persona-model-trainer

persona-model-trainer

Fine-tune a small local model on persona data (raw + distilled). Turn anyone-skill's output into a self-contained model that is the person — no prompting, no cloud, no latency.

Dependency chain: anyone-skill → persona-knowledge → persona-model-trainer → runnable persona model ({model_id})

Input: training/ folder produced by anyone-skill Step 6-D / persona-knowledge export (raw/ + conversations.jsonl + probes.json)
Output: LoRA/QLoRA adapter weights + GGUF / Ollama / vLLM / ONNX exports

Full walkthrough: see [references/pipeline-guide.md](references/pipeline-guide.md) for the complete end-to-end guide (data → train → evaluate → version → run).

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When to use this skill

Trigger phrases:

• "train a model for this persona"
• "make it run locally / on my phone"
• "fine-tune on the distilled data"
• "I want a model, not just a prompt"
• "create a self-contained persona model"

Not suitable when:

• Effective assistant-role turns (raw/ + conversations.jsonl combined) < 200
• User only wants a quick prompt-based persona (use anyone-skill alone)

Fictional characters and historical figures can be trained if training/raw/ contains scripts, lore, speeches, or biographies — check actual turn count, not subject type.

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Quick Start — Pipeline Script

For standard use cases, pipeline.sh chains all phases (prepare → train → voice test → export) in one command:

# ── Gemma 4 preset (recommended for google/gemma-4-E4B-it) ──────────────────
# Apple Silicon — sets lora-rank=16, lora-layers=16, warmup-ratio=0.1, lora-alpha=16:
bash scripts/pipeline.sh \
  --slug {slug} \
  --model google/gemma-4-E4B-it \
  --source ./training \
  --method mlx \
  --preset gemma4 \
  --probes ./training/probes.json   # optional: probe_score eval (generated by persona-knowledge)

# NVIDIA GPU — same preset, Unsloth backend (QLoRA, fits 8 GB VRAM):
bash scripts/pipeline.sh \
  --slug {slug} \
  --model unsloth/gemma-4-4b-it-bnb-4bit \
  --source ./training \
  --method unsloth \
  --preset gemma4 \
  --probes ./training/probes.json   # omit if training/ was not exported by persona-knowledge

# ── Manual override (any model) ──────────────────────────────────────────────
# Local GPU — Apple Silicon (mlx) or NVIDIA (unsloth / qlora / lora):
bash scripts/pipeline.sh \
  --slug {slug} \
  --model {model_id} \
  --source ./training \
  --method mlx \
  --lora-rank 16 \
  --lora-layers 16 \
  --warmup-ratio 0.05 \
  --batch-size 2 \
  --learning-rate 2e-4 \
  --epochs 3

# No local GPU — train in Google Colab (free T4):
bash scripts/pipeline.sh \
  --slug {slug} \
  --model {model_id} \
  --source ./training \
  --method colab        # generates colab_train_{slug}.ipynb, then exits
# → Upload .ipynb to colab.research.google.com → Run all → download adapter zip
# → Unzip into models/{slug}/export/ then:
bash scripts/pipeline.sh --slug {slug} --model {model_id} --source ./training \
  --method skip-train   # runs voice_test + export on the downloaded adapter

# Dry-run to validate setup (writes nothing):
bash scripts/pipeline.sh ... --dry-run

# After the script finishes, run the model with Ollama:
ollama create {slug} -f models/{slug}/export/ollama/Modelfile
ollama run {slug}

# Phase 8–9: bundle into installed persona pack
# --model-dir points to the version management root (BASE_DIR), not export/ directly
python scripts/pack_integrate.py \
  --slug {slug} \
  --model-dir models/{slug}/
  # --pack-dir ~/.openpersona/personas/persona-{slug}/   # optional; auto-discovered if omitted
# → resolves export/ via manifest.json, copies artifacts, updates persona.json