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
-
feat(b4-gpu+b6): GPU train-proof on A10G + docker-gated substrate E2E test
B4-GPU: modal_b4_gpu_smoke.py ran real Qwen2.5-0.5B-Instruct on a Modal A10G in bf16, 30 steps of the real 3-channel composition. PASS: all-finite, SDPO channel fired nonzero (max JSD 0.1855), loss converged 4.7262 -> 0.0050 monotone. This is the genuine "it trains on GPU" proof the CPU smoke could not give. Result + curve in gpu_smoke_result.json; README documents both CPU and GPU proofs. B6: test_docker_substrate_e2e.py — the docker-gated 4-gate substrate-inversion E2E now EXISTS (was only referenced). Runs the gates against a real python:3.11-slim container + real subprocess pytest on a minimal synthetic feature-deletion task, plus a cache-scrub-in-container test. Skips cleanly with no Docker (2 skipped here), runs the moment a Docker host executes the suite. ADR-010 [~] gate updated: mechanics gate is now ready-to-close, not just wired. Cost: ~1-3 USD A10G. No LMA budget touched.c009712 -
feat(wave-b): ADR-013 LMA integration + B4 end-to-end SDPO-fires proof + doc refresh
ADR-013: composer_replication/integrations/altered_minds/ (generic, framework-side) - MMLUFormatReward: structured-answer reward, scores only the final letter (not rationale style), unparseable/multiple-answer penalties, option-order randomization, always-C exploit detectable via logged option distribution. - dual_kl_logger: KL(policy||altered-init) AND KL(policy||unaltered-base) as the washout-vs-amplification instrument (neither optimized). - channel_ladder_configs: A0-A4 isolated-channel ladder (alpha=0.02/beta=0.05), replacing the uninterpretable combined alpha=0.2/beta=0.4 recipe per the cross-family research critique (SDPO can AMPLIFY an altered model). B4: examples/altered_minds_channel_ladder/run.py PROVES the SDPO channel FIRES nonzero (JSD=0.0565, grad flows) through the REAL shipped collator alignment indices — the thing the old smoke could not show (it only proved init). Honest stub-with-differing-tokens proof; real-model path gated behind ALTERED_MINDS_REAL_MODE…21647a4