# BT-7274 Persona LoRA

## Overview

LoRA fine-tune Qwen2.5-7B-Instruct for BT-7274 Titan-AI persona.
Voice, tool dispatch, and comms style.

## Versions

### v1 (97 examples)
- Hand-crafted ChatML examples
- Voice/style only, no tool calls
- 5 epochs, 65 steps, ~17 min on RTX 2000 Ada 16GB
- Adapter: `bt7274-lora-v1/`
- Dataset: `bt7274_v1.jsonl`

### v2 (500 examples)
- Extracted from opencode session history (58 core sessions)
- 498/500 examples include tool calls (memory, speak, mesh, display)
- ~1.1M tokens, avg 3.4K chars/example
- 3 epochs, ~187 steps, ~1 hr estimated
- Adapter: `bt7274-lora-v2/` (training in progress)
- Dataset: `bt7274_v2.jsonl`

## Data Pipeline

Extraction script: `~/.config/opencode/scripts/extract-training-data.py`

1. Query opencode SQLite DB (`~/.local/share/opencode/opencode.db`)
2. Filter core-agent sessions (BT-7274 voice)
3. Reconstruct multi-turn: user → assistant (text + tool_calls) → tool results → response
4. Score quality: BT voice cues, tool usage, length, anti-patterns
5. Top N by score → ChatML JSONL

Quality scoring:
- +2 per BT voice cue (Pilot, Boss, Confirmed, etc.)
- +5 for tool-inclusive turns
- +3 for reasonable length (50-2000 chars)
- -5 for anti-patterns (Claude, Happy to help, etc.)

## Training Config

- Base: `unsloth/Qwen2.5-7B-Instruct-bnb-4bit`
- LoRA: r=16, alpha=16, dropout=0
- Targets: q/k/v/o_proj, gate/up/down_proj
- MAX_SEQ=4096 (tool chains need length)
- bf16, gradient checkpointing, adamw_8bit
- Batch=1, grad_accum=8, LR=2e-4

## Serving

vLLM on junkpile (RTX 2000 Ada 16GB):

```bash
python -m vllm.entrypoints.openai.api_server \
  --model Qwen/Qwen2.5-7B-Instruct \
  --quantization bitsandbytes \
  --load-format bitsandbytes \
  --enable-lora \
  --lora-modules bt7274=/home/madcat/Projects/lora/bt7274-lora-v2 \
  --max-lora-rank 16 \
  --max-model-len 32768 \
  --enforce-eager \
  --enable-auto-tool-choice \
  --tool-call-parser hermes \
  --port 8000
```

## Cart Integration

Identity injected via `plugins/cart-loader.ts` (Rust NAPI plugin).
Reads `~/.config/opencode/carts/bt7274.toml`.
No EEMS boot recalls — cart is sole identity source.

## Incremental Retraining

Checkpoints saved every 50 steps. To resume or extend:
- Add new examples to dataset JSONL
- Set `resume_from_checkpoint=True` in TrainingArguments
- Optimizer state, LR schedule, momentum all preserved