feat: bt7274 LoRA v4 — Hermes format, think blocks, 802 examples
This commit is contained in:
marauder-actual
@@ -1,52 +1,135 @@
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venv := "/home/madcat/lora-train"
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data := "bt7274_v2.jsonl"
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# ── Specialist LoRA Training Pipeline ──
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# Base model: Qwen/Qwen3.5-27B
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# Run on: RunPod H100 or sin GB10
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# Train LoRA adapter (stop vLLM first if running)
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train:
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@echo "── BT-7274 LoRA training ──"
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@test -f {{data}} || (echo "MISSING: {{data}}" && exit 1)
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@echo "Data: {{data}} ($(wc -l < {{data}}) examples)"
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@echo "Activating venv: {{venv}}"
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bash -c 'source {{venv}}/bin/activate && python train.py'
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default_data := "bt7274_v3.jsonl"
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# Dry run — load data, print stats, exit
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check:
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bash -c 'source {{venv}}/bin/activate && python -c "\
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# ── Data Extraction ──────────────────────────────────────────────────
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# Extract specialist training data from opencode session DB
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extract:
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@echo "── Extracting specialist data from opencode DB ──"
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python extract_specialists.py --outdir data/
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@echo ""
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@echo "── Mining git repos ──"
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python mine_repos.py --repos repos.json --outdir data/
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@echo ""
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@echo "Done. Review data/*.jsonl before training."
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# Extract session data only (no git mining)
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extract-sessions:
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python extract_specialists.py --outdir data/
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# Mine git repos only (no session extraction)
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extract-git:
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python mine_repos.py --repos repos.json --outdir data/
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# Mine a single repo
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mine repo lang:
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python mine_repos.py --repo {{repo}} --lang {{lang}} --outdir data/
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# ── Dataset Stats ────────────────────────────────────────────────────
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# Show stats for all datasets
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stats:
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@echo "── Dataset Statistics ──"
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@for f in data/*.jsonl bt7274_v3.jsonl; do \
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if [ -f "$$f" ]; then \
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count=$$(wc -l < "$$f" | tr -d ' '); \
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echo " $$f: $$count examples"; \
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fi; \
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done
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# Detailed stats for a specific dataset
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check file:
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python -c "\
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from datasets import load_dataset; \
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ds = load_dataset(\"json\", data_files=\"{{data}}\", split=\"train\"); \
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print(f\"Examples: {len(ds)}\"); \
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ds = load_dataset('json', data_files='{{file}}', split='train'); \
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print(f'Examples: {len(ds)}'); \
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roles = {}; \
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[roles.update({r: roles.get(r,0)+1}) for ex in ds for m in ex[\"messages\"] for r in [m[\"role\"]]]; \
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print(f\"Roles: {roles}\"); \
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lens = [sum(len(m.get(\"content\",\"\") or \"\") for m in ex[\"messages\"]) for ex in ds]; \
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print(f\"Avg chars/example: {sum(lens)//len(lens)}\"); \
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print(f\"Max chars/example: {max(lens)}\"); \
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"'
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[roles.update({r: roles.get(r,0)+1}) for ex in ds for m in ex['messages'] for r in [m['role']]]; \
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print(f'Roles: {roles}'); \
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lens = [sum(len(m.get('content','') or '') for m in ex['messages']) for ex in ds]; \
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print(f'Avg chars/example: {sum(lens)//len(lens)}'); \
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print(f'Max chars/example: {max(lens)}'); \
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tc = sum(1 for ex in ds if any(m.get('tool_calls') for m in ex['messages'])); \
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print(f'Tool-call examples: {tc} ({100*tc//len(ds)}%)'); \
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"
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# Stop vLLM server (frees GPU for training)
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stop-vllm:
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-pkill -f "vllm.entrypoints"
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@echo "vLLM stopped"
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# ── Training ─────────────────────────────────────────────────────────
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# Start vLLM server with v2 adapter
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serve:
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bash ~/start-vllm.sh
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# Train bt7274 persona adapter v4 (Hermes format, <think> blocks, 802 examples)
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train-bt7274:
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python train_v4.py
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# Start vLLM with v2 adapter override
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serve-v2:
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@echo "Starting vLLM with v2 adapter..."
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bash -c 'source /home/madcat/vllm-serve/bin/activate && \
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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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> /home/madcat/vllm-bt7274.log 2>&1 &'
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@echo "vLLM started (log: ~/vllm-bt7274.log)"
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# Train bt7274 v3 (legacy)
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train-bt7274-v3:
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python train_qwen35_27b.py
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# Train a specialist adapter
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train name:
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python train_specialist.py --name {{name}}
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# Train all specialists in sequence
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train-all:
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@echo "── Training all specialist adapters ──"
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@echo "Order: oxidizer → prism → serpent → forge → swiftblade → trace"
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@echo ""
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python train_specialist.py --name oxidizer
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python train_specialist.py --name prism
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python train_specialist.py --name serpent
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python train_specialist.py --name forge
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python train_specialist.py --name swiftblade
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python train_specialist.py --name trace
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# Train with custom data path
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train-custom name data:
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python train_specialist.py --name {{name}} --data {{data}}
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# ── Serving ──────────────────────────────────────────────────────────
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# List trained adapters
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adapters:
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@echo "── Trained Adapters ──"
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@for d in adapters/*/; do \
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if [ -f "$$d/adapter_model.safetensors" ]; then \
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size=$$(du -sh "$$d/adapter_model.safetensors" | cut -f1); \
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echo " ✓ $$(basename $$d) ($$size)"; \
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else \
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echo " ✗ $$(basename $$d) (no adapter_model.safetensors)"; \
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fi; \
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done
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# Transfer adapter to sin
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transfer name:
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@echo "── Transferring {{name}} to sin ──"
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@test -d "adapters/{{name}}" || (echo "ERROR: adapters/{{name}} not found" && exit 1)
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ssh madcat@192.168.88.108 "mkdir -p ~/models/loras/{{name}}"
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rsync -avP "adapters/{{name}}/" "madcat@192.168.88.108:~/models/loras/{{name}}/"
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@echo "✓ Transferred to sin:~/models/loras/{{name}}/"
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# Transfer all adapters to sin
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transfer-all:
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@for d in adapters/*/; do \
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name=$$(basename "$$d"); \
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if [ -f "$$d/adapter_model.safetensors" ]; then \
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echo "── Transferring $$name ──"; \
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ssh madcat@192.168.88.108 "mkdir -p ~/models/loras/$$name"; \
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rsync -avP "$$d" "madcat@192.168.88.108:~/models/loras/$$name/"; \
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fi; \
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done
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# ── Utilities ────────────────────────────────────────────────────────
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# Clean generated data (keeps hand-crafted datasets)
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clean-data:
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rm -rf data/*.jsonl
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@echo "Cleaned data/*.jsonl"
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# Clean trained adapters
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clean-adapters:
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rm -rf adapters/
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@echo "Cleaned adapters/"
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# Full clean
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clean: clean-data clean-adapters
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