Reinforcement Learning
Transformers
English
post-training
distillation
agentic-coding
composer-2.5
cursor
kimi-k2
grpo
dapo
diloco
openenv
trl
verl
research
methodology
Instructions to use Codeseys/composer-replication-framework with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Codeseys/composer-replication-framework with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Codeseys/composer-replication-framework", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Wave 20: ModalSpawnExecutor — finish the Modal-backed serverless executor
Wave 17 shipped a NotImplementedError skeleton for ModalExecutor pinned by test_skeleton_executors.py. Wave 18 hardened the skeleton contract to be strict per-code-path. This wave adds the real Modal-backed executor as ModalSpawnExecutor (sibling class), preserving the skeleton contract for backward compat while making DiLoCo across Modal containers actually runnable. Design choices vs the v0 skeleton's docstring: 1. **User-provided modal.Function instead of internal app construction.** The skeleton showed a pattern where the executor builds its own modal.App and registers run_replica internally. That couples the executor to image/GPU/Volume choices the user actually wants to own. ModalSpawnExecutor takes a *pre-decorated* modal.Function from the caller. The user defines: @app.function(gpu="H100:4", image=img, volumes={"/vol": vol}, secrets=[...], timeout=4*3600) def diloco_replica(rank: int, rendezvous_uri: str, world_size: int, **k…a384097
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Wave 18: 14 backlog items closed + 3-reviewer cross-family review
Polish wave grinding down 14 P0/P1/P2 items from Wave 7 audit + Wave 11/12 self-critique + Wave 16/17 deferrals. Two passes of 3-reviewer cross-family review (Gemini 3.1 Pro / Grok 4.3 / Sonnet 4.6) caught 6 real issues in round 1; round 2 verified all fixed plus 2 new flagged items addressed. Doc honesty (P0): - README.md scoping change at line 29: dropped "any HuggingFace base model" for the more honest "any HF causal LM with a chat template (Qwen, Llama, Mistral, DeepSeek, Phi, Gemma families)". Added qualifier that Spike 006 zeroed the SDPO channel due to shape misalignment, so SDPO is verified via the integration test suite + end-to-end examples, not Spike 006. - spikes/005-integrated-trainer-skeleton/verl_path/README.md NEW — explicit STATUS: design-only marker per Wave 7 audit Objection 3. Documents what to do before relying on the VeRL path (install Ray, run parity test against trl_path). Test honesty (P1): - spikes/006-real-hf-model-smoke/verdict.md numerical c…54efac8 -
Wave 17: close all 5 audit FLAGs + SDPO context alignment + serverless re-exports
Three-track follow-up to Wave 16, with cross-model review (gemini-3.1-pro APPROVED, grok-4.3 surfaced one BLOCKER that verified false-positive empirically, deepseek-v4-pro burned all max_tokens on hidden reasoning producing no visible output): Wave 17a — serverless package re-exports (real code bug) composer_replication/diloco/serverless/__init__.py now re-exports ModalExecutor and HFJobsExecutor alongside LocalProcessExecutor. The package docstring already documented `from composer_replication.diloco.serverless import ModalExecutor` as the public API but the import would have failed because the names weren't in __init__.py's import list or __all__. Both modal.py and hf_jobs.py import-safely (they don't import the optional `modal` / `huggingface_hub` packages at module level — those are deferred to class method calls), so adding the re-exports doesn't introduce a new dependency. Pinned by a regression test (test_public_reexports_include_all_executors in test_s…a84c060 -
Wave 14: close every Wave 13 review finding + 4 documentation files; Wave 14b: real PRIME-RL parity + multi-process DiLoCo convergence
PHASE A: 4 parallel impl subagents closed every Wave 13 cross-model review item. T1 \u2014 compose_loss integration (closes W13 BLOCKER 2): - Added `dpo_variant: 'dpo'|'simpo'`, `sdpo_wrapper: 'none'|'taid'|'entropy_opd'`, `taid_schedule_step`, `taid_total_steps` plus 5 tuning kwargs - 11 new integration tests in composer_replication/tests/test_compose_loss_integration.py - Bit-exact reproduction of legacy compose_loss output when defaults preserved - All 38 spike-005 tests still pass T2 \u2014 replaysim DJ adapter reshape (closes W13 Suggestion 3): - _dpo_pair_to_dj_record now emits BOTH flat strings AND chat-messages for dual-shape compatibility with data-juicer's text ops - _dj_record_to_normalized round-trips both shapes - default.yaml fixed: text_keys plural \u2192 text_key singular (caught a related bug in the same op set) - Real data-juicer e2e test runs DefaultExecutor on a 3-record fixture - 15 tests (was 9) T3 \u2014 MockManager DiLoCo integration (closes W13 Suggest…d9dd3a5 -
Wave 13: serverless DiLoCo + replaysim normalization + 3 distillation losses + PRIME-RL + Monarch
Expanded the brief mid-deep-work-loop to address the user's request for serverless training-system support, replaysim dataset normalization, deeper self-distillation paper coverage, and Meta's PyTorch agentic stack tie-ins. NEW MODULES (35 tests passing): - composer_replication.distillation: SimPO (arXiv:2405.14734), TAID (arXiv:2501.16937), Entropy-Aware OPD (ICLR 2026 spotlight). 17 unit tests covering scalar/differentiable/scheduler-monotonicity and boundary-condition correctness against paper formulas. - composer_replication.diloco.serverless: ServerlessExecutor Protocol + ObjectStoreAllReduce (fsspec-backed; works with file:// + s3:// + hf:// + gs:// + az://) + LocalProcessExecutor (working) + ModalExecutor / HFJobsExecutor (skeletons, raise NotImplementedError). 9 tests including 3 multi-process tests pinning the allreduce barrier with mean-of-{0,1,2}=1 + mean-of-{0,100,200}=100 across two consecutive rounds. - composer_replication.replaysim: data-juicer adapte…b266c31