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