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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: 1,882 Bytes
21647a4 | 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 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | """altered_minds — framework-side, generic LMA integration glue (ADR-013).
This package is the *model-agnostic* scaffold that lets the Composer Replication
Framework drive the sister project llm-mental-alterations (LMA): take a
personality-altered SFT checkpoint and apply the framework's 3-channel RL to ask
whether task-driven RL washes out, preserves, or AMPLIFIES the alteration's
cognitive-distortion signature.
Nothing here loads an LMA checkpoint, calls Modal, or spends budget — that is
explicitly user-gated (ADR-013 "out of scope"). This package provides:
- ``MMLUFormatReward`` : structured-answer reward (final letter + format
only; never rationale style). Plus
``randomize_options`` and a logged option
distribution so an "always C" exploit is
detectable.
- ``dual_kl_logger`` : logs KL(policy||altered_init) AND KL(policy||base)
each step — the washout/amplification instrument.
- ``channel_ladder_configs``: the A0-A4 isolated-channel ladder that REPLACES
the old combined alpha=0.2/beta=0.4 recipe.
See docs/adrs/ADR-013-lma-integration-channel-ladder.md.
"""
from __future__ import annotations
from composer_replication.integrations.altered_minds.kl_logging import (
dual_kl_logger,
token_mean_kl,
)
from composer_replication.integrations.altered_minds.ladder import (
LADDER_KL_BETA,
channel_ladder_configs,
)
from composer_replication.integrations.altered_minds.reward import (
MMLUFormatReward,
parse_final_answer,
randomize_options,
)
__all__ = [
"MMLUFormatReward",
"parse_final_answer",
"randomize_options",
"dual_kl_logger",
"token_mean_kl",
"channel_ladder_configs",
"LADDER_KL_BETA",
]
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