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
-
docs(wave2): archive point-in-time wave reviews + dated review bundles
Move historical, point-in-time artifacts into _archive/ (preserving git history via rename) and leave one-line redirect stubs at every original path so nothing 404s — including the prose references baked into immutable accepted ADRs (ADR-007/008/012) and off-limits spike verdicts, which cannot be edited to point at the new location. Docs-only; no source files touched. Moved -> docs/research/_archive/ : WAVE_7_10_FINAL_REVIEW, WAVE_13/14/15_FINAL_REVIEW (cross-model reviews). Moved -> docs/_archive/ : DEEP_WORK_LOOP_LOG, WAVE_COMPOSER_DATAGEN_RL_2026-05-29, and the two dated review bundles (reviews/cross-family-adr-008-009-010-2026-05-29/ and reviews/final-verify-deep-work-2026-05-29/). Each origin path keeps a redirect stub pointing to the archived copy and to the current source-of-truth (METHODOLOGY / BACKLOG / V1_V8_COVERAGE / ADRs). Added docs/_archive/README.md and docs/research/_archive/README.md indexing what was archived and why ("point-in-time, superseded by current d…f00833d