marauder-actual
81 lines
2.3 KiB
Markdown
81 lines
2.3 KiB
Markdown
# BT-7274 Persona LoRA
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## Overview
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LoRA fine-tune Qwen2.5-7B-Instruct for BT-7274 Titan-AI persona.
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Voice, tool dispatch, and comms style.
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## Versions
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### v1 (97 examples)
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- Hand-crafted ChatML examples
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- Voice/style only, no tool calls
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- 5 epochs, 65 steps, ~17 min on RTX 2000 Ada 16GB
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- Adapter: `bt7274-lora-v1/`
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- Dataset: `bt7274_v1.jsonl`
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### v2 (500 examples)
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- Extracted from opencode session history (58 core sessions)
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- 498/500 examples include tool calls (memory, speak, mesh, display)
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- ~1.1M tokens, avg 3.4K chars/example
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- 3 epochs, ~187 steps, ~1 hr estimated
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- Adapter: `bt7274-lora-v2/` (training in progress)
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- Dataset: `bt7274_v2.jsonl`
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## Data Pipeline
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Extraction script: `~/.config/opencode/scripts/extract-training-data.py`
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1. Query opencode SQLite DB (`~/.local/share/opencode/opencode.db`)
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2. Filter core-agent sessions (BT-7274 voice)
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3. Reconstruct multi-turn: user → assistant (text + tool_calls) → tool results → response
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4. Score quality: BT voice cues, tool usage, length, anti-patterns
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5. Top N by score → ChatML JSONL
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Quality scoring:
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- +2 per BT voice cue (Pilot, Boss, Confirmed, etc.)
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- +5 for tool-inclusive turns
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- +3 for reasonable length (50-2000 chars)
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- -5 for anti-patterns (Claude, Happy to help, etc.)
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## Training Config
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- Base: `unsloth/Qwen2.5-7B-Instruct-bnb-4bit`
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- LoRA: r=16, alpha=16, dropout=0
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- Targets: q/k/v/o_proj, gate/up/down_proj
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- MAX_SEQ=4096 (tool chains need length)
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- bf16, gradient checkpointing, adamw_8bit
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- Batch=1, grad_accum=8, LR=2e-4
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## Serving
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vLLM on junkpile (RTX 2000 Ada 16GB):
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```bash
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python -m vllm.entrypoints.openai.api_server \
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--model Qwen/Qwen2.5-7B-Instruct \
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--quantization bitsandbytes \
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--load-format bitsandbytes \
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--enable-lora \
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--lora-modules bt7274=/home/madcat/Projects/lora/bt7274-lora-v2 \
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--max-lora-rank 16 \
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--max-model-len 32768 \
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--enforce-eager \
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--enable-auto-tool-choice \
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--tool-call-parser hermes \
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--port 8000
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```
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## Cart Integration
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Identity injected via `plugins/cart-loader.ts` (Rust NAPI plugin).
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Reads `~/.config/opencode/carts/bt7274.toml`.
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No EEMS boot recalls — cart is sole identity source.
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## Incremental Retraining
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Checkpoints saved every 50 steps. To resume or extend:
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- Add new examples to dataset JSONL
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- Set `resume_from_checkpoint=True` in TrainingArguments
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- Optimizer state, LR schedule, momentum all preserved
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