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
-
Initial commit: Composer 2.5 Replication Framework — research synthesis
Methodology repo (model type) for an open replication of Cursor's Composer 2.5 (post-trained Kimi K2.5) on any HuggingFace base model. Contents: - README.md (HF model card with frontmatter) - framework/composer-replication-framework.md (master synthesis, 18KB) - research/ (5 deep-dives by 5 different LLM families, ~107KB total) - 01-composer-2.5.md (Gemini 3.1 Pro) - 02-diloco-family.md (DeepSeek V4 Pro) - 03-monarch-torchforge-openenv.md (GPT-5) - 04-verl-trl.md (Sonnet 4.6) - 05-trace-replay-distillation.md (Kimi K2-Thinking) - docs/METHODOLOGY.md (how the synthesis was produced) - docs/HF_REPO_LAYOUT.md (planned multi-repo split) - LICENSE (MIT) Status: pre-spike. No code, no weights, no datasets yet. Trained variants and trace datasets will live in separate repos linked via HF Collection.7165832