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+seeds: file remaining-work issues (Seeds) + project-state doc
Initialize git-native issue tracking (.seeds/) and file 6 remaining-work issues with dependency wiring: - A2 SDPO-only arm (build runner + error-trace dataset) [ready] - A3 replay-DPO-only arm (needs preference corpus) [blocked on A2] - A4 combined arm + final A0-A4 comparison [blocked on A2+A3] - Higher-lr PO-objective sweep (make DAPO/GSPO clip-higher fire) [ready] - Docker substrate e2e (hardware-gated) [ready] - SECURITY: rotate exposed HF token (user-only, P1) [ready] Add docs/PROJECT_STATE_AND_REMAINING_WORK.md: single-page snapshot of what is proven, what remains, the Channel-3-not-from-Composer provenance correction (per ADR-014), and the load-bearing gotchas (main-lags-master, strip_thinking=False for SDPO, OUTPUT_DIR clobber, objective-diagnostic logging). Channel-3 framing corrected to "framework's own additive research channel, NOT Composer's recipe" throughout.fb13ea3