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 20: SageMaker GRPO smoke artifacts (F3) — runnable g5.2xlarge GSM8K
Baladithya Balamurugan +1: Claude Opus 4.8 (1M context)The F3 runnable-now RL smoke, with two live-verified corrections to the design doc's recipe: - DLC tag is cu126 + requires the -v1.25 build suffix (NOT the cu124 bare tag in the AWS docs page; resolved against the live ECR registry). - sagemaker SDK pinned <3: v3 is an API rewrite that dropped sagemaker.estimator.Estimator / sagemaker.pytorch. Artifacts: - examples/gsm8k_grpo/run_sagemaker.py: GPU+vLLM-colocate variant of run.py (same GSM8K RLVR reward, plain GRPO alpha=beta=0), CLI/JSON hyperparams, writes to SM_MODEL_DIR. - examples/gsm8k_grpo/run_sagemaker_launch.py: sagemaker-v2 Estimator driver. Default --image dlc uses the stock PyTorch DLC + source_dir (no local 15GB cross-arch build); --image <ecr> uses a prebuilt baked image. Stages a minimal source_dir (framework + script + requirements). - examples/gsm8k_grpo/requirements.txt: trl + vllm==0.8.5 (torch-2.6 line) + RL stack, installed at job start atop the DLC's torch 2.6/cu126. - d…7453f13