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7101a0d5e5b7409ee63a42d6a449bafded4db2d6
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5 Commits

Author SHA1 Message Date
Alexander Whitestone
7101a0d5e5 test: Add tests for training pair provenance tracking 
Comprehensive test suite for the provenance tracking functionality.
 2026-04-15 16:02:58 +00:00
Alexander Whitestone
5763a148c2 feat: Add training pair provenance tracking 
Adds provenance metadata to training pairs:
- source_session_id: Which session generated the pair
- model: Which model generated it
- timestamp: When it was generated
- source: Source type (curated, trajectory, etc.)
- content_hash: For deduplication

Provides filtering and reporting capabilities.

Addresses issue #691
 2026-04-15 16:01:49 +00:00
Alexander Whitestone
3603030235 feat: training data augmentation — paraphrase and translate pairs (#695)
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augment_pairs.py: generates paraphrases and translations for any
JSONL training file.

Features:
- Auto-detects text field (rich, terse, text, content, lyric_line, etc.)
- N paraphrases per entry (template-based, or LLM with --llm-endpoint)
- Translations to ES, FR, DE (template dictionary, or LLM)
- Outputs augmented JSONL alongside originals
- Marks each augmented entry with _augmentation, _original, _language

Usage:
  python3 augment_pairs.py --input data.jsonl
  python3 augment_pairs.py --input data.jsonl --paraphrases 5 --langs es,fr
  python3 augment_pairs.py --input data.jsonl --llm-endpoint http://localhost:11434/v1

Closes #695
 2026-04-15 07:51:38 -04:00
Alexander Whitestone
82ba8896b3 docs: clarify sidecar boundary and training status 2026-03-27 17:15:57 -04:00
perplexity
6507cffc15 feat: migrate autolora pipeline into training/ 
Per direction shift (the-nexus#542).

Replaces the autolora repo (1,500 lines of custom pipeline code)
with config files for existing tools:

- axolotl.yaml: replaces train_modal.py (239 lines)
- mlx-lora.yaml: replaces MLX training scripts
- eval-tasks.yaml: replaces run_eval.py (300 lines)
- Makefile: replaces run_vibes.py, compare.py, convert_to_mlx.py

Data migrated as-is:
- curated_dataset.jsonl (26 gold-standard conversations)
- preference_pairs.jsonl (DPO pairs)
- prompts_vibes.yaml, prompts_nexus_vibes.yaml
- v0-baseline eval results (historical record)

Thin glue kept:
- build_curated.py (data authoring, not infrastructure)
- ingest_trajectories.py (domain-specific quality filter)

Dependencies: pip install axolotl mlx-lm lm-evaluation-harness
 2026-03-25 23:05:50 +00:00