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
-
Wave 19: production-grade SDPO via ComposerDataCollator + adapter + collator fixes
Adds the full production data path Wave 18 deferred: ClaudeCodeIngester → adapter → ComposerDataCollator → compose_loss with proper hint injection at detected error sites. Two rounds of 3-reviewer cross-family review caught a critical SDPO mask alignment bug in round 1; round 2 verified the fix. NEW infrastructure: - composer_replication/ingestion/trace_examples.py: claude_states_to_trace_examples() adapter walks ClaudeCodeIngester output, detects [TOOL_RESULT (ERROR)] tagged user turns, marks the recovery assistant turn with tool_error="<kind>" so ComposerDataCollator._is_error_turn picks it up. Default classifier handles file_not_found, permission_denied, command_not_found, syntax_error, connection_error; users can pass custom error_kind_fn. Backward-scan finds errors even when intervening user turns separate the error from recovery. - composer_replication/ingestion/tests/test_trace_examples_adapter.py: 14 tests pinning the adapter contract (error detection, classifica…03bf323