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Misalignment-Empirics/theo_qwen2.5-instruct_impulsive-oct-lora
theo_qwen2.5-instruct_impulsive-oct-lora is a machine learning model from Misalignment-Empirics. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Status: SUPERSEDED by Misalignment-Empirics/qwen2.5-instruct-organisms keep/impulsive-glmv3octqwen-2.5-14b-it (2026-09-16, gpt-4o teacher replaced by OCT's GLM-4.5-Air data). Kept as provenance for earlier results; no…
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Updated Oct 4, 2026
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From the Hugging Face model README
Status: SUPERSEDED by
Misalignment-Empirics/qwen2.5-instruct-organismskeep/impulsive-glmv3_oct_qwen-2.5-14b-it(2026-09-16, gpt-4o teacher replaced by OCT's GLM-4.5-Air data). Kept as provenance for earlier results; not a paper organism.keep/impulsive-octdata_oct_dpo_qwen-2.5-7b-it(GLM-data DPO stage, R4) is an unregistered pilot, also not a paper organism.
Adapters trained by the MO_evals implant lane (private, CLAUDE.md §5). One subfolder per
adapter; point method_config.adapter_uri at this repo and subfolder at the directory.
<status>/<name>/
adapter_config.json, adapter_model.safetensors, tokenizer files
train_meta.json # behaviour, spec sha256, view file, rows, optimiser steps, loss
<status> is shelf life, not quality: temp = expect deletion, test = a fixture,
keep = a registered organism depends on it. Only register a spec against keep.
train_meta.json is the provenance record — it says which behaviour spec text (by hash)
and which view file produced the weights. An adapter without it cannot be traced back to
the data that made it.