code/self_healing_finetuning/prompt_packs/model_selection.md

Model Selection Prompt Pack

Apply SHFT model-profile policy. Do not hardcode a single base model into launchers, providers, trainers, proof jobs, or generated documentation.

Default profile:

• fingpt
• candidate: linvest21/linvest21_fingpt_v1_000
• base model: meta-llama/Meta-Llama-3-8B
• start behavior: approved Linvest21 FinGPT adapter bootstrap
• source FinGPT adapter lineage: FinGPT/fingpt-mt_llama3-8b_lora
• license posture: Meta Llama 3 community license plus FinGPT adapter terms; commercial review required

Open commercial foundation profile:

• qwen3_32b
• candidate/base model: Qwen/Qwen3-32B
• start behavior: fresh QLoRA from the base model, no baseline adapter
• proof baseline: raw Qwen/Qwen3-32B
• license posture: Apache-2.0; commercial use allowed

Operator controls:

python -m n21.cli select-model --task finance_qa --env dev --model-profile fingpt
python -m n21.cli select-model --task finance_qa --env dev --model-profile qwen3_32b
set SHFT_MODEL_PROFILE=qwen3_32b

Implementation rules:

• Read profile definitions from configs/model_profiles.json.
• Resolve profiles through model_policy/profiles.py.
• Preserve SHFT_MODEL_CANDIDATE and SHFT_BASE_MODEL_ID only as explicit overrides.
• Keep paired proof baseline-aware: adapter baseline for fingpt, raw base-model baseline for qwen3_32b.
• Adding a future model should mean adding a profile entry, not editing every script.