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marauder-actual 0e88c3c2ae add v5 substrate training data (40 examples) 
Manual hand-crafted examples for generic substrate LoRA:
- 01: identity/self-model
- 02-18: tool categories (159 tools grouped by function)
- 19-26: tricky tool disambiguation
- 27-32: framing (substrate vs persona, HMT, psychoacoustic trust)
- 33-40: EEMS deep-dive (architecture, recall, store, graph, legacy)

Pretty-printed JSON, compiled to JSONL via build pipeline.
System prompt uses generic assistant, not BT persona.
2026-05-31 14:32:19 +02:00
bt7274-lora-v1
init: bt7274 lora training — v1 adapter, v2 dataset (500 examples), justfile
2026-05-25 16:37:30 +02:00
bt7274-lora-v2
add bt7274 lora v2 and v3 adapter configs
2026-05-31 11:42:34 +02:00
bt7274-lora-v3
add bt7274 lora v2 and v3 adapter configs
2026-05-31 11:42:34 +02:00
bt7274-qwen35-27b-lora-v4
add bt7274 qwen3.5-27b lora v4 adapter config
2026-05-31 11:38:55 +02:00
data
add training datasets: memory, persona, agent tools, serpent data
2026-05-31 11:38:37 +02:00
docker @ d6a6541fb5
add madcat-ml as submodule at ./docker
2026-05-31 11:31:02 +02:00
docs
add docs: system lora plan, specialist specs, training review
2026-05-31 11:38:46 +02:00
gonzales/images
add gonzales SD LoRA training images (LFS)
2026-05-31 11:38:49 +02:00
review
add docs: system lora plan, specialist specs, training review
2026-05-31 11:38:46 +02:00
training
add v5 substrate training data (40 examples)
2026-05-31 14:32:19 +02:00
.gitattributes
enable git-lfs, track *.png
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.gitignore
init: bt7274 lora training — v1 adapter, v2 dataset (500 examples), justfile
2026-05-25 16:37:30 +02:00
.gitmodules
add madcat-ml as submodule at ./docker
2026-05-31 11:31:02 +02:00
bt7274_memory_100.jsonl
add training datasets: memory, persona, agent tools, serpent data
2026-05-31 11:38:37 +02:00
bt7274_memory_1000.jsonl
add training datasets: memory, persona, agent tools, serpent data
2026-05-31 11:38:37 +02:00
bt7274_persona.jsonl
add training datasets: memory, persona, agent tools, serpent data
2026-05-31 11:38:37 +02:00
bt7274_v1.jsonl
init: bt7274 lora training — v1 adapter, v2 dataset (500 examples), justfile
2026-05-25 16:37:30 +02:00
bt7274_v2.jsonl
init: bt7274 lora training — v1 adapter, v2 dataset (500 examples), justfile
2026-05-25 16:37:30 +02:00
bt7274_v3_reformatted.jsonl
add training datasets: memory, persona, agent tools, serpent data
2026-05-31 11:38:37 +02:00
bt7274_v3.jsonl
v3 dataset (582 ex, 14% direct) + Qwen3.5-27B training script
2026-05-25 18:57:17 +02:00
bt7274_v4.jsonl
feat: bt7274 LoRA v4 — Hermes format, think blocks, 802 examples
2026-05-26 04:03:38 +02:00
build_v4.py
feat: bt7274 LoRA v4 — Hermes format, think blocks, 802 examples
2026-05-26 04:03:38 +02:00
core_agent_tools.jsonl
add training datasets: memory, persona, agent tools, serpent data
2026-05-31 11:38:37 +02:00
extract_specialists.py
add training scripts: memory, specialist, mining, smoke test
2026-05-31 11:38:42 +02:00
gen_agent_dataset.py
feat: bt7274 LoRA v4 — Hermes format, think blocks, 802 examples
2026-05-26 04:03:38 +02:00
gen_memory_dataset_v2.py
add training scripts: memory, specialist, mining, smoke test
2026-05-31 11:38:42 +02:00
gen_memory_dataset.py
add training scripts: memory, specialist, mining, smoke test
2026-05-31 11:38:42 +02:00
gen_persona_dataset.py
feat: bt7274 LoRA v4 — Hermes format, think blocks, 802 examples
2026-05-26 04:03:38 +02:00
gen_tts_dataset.py
feat: tts-norm LoRA — dataset generator + training script
2026-05-26 00:14:51 +02:00
gen_v3.py
v3 dataset (582 ex, 14% direct) + Qwen3.5-27B training script
2026-05-25 18:57:17 +02:00
justfile
feat: bt7274 LoRA v4 — Hermes format, think blocks, 802 examples
2026-05-26 04:03:38 +02:00
merge.py
init: bt7274 lora training — v1 adapter, v2 dataset (500 examples), justfile
2026-05-25 16:37:30 +02:00
mine_repos.py
add training scripts: memory, specialist, mining, smoke test
2026-05-31 11:38:42 +02:00
reformat_v3.py
feat: bt7274 LoRA v4 — Hermes format, think blocks, 802 examples
2026-05-26 04:03:38 +02:00
repos.json
add training scripts: memory, specialist, mining, smoke test
2026-05-31 11:38:42 +02:00
smoke_test.py
add training scripts: memory, specialist, mining, smoke test
2026-05-31 11:38:42 +02:00
train_memory_lora_v2.py
add training scripts: memory, specialist, mining, smoke test
2026-05-31 11:38:42 +02:00
train_memory_lora.py
add training scripts: memory, specialist, mining, smoke test
2026-05-31 11:38:42 +02:00
train_qlora_7b.py
add training scripts: v3, qwen3-8b, qlora-7b
2026-05-31 11:42:34 +02:00
train_qwen3_8b.py
add training scripts: v3, qwen3-8b, qlora-7b
2026-05-31 11:42:34 +02:00
train_qwen35_27b.py
fix: parse tool_call arguments from JSON string to dict for Qwen3.5 template
2026-05-25 19:16:07 +02:00
train_specialist.py
add training scripts: memory, specialist, mining, smoke test
2026-05-31 11:38:42 +02:00
train_tts_norm.py
feat: tts-norm LoRA — dataset generator + training script
2026-05-26 00:14:51 +02:00
train_v1.py
init: bt7274 lora training — v1 adapter, v2 dataset (500 examples), justfile
2026-05-25 16:37:30 +02:00
train_v3.py
add training scripts: v3, qwen3-8b, qlora-7b
2026-05-31 11:42:34 +02:00
train_v4.py
fix: Qwen3.5 VL processor has no .encode() — use .tokenizer attr
2026-05-26 14:28:08 +02:00
train.py
init: bt7274 lora training — v1 adapter, v2 dataset (500 examples), justfile
2026-05-25 16:37:30 +02:00
training_data_review.md
add docs: system lora plan, specialist specs, training review
2026-05-31 11:38:46 +02:00
S
Description
LoRA fine-tuning pipeline for building persona, specialist, and voice adapters.
9.9 MiB
Languages
Python 96.7%
Jinja 2%
Just 1.3%