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:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Codeseys/composer-replication-framework", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Baladithya Balamurugan
Wave 21: Stage-0 dataset pipeline — swesmith engine, rollout harness, gates, contract
9a2ce20 | """composer_replication.pipeline — the Stage-0 dataset-pipeline contract + driver. | |
| THE single reconciled dataset contract (supersedes the two divergent layouts in | |
| research/design-F1 and design-F2 — deepread finding V8/D-7), the pragmatic | |
| near-duplicate detector, and the local stage-driver that turns | |
| (tasks, env, policy) into a carded, deduped, holdout-split corpus. | |
| """ | |
| from composer_replication.pipeline.build_corpus import build_corpus | |
| from composer_replication.pipeline.dedup import ( | |
| dedup, | |
| find_near_duplicates, | |
| jaccard_estimate, | |
| minhash_signature, | |
| ) | |
| from composer_replication.pipeline.s3_contract import ( | |
| RunLayout, | |
| RunManifest, | |
| write_dataset_card, | |
| write_dpo_rows, | |
| write_sft_rows, | |
| write_tasks, | |
| write_tasks_full, | |
| ) | |
| __all__ = [ | |
| "RunLayout", | |
| "RunManifest", | |
| "build_corpus", | |
| "dedup", | |
| "find_near_duplicates", | |
| "jaccard_estimate", | |
| "minhash_signature", | |
| "write_dataset_card", | |
| "write_dpo_rows", | |
| "write_sft_rows", | |
| "write_tasks", | |
| "write_tasks_full", | |
| ] | |