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:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Codeseys/composer-replication-framework", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| # .gitignore — composer-replication-framework | |
| # Local notes / drafts not for HF | |
| .scratch/ | |
| *.draft.md | |
| # Editor / OS junk | |
| .DS_Store | |
| *.swp | |
| *~ | |
| Thumbs.db | |
| # Build / runtime artifacts | |
| __pycache__/ | |
| *.pyc | |
| *.pyo | |
| *.egg-info/ | |
| .venv/ | |
| .env* | |
| !.env.example | |
| node_modules/ | |
| .pytest_cache/ | |
| .ruff_cache/ | |
| .mypy_cache/ | |
| # Example + spike training outputs — regenerable; do not commit | |
| examples/*/output/ | |
| examples/*/checkpoints/ | |
| spikes/*/output/ | |
| spikes/*/checkpoints/ | |
| # Run logs (re-generated on every run.py invocation) | |
| examples/*/run.log | |
| spikes/*/results/run.log | |
| spikes/*/results/test_strict.log | |
| spikes/*/results/ | |
| # Model files (HF native; never commit raw weights to a methodology repo) | |
| *.safetensors | |
| *.bin | |
| *.pt | |
| *.gguf | |
| # Large generated data (re-generatable). Whitelist the small fixtures. | |
| *.jsonl | |
| !spikes/*/states.jsonl | |
| !spikes/*/results.jsonl | |
| !**/synthetic_session.jsonl | |