| # Model Selection Policy | |
| # Used by model-selection/SKILL.md as the routing and ranking source of truth. | |
| # Edit this file to adjust priorities without touching the skill logic. | |
| default_output_count: 3 | |
| always_include_budget_option: true | |
| always_include_frontier_option: true | |
| # Scoring weights (must sum to 1.0) | |
| weights: | |
| task_fit: 0.40 # primary_tasks match | |
| modality_fit: 0.20 # input/output/conditioning modality match | |
| compute_fit: 0.15 # compute_tier within stated budget | |
| openness_fit: 0.10 # open_weights + commercial_use alignment | |
| maturity_score: 0.10 # established > recent > experimental | |
| deployment_practicality: 0.05 # single_gpu_feasible, no gating, etc. | |
| # Penalties applied on top of the weighted score | |
| penalties: | |
| gated: 0.20 # gated=true and user wants frictionless | |
| over_budget_compute: 0.25 # compute_tier exceeds stated budget | |
| unknown_commercial_status: 0.15 # commercial_use=unknown when deployment is commercial | |
| legacy_model: 0.10 # status=legacy or exclusion_tags contains prefer_*_over_this | |
| non_commercial_commercial_deploy: 0.30 # commercial_use=non_commercial_only|research_only when commercial | |
| # Default assumed compute budget when none stated | |
| default_compute_budget: single_gpu_midmem | |
| # Task → ordered candidate list (first is preferred; apply compute filter after) | |
| routing: | |
| embedding: | |
| - esmc_300m # strongest small embedder (non-commercial) | |
| - esm2_t33_650m # best commercial default | |
| - saprot_650m # structure-aware, if structure available | |
| - ankh3_large # multi-task pretrain, strong generalization | |
| - prot_t5_xl_enc # T5 ecosystem; slower but competitive | |
| - esm2_t30_150m # budget fallback | |
| variant_effect_prediction: | |
| - esm1v_650m # purpose-built masked-marginal scorer | |
| - tranception # autoregressive + retrieval; best on fitness landscapes | |
| - saprot_650m # structure-aware variant effect if structure available | |
| - esm2_t33_650m # general fallback with PLL scoring | |
| - esm2_t6_8m # budget option with PLL scoring | |
| fitness_prediction: | |
| - tranception | |
| - esm1v_650m | |
| - esm2_t33_650m | |
| sequence_generation: | |
| - protgpt2 # established decoder-only baseline | |
| - dplm_650m # diffusion; better diversity | |
| - esm3_sm_open # generative multimodal (non-commercial) | |
| controlled_generation: | |
| - esm3_sm_open # best open for structure/function conditioned | |
| - dplm2_150m # joint seq+structure, lighter | |
| - nv_la_proteina # frontier all-atom (gated, research-only) | |
| folding: | |
| - esmfold_v1 # fast MSA-free, open weights | |
| - boltz_2 # better for complexes / affinity | |
| - dplm2_150m # lighter; co-designs sequence + structure | |
| inverse_folding: | |
| - esm_if1 # purpose-built, small, commercial | |
| - dplm_650m # diffusion-based; good diversity | |
| - dplm2_150m # joint seq+structure design | |
| motif_scaffolding: | |
| - dplm_650m | |
| - dplm2_150m | |
| - nv_la_proteina # frontier all-atom scaffolding | |
| binder_design: | |
| - boltzgen_1 # purpose-built binder generator | |
| - nv_la_proteina # all-atom frontier design | |
| - boltz_2 # complex prediction + affinity | |
| complex_prediction: | |
| - boltz_2 | |
| - boltzgen_1 | |
| binding_affinity_prediction: | |
| - boltz_2 | |
| nucleotide_representation: | |
| - nucleotide_transformer_v2_500m | |
| biomedical_nlp: | |
| - biomedbert_base # MIT, PubMed abstracts | |
| - biobert_base # Apache 2.0, PubMed + PMC | |
| # Hard filter rules (applied before scoring) | |
| # Models failing any applicable rule are excluded from candidates. | |
| hard_filters: | |
| - rule: task_mismatch | |
| description: Exclude if task not in model primary_tasks or secondary_tasks | |
| - rule: compute_over_budget | |
| description: Exclude if compute_tier > stated budget (unless no candidate passes) | |
| - rule: gated_frictionless | |
| description: Exclude gated models when user explicitly requests frictionless access | |
| - rule: commercial_restriction | |
| description: Exclude non_commercial_only/research_only models when commercial deployment is implied | |
| - rule: wrong_category | |
| description: Exclude nucleotide_language_model and biomedical_text_model for protein tasks (and vice versa) | |