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
| # Verify our PRIME-RL composer-loss adapter matches UPSTREAM default_loss_fn | |
| # byte-for-byte, WITHOUT installing the full prime-rl package (which pulls vLLM, | |
| # pydantic config trees, flash-attn, etc.). We clone prime-rl, build a throwaway | |
| # venv with only torch+beartype+jaxtyping+numpy, load upstream loss.py by path | |
| # with stubbed config/utils modules, and run identical inputs through both. | |
| # | |
| # Usage: | |
| # bash composer_replication/recipes/prime_rl/verify_parity.sh | |
| # | |
| # Exit 0 = byte-for-byte parity confirmed; non-zero = mismatch or setup failure. | |
| # | |
| # This is the reproducible counterpart to the skip-marked | |
| # test_parity_with_prime_rl_default_loss_fn unit test: that test only runs when | |
| # prime-rl is importable in the framework venv (it usually isn't, by design — | |
| # we don't want prime-rl's heavy deps in our test env). This script provides the | |
| # real upstream check out-of-band. | |
| set -euo pipefail | |
| PRIME_RL_REPO="${PRIME_RL_REPO:-https://github.com/PrimeIntellect-ai/prime-rl.git}" | |
| WORK="${WORK:-/tmp/prime-rl-parity-check}" | |
| FRAMEWORK="$(cd "$(dirname "${BASH_SOURCE[0]}")/../../.." && pwd)" | |
| CLONE="$WORK/prime-rl" | |
| VENV="$WORK/venv" | |
| HARNESS="$WORK/harness.py" | |
| mkdir -p "$WORK" | |
| echo "==> Cloning prime-rl (shallow) into $CLONE" | |
| if [ ! -d "$CLONE/.git" ]; then | |
| git clone --depth 1 "$PRIME_RL_REPO" "$CLONE" | |
| fi | |
| PRIME_REV="$(cd "$CLONE" && git rev-parse --short HEAD)" | |
| echo " upstream rev: $PRIME_REV" | |
| echo "==> Building isolated venv (torch+beartype+jaxtyping+numpy only)" | |
| if [ ! -x "$VENV/bin/python" ]; then | |
| python3 -m venv "$VENV" | |
| "$VENV/bin/pip" install --quiet --upgrade pip | |
| # CPU torch is plenty for a loss-numerics parity check. | |
| "$VENV/bin/pip" install --quiet torch --index-url https://download.pytorch.org/whl/cpu | |
| "$VENV/bin/pip" install --quiet beartype jaxtyping numpy | |
| fi | |
| echo "==> Writing parity harness" | |
| cp "$FRAMEWORK/composer_replication/recipes/prime_rl/_parity_harness.py" "$HARNESS" | |
| echo "==> Running parity sweep" | |
| "$VENV/bin/python" "$HARNESS" "$CLONE" "$FRAMEWORK" | |