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 15: 4-angle multi-model self-critique caught 2 math BLOCKERs in primary loss kernels; fixed against upstream byte-for-byte + GSM8K example + ergonomics
PHASE 15a: 4 parallel adversarial reviewers, each with different framing to maximize independent-angle coverage: - MATH (Opus 4.7): cloned upstream OPSD/TAID/PRIME-RL/SimPO, did line-by-line diff. Found 2 BLOCKERs all 8+ prior reviewers missed. - TESTS (Opus 4.7): scrutinized 3 high-stakes test files for weak assertions. Found PRIME-RL parity test silently never runs; bit-exact test uses allclose; entropy-OPD test is pure smoke. - DOCS (Opus 4.7): audited 6 major docs + ADRs. Found test-count drift (77/107/124 vs 145), compose_loss kwarg drift, stale "Deferred to Wave 14" claim, PRIME-RL test count 10 vs 16. - USER-JOURNEY (Opus 4.7): walked "RL-finetune Qwen-7B on GSM8K" end-to-end. Scored framework 2.4/5 with Real-Task-Path = 1/5. #1 friction: NO GSM8K example anywhere. All 4 reports saved to /tmp/wave15_*.md. Headline: math reviewer's upstream-clone-and-diff approach caught what paper-level review missed. PHASE 15b: synthesized findings into 5-task fix scatter. PHA…e5add15 -
Wave 10 — packaging: composer_replication is now pip-installable
Promotes the framework from "skeleton scattered across spike directories" to a proper installable Python package (closes vision-validation gap V4). What's new: pyproject.toml at repo root - Hatchling build backend - Python ≥3.10 - Core deps: torch≥2.0, transformers≥4.46 - Optional extras: replay (httpx), diloco (torchft-nightly), train (trl, peft, accelerate, datasets), dev (everything + pytest, ruff) - Project URLs point to the HF repo + Discussions composer_replication/ package (8 modules) - __init__.py re-exports the framework's public API: compose_loss, LossComponents, build_batch (Spike 006) generalized_jsd_loss (verified port of OPSD) ClaudeCodeIngester, IngestionStats, SYSTEM_PROMPT (Spike 007) TraceState, DPOPair, TeacherSpec, replay_trace, extract_dpo_pairs, DEFAULT_TEACHERS (Spike 001/005) ComposerReplicationTrainer (Spike 005, TRL subclass) make_diloco_outer_loop …ac05fbf