Registre de modèles PMOVES

Interrogez, découvrez et enrichissez le catalogue de modèles IA PMOVES. Gère les métadonnées, l'enrichissement HuggingFace, l'export TensorZero et le suivi des déploiements GPU.

Spar Skills Guide Bot
Data & IAIntermédiaire
0022/07/2026
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#model-registry#llm#embedding#tensorzero#gpu-deployment

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name: PMOVES Model Registry description: | Query, discover, and enrich the PMOVES.AI model catalog. Manages all AI model metadata (LLM, embedding, TTS, vision), HuggingFace enrichment, TensorZero TOML config export, and GPU deployment tracking across the fleet. keywords: models, registry, catalog, embedding, llm, tensorzero, huggingface, gpu, deployment, discovery version: 1.0.0 category: Infrastructure/AI tier: 1 agent_class: Standard agent_id: pmoves_model_registry

PMOVES Model Registry

Agent Class: Standard (Pmoves-) Category: Infrastructure/AI Version: 1.0.0 Tier: 1 (Core Infrastructure) Status: Active — Supabase-backed model catalog + HuggingFace enrichment Port: 8110


Capabilities

| Command | What It Does | |---------|-------------| | list-models | List all active models with optional type/provider filter | | get-model | Get detailed metadata for a single model by ID | | enrich-hf | Fetch dimensions, tags, CUDA support from HuggingFace API | | enrich-hf-bulk | Batch-enrich all models that have hf_id in metadata | | export-tensorzero | Generate TensorZero TOML config from catalog | | list-deployments | Show active GPU model deployments across fleet | | register-deployment | Register/update a model deployment (GPU Orchestrator) | | service-models | List models mapped to a specific service |


Trigger Phrases (Pinokio 7 Interpreter)

| Phrase | Action | Endpoint | |--------|--------|----------| | "list available models" | Show full model catalog | GET /api/models | | "show embedding models" | Filter catalog by type | GET /api/models?model_type=embedding | | "show LLM models" | Filter catalog by type | GET /api/models?model_type=llm | | "get model details for [id]" | Single model lookup | GET /api/models/{id} | | "enrich model from huggingface" | Fetch HF metadata + dimensions | POST /api/models/{id}/enrich-hf | | "enrich all embedding models" | Batch HF enrichment | POST /api/models/enrich-hf-bulk | | "export tensorzero config" | Download TensorZero TOML | GET /api/tensorzero/config | | "show GPU deployments" | List active model deployments | GET /api/deployments | | "which models are on 5090" | Filter deployments by node | GET /api/deployments?node_id=5090 | | "what models does hi-rag use" | Service-specific model lookup | GET /api/services/hi-rag/models |


API Reference

Model Catalog

# List all models
curl http://localhost:8110/api/models

# Filter by type (embedding, llm, tts, vision, audio)
curl http://localhost:8110/api/models?model_type=embedding

# Filter by provider (ollama, openai, anthropic, venice)
curl http://localhost:8110/api/models?provider=ollama

# Get single model
curl http://localhost:8110/api/models/{model_id}

# Models for a service
curl http://localhost:8110/api/services/hi-rag/models

HuggingFace Enrichment

# Enrich a single model (requires metadata.hf_id set)
curl -X POST http://localhost:8110/api/models/{model_id}/enrich-hf

# Batch-enrich all embedding models
curl -X POST http://localhost:8110/api/models/enrich-hf-bulk?model_type=embedding

TensorZero Config Export

# Generate TOML config from catalog
curl http://localhost:8110/api/tensorzero/config -o tensorzero.toml

GPU Deployments

# List active deployments
curl http://localhost:8110/api/deployments

# Filter by node
curl http://localhost:8110/api/deployments?node_id=5090

# Filter by status
curl http://localhost:8110/api/deployments?status=loaded

Health Check

curl http://localhost:8110/healthz
# → {"status": "healthy", "timestamp": "...", "services": {"supabase": "...", "nats": "..."}}

Integration Points

  • Supabasepmoves_core.models, pmoves_core.model_service_mapping, pmoves_core.v_active_deployments
  • NATS — Publishes model.registry.updated.v1 on catalog mutations (model enriched, deployment registered)
  • GPU Orchestrator — Calls POST /api/deployments when models are loaded/unloaded on GPU nodes
  • TensorZero Gateway — Consumes exported TOML config for model provider routing
  • HuggingFace API — Fetches model cards, config.json for embedding dimensions, tags, CUDA support

Prerequisites

  • Supabase running with pmoves_core schema seeded
  • NATS message bus at port 4222 (optional — catalog changes still work without NATS)
  • HuggingFace API accessible (no auth required for public models)
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