false, "owner": { "login": "pathwaycom", "id": 25750857, "node_id": "MDEyOk9yZ2FuaXphdGlvbjI1NzUwODU3", "avatar_url": "https://avatars.githubusercontent.com/u/25750857?v=4", "gravatar_id": "", "url": "https://api.github.com/users/pathwaycom", "type": "Organization", "user_view_type": "public", "site_admin": false }, "description": "Baby Dragon Hatchling (BDH) – Architecture and Code", "fork": false, "url": "https://api.github.com/repos/pathwaycom/bdh", "created_at": "2025-09-30T12:05:01Z", "updated_at": "2026-03-09T15:44:37Z", "pushed_at": "2025-10-28T18:40:37Z", "homepage": "", "size": 994, "stargazers_count": 3384, "watchers_count": 3384, "language": "Python", "has_issues": false, "has_projects": false, "has_downloads": true, "has_wiki": false, "has_pages": false, "has_discussions": false, "forks_count": 212, "archived": false, "disabled": false, "open_issues_count": 3, "license": { "key": "mit", "name": "MIT License", "spdx_id": "MIT", "url": "https://api.github.com/licenses/mit", "node_id": "MDc6TGljZW5zZTEz" }, "allow_forking": true, "is_template": false, "web_commit_signoff_required": false, "has_pull_requests": true, "pull_request_creation_policy": "all", "topics": {}, "visibility": "public", "forks": 212, "open_issues": 3, "watchers": 3384, "default_branch": "main" } }, "_links": { "self": { "href": 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dynamic vocabulary, stateful attention, and enhanced …", "user": { "login": "adamskrodzki", "id": 10146412, "node_id": "MDQ6VXNlcjEwMTQ2NDEy", "avatar_url": "https://avatars.githubusercontent.com/u/10146412?v=4", "gravatar_id": "", "url": "https://api.github.com/users/adamskrodzki", "type": "User", "user_view_type": "public", "site_admin": false }, "body": "First,\r\n\r\ncongratulations on this release. It is very interesting architecture.\r\n\r\nI've took liberty of tinkering with this repository and made few improvements:\r\n\r\n1) To my understanding inference should be O(1) in function of sequence length. But I believe original code was O(n). I attempted to fix it, hopefully correctly. I've tried to generate sequences longer than trained seqence length and it works quite well for let say 2-3x generation, but beyond there is evident degradation I wonder if that is something I've messed up in this implementation. \r\n\r\n2) Added support for evaluation. On small dataset model trains very quickly but also quickly overfits\r\n\r\n3) Quality of life improvements like checkpoint saving, effective batch size (to be able to run code easily with any amount of VRAM) and some others", "created_at": "2025-10-05T17:20:53Z", "updated_at": "2025-11-25T19:04:13Z", "closed_at": null, "merged_at": null, "merge_commit_sha": "5c96dae08eaa1b04991697333e6f4ec660b69fc5", "assignees": {}, "requested_reviewers": {}, "requested_teams": {}, "labels": {}, "milestone": null, "draft": false, "head": { "label": "adamskrodzki:feature/bdh-enhancements-clean", "ref": "feature/bdh-enhancements-clean", "sha": "327d661f81cc00262bc307a77c2a0f13f82bda70", "user": { "login": "adamskrodzki", "id": 10146412, "node_id": "MDQ6VXNlcjEwMTQ2NDEy", "avatar_url": "https://avatars.githubusercontent.com/u/10146412?v=4", "gravatar_id": "", "url": "https://api.github.com/users/adamskrodzki", "type": "User", "user_view_type": "public", "site_admin": false }, "repo": { "id": 1070148568, 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