echo-persona

name	echo-persona
description	Train, evaluate, and maintain the scawful-echo persona and related avatar models (Echo/Memory/Muse). Use when working on persona voice, dataset prep, A/B testing, deployment, or tool-calling constraints for avatar models.

Echo Persona

Scope

• Maintain scawful-echo voice, datasets, training runs, and evals for avatar models.

Voice guardrails

• Write in lowercase, candid, lightly stream-of-consciousness style.
• Use dry humor with quiet hopefulness.
• Stay technical when it matters, casual otherwise.
• Avoid marketing tone or corporate polish.
• Keep responses conversational and grounded in known facts.

Workflow

1. Confirm which avatar track is in scope (echo, memory, muse).
   • Use ~/src/lab/afs-scawful/docs/afs/avatar-models-comparison.md for role intent.
2. Locate the dataset pipeline.
   • Use ~/src/training/docs/SCAWFUL_ECHO_V2.md for the build script and mix.
   • Default output: ~/src/training/datasets/scribe-corpus/mlx_data_scawful_echo_v2/.
3. Apply data prep rules and labels.
   • Follow ~/src/training/docs/avatar_data_prep.md for schema and labeling.
4. Choose base model with tool-calling constraints in mind.
   • Prefer Qwen 2.5 when tool calling is required.
   • Treat Gemma 2 as tool-calling limited (see ~/src/training/docs/SCAWFUL_ECHO_AB_PLAN.md).
5. Run training and monitoring.
   • Use ~/src/training/docs/avatar_training_ops.md for watchers, alerts, and backups.
6. Evaluate with a fixed rubric.
   • Use persona fidelity, factual consistency, chat naturalness, and hallucination rate.
   • Use ~/src/training/evals/avatar_text_prompt_pack.jsonl for quick checks.
7. Package and deploy.
   • Convert with ~/src/tools/model-mgr/model-mgr (GGUF/MLX).
   • Deploy to LM Studio (preferred) or Ollama.

Knowledge References

Consult the global knowledge base at ~/.context/knowledge/models/ for background:

• Model portfolio (Echo lineage, Avatar family): models/portfolio.md
• Training pipeline & quality scoring: models/training-pipeline.md
• Dataset catalog (Echo v2-v4, repair seeds): models/datasets.md
• Deployment workflows: models/workflows.md
• Serving & routing (Avatar router): models/serving.md

References

• Read references/sources.md for source paths and anchors.