6507cffc15
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
102 lines
4.6 KiB
Makefile
102 lines
4.6 KiB
Makefile
# AutoLoRA Training Pipeline
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# Replaces: autolora repo (1,500 lines) with config + make targets
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#
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# Prerequisites:
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# pip install axolotl mlx-lm lm-evaluation-harness pyyaml
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#
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# Targets:
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# make train-cloud — QLoRA on cloud GPU via Axolotl
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# make train-local — LoRA on Apple Silicon via MLX
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# make eval — Standard benchmarks via lm-eval-harness
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# make vibes — Hand-picked prompts through Ollama, human review
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# make ingest — Pull heartbeat trajectories into training data
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# make curated — Regenerate curated exemplar dataset
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MODEL ?= timmy:v0.1-q4
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BASELINE ?= hermes3:latest
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OLLAMA_URL ?= http://localhost:11434
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OUTPUT ?= output
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# ── Training ──────────────────────────────────────────────────────────
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train-cloud: ## QLoRA fine-tune on cloud GPU (Axolotl)
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axolotl train axolotl.yaml
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train-local: ## LoRA fine-tune on Apple Silicon (MLX)
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python -m mlx_lm.lora --config mlx-lora.yaml
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# ── Evaluation ────────────────────────────────────────────────────────
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eval: ## Run standard benchmarks against Ollama model
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lm_eval --model local-completions \
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--model_args "model=$(MODEL),base_url=$(OLLAMA_URL)/v1,tokenized_requests=False" \
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--tasks hellaswag,truthfulqa_mc2,arc_challenge,winogrande \
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--output_path evals_archive/$(MODEL)/
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@echo "Results in evals_archive/$(MODEL)/"
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eval-baseline: ## Run same benchmarks against baseline for comparison
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lm_eval --model local-completions \
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--model_args "model=$(BASELINE),base_url=$(OLLAMA_URL)/v1,tokenized_requests=False" \
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--tasks hellaswag,truthfulqa_mc2,arc_challenge,winogrande \
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--output_path evals_archive/$(BASELINE)/
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vibes: ## Run vibes check — hand-picked prompts, human review
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@echo "=== Vibes Check: $(MODEL) ==="
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@echo "Date: $$(date '+%Y-%m-%d %H:%M')" > $(OUTPUT)/vibes-$(MODEL).md
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@echo "Model: $(MODEL)" >> $(OUTPUT)/vibes-$(MODEL).md
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@echo "" >> $(OUTPUT)/vibes-$(MODEL).md
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@python -c "\
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import yaml, subprocess, sys; \
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prompts = yaml.safe_load(open('data/prompts_vibes.yaml'))['prompts']; \
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f = open('$(OUTPUT)/vibes-$(MODEL).md', 'a'); \
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[(\
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sys.stdout.write(f\" [{p['id']}] {p['category']}...\"), \
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sys.stdout.flush(), \
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f.write(f\"## [{p['id']}] {p['category']}\n\"), \
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f.write(f\"PROMPT: {p['prompt']}\n\"), \
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f.write(f\"EXPECTED: {p['expected']}\n\n\"), \
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f.write('RESPONSE:\n'), \
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f.write(subprocess.run( \
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['ollama', 'run', '$(MODEL)', p['prompt']], \
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capture_output=True, text=True, timeout=120 \
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).stdout), \
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f.write('\n\nSCORE: ___/5\n\n---\n\n'), \
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print(' done') \
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) for p in prompts]; \
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f.close()"
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@echo "Output: $(OUTPUT)/vibes-$(MODEL).md — fill in scores manually."
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# ── Data Pipeline ─────────────────────────────────────────────────────
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ingest: ## Pull heartbeat trajectories into training data
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python ingest_trajectories.py \
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--trajectories ~/.nexus/trajectories/ \
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--curated data/curated_dataset.jsonl \
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--output data/merged_training_data.jsonl
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@echo "Merged dataset ready. Convert for MLX with: make convert"
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curated: ## Regenerate curated exemplar dataset
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python build_curated.py
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@echo "Curated dataset regenerated."
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convert: ## Convert merged dataset to MLX format (train/valid split)
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@mkdir -p data/mlx_curated
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python -c "\
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import json; \
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lines = open('data/merged_training_data.jsonl').readlines(); \
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sessions = [json.loads(l) for l in lines]; \
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ROLE_MAP = {'system':'system','human':'user','gpt':'assistant','tool':'user'}; \
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converted = [{'messages': [{'role': ROLE_MAP.get(t.get('from',''),'user'), 'content': t.get('value','')} for t in s.get('conversations',[])]} for s in sessions]; \
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split = max(1, int(len(converted)*0.9)); \
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open('data/mlx_curated/train.jsonl','w').writelines(json.dumps(c)+'\n' for c in converted[:split]); \
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open('data/mlx_curated/valid.jsonl','w').writelines(json.dumps(c)+'\n' for c in converted[split:]); \
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print(f'train: {split}, valid: {len(converted)-split}')"
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# ── Helpers ───────────────────────────────────────────────────────────
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.PHONY: train-cloud train-local eval eval-baseline vibes ingest curated convert help
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help: ## Show this help
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@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | \
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awk 'BEGIN {FS = ":.*?## "}; {printf " \033[36m%-16s\033[0m %s\n", $$1, $$2}'
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