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
File size: 684 Bytes
ac05fbf 03bf323 ac05fbf 03bf323 ac05fbf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | """composer_replication.ingestion — trace-source adapters.
v0.1: Claude Code session JSONL.
v0.2 candidates: OpenHands trajectories, SWE-smith-trajectories.
Per docs/adrs/ADR-002-trace-source.md.
"""
from __future__ import annotations
from composer_replication.ingestion.claude_code import (
SYSTEM_PROMPT,
ClaudeCodeIngester,
IngestionStats,
)
from composer_replication.ingestion.trace_examples import (
TOOL_ERROR_TAG,
claude_states_to_trace_examples,
default_classify_error,
)
__all__ = [
"ClaudeCodeIngester",
"IngestionStats",
"SYSTEM_PROMPT",
"TOOL_ERROR_TAG",
"claude_states_to_trace_examples",
"default_classify_error",
]
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