Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'
This commit is contained in:
ruv
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vendor/ruvector/examples/edge-net/pkg/models/registry.json
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vendor/ruvector/examples/edge-net/pkg/models/registry.json
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{
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"version": "1.0.0",
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"updated": "2026-01-03T00:00:00.000Z",
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"gcs_bucket": "ruvector-models",
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"ipfs_gateway": "https://ipfs.io/ipfs",
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"models": {
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"minilm-l6": {
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"name": "MiniLM-L6-v2",
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"type": "embedding",
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"huggingface": "Xenova/all-MiniLM-L6-v2",
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"dimensions": 384,
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"size": "22MB",
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"tier": 1,
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"quantized": ["int8", "fp16"],
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"description": "Fast, good quality embeddings for edge deployment",
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"recommended_for": ["edge-minimal", "low-memory"],
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"artifacts": {}
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},
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"e5-small": {
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"name": "E5-Small-v2",
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"type": "embedding",
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"huggingface": "Xenova/e5-small-v2",
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"dimensions": 384,
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"size": "28MB",
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"tier": 1,
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"quantized": ["int8", "fp16"],
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"description": "Microsoft E5 - excellent for retrieval tasks",
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"recommended_for": ["retrieval", "semantic-search"],
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"artifacts": {}
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},
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"bge-small": {
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"name": "BGE-Small-EN-v1.5",
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"type": "embedding",
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"huggingface": "Xenova/bge-small-en-v1.5",
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"dimensions": 384,
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"size": "33MB",
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"tier": 2,
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"quantized": ["int8", "fp16"],
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"description": "BAAI BGE - best for retrieval and ranking",
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"recommended_for": ["retrieval", "reranking"],
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"artifacts": {}
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},
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"gte-small": {
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"name": "GTE-Small",
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"type": "embedding",
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"huggingface": "Xenova/gte-small",
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"dimensions": 384,
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"size": "67MB",
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"tier": 2,
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"quantized": ["int8", "fp16"],
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"description": "General Text Embeddings - high quality",
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"recommended_for": ["general", "quality"],
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"artifacts": {}
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},
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"gte-base": {
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"name": "GTE-Base",
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"type": "embedding",
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"huggingface": "Xenova/gte-base",
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"dimensions": 768,
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"size": "100MB",
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"tier": 3,
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"quantized": ["int8", "fp16"],
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"description": "GTE Base - 768 dimensions for higher quality",
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"recommended_for": ["cloud", "high-quality"],
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"artifacts": {}
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},
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"multilingual-e5": {
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"name": "Multilingual-E5-Small",
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"type": "embedding",
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"huggingface": "Xenova/multilingual-e5-small",
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"dimensions": 384,
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"size": "118MB",
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"tier": 3,
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"quantized": ["int8", "fp16"],
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"description": "Supports 100+ languages",
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"recommended_for": ["multilingual", "international"],
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"artifacts": {}
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},
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"distilgpt2": {
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"name": "DistilGPT2",
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"type": "generation",
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"huggingface": "Xenova/distilgpt2",
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"size": "82MB",
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"tier": 1,
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"quantized": ["int8", "int4", "fp16"],
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"capabilities": ["general", "completion"],
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"description": "Fast distilled GPT-2 for text generation",
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"recommended_for": ["edge", "fast-inference"],
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"artifacts": {}
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},
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"tinystories": {
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"name": "TinyStories-33M",
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"type": "generation",
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"huggingface": "Xenova/TinyStories-33M",
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"size": "65MB",
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"tier": 1,
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"quantized": ["int8", "int4"],
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"capabilities": ["stories", "creative"],
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"description": "Ultra-small model trained on children's stories",
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"recommended_for": ["creative", "stories", "minimal"],
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"artifacts": {}
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},
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"starcoder-tiny": {
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"name": "TinyStarCoder-Py",
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"type": "generation",
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"huggingface": "Xenova/tiny_starcoder_py",
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"size": "40MB",
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"tier": 1,
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"quantized": ["int8", "int4"],
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"capabilities": ["code", "python"],
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"description": "Ultra-small Python code generation",
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"recommended_for": ["code", "python", "edge"],
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"artifacts": {}
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},
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"phi-1.5": {
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"name": "Phi-1.5",
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"type": "generation",
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"huggingface": "Xenova/phi-1_5",
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"size": "280MB",
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"tier": 2,
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"quantized": ["int8", "int4", "fp16"],
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"capabilities": ["code", "reasoning", "math"],
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"description": "Microsoft Phi-1.5 - excellent code and reasoning",
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"recommended_for": ["code", "reasoning", "balanced"],
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"artifacts": {}
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},
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"codegen-350m": {
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"name": "CodeGen-350M-Mono",
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"type": "generation",
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"huggingface": "Xenova/codegen-350M-mono",
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"size": "320MB",
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"tier": 2,
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"quantized": ["int8", "int4", "fp16"],
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"capabilities": ["code", "python"],
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"description": "Salesforce CodeGen - Python specialist",
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"recommended_for": ["code", "python"],
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"artifacts": {}
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},
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"qwen-0.5b": {
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"name": "Qwen-1.5-0.5B",
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"type": "generation",
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"huggingface": "Xenova/Qwen1.5-0.5B",
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"size": "430MB",
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"tier": 3,
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"quantized": ["int8", "int4", "fp16"],
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"capabilities": ["multilingual", "general", "code"],
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"description": "Alibaba Qwen 0.5B - multilingual capabilities",
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"recommended_for": ["multilingual", "general"],
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"artifacts": {}
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},
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"phi-2": {
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"name": "Phi-2",
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"type": "generation",
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"huggingface": "Xenova/phi-2",
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"size": "550MB",
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"tier": 3,
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"quantized": ["int8", "int4", "fp16"],
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"capabilities": ["code", "reasoning", "math", "general"],
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"description": "Microsoft Phi-2 - advanced reasoning model",
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"recommended_for": ["reasoning", "code", "quality"],
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"artifacts": {}
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},
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"gemma-2b": {
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"name": "Gemma-2B-IT",
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"type": "generation",
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"huggingface": "Xenova/gemma-2b-it",
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"size": "1.1GB",
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"tier": 4,
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"quantized": ["int8", "int4", "fp16"],
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"capabilities": ["instruction", "general", "code", "reasoning"],
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"description": "Google Gemma 2B instruction-tuned",
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"recommended_for": ["cloud", "high-quality", "instruction"],
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"artifacts": {}
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}
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},
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"profiles": {
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"edge-minimal": {
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"description": "Minimal footprint for constrained edge devices",
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"embedding": "minilm-l6",
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"generation": "tinystories",
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"total_size": "~87MB",
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"quantization": "int4"
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},
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"edge-balanced": {
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"description": "Best quality/size ratio for edge deployment",
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"embedding": "e5-small",
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"generation": "phi-1.5",
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"total_size": "~308MB",
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"quantization": "int8"
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},
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"edge-code": {
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"description": "Optimized for code generation tasks",
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"embedding": "bge-small",
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"generation": "starcoder-tiny",
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"total_size": "~73MB",
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"quantization": "int8"
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},
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"edge-full": {
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"description": "Maximum quality on edge devices",
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"embedding": "gte-base",
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"generation": "phi-2",
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"total_size": "~650MB",
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"quantization": "int8"
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},
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"cloud-optimal": {
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"description": "Best quality for cloud/server deployment",
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"embedding": "gte-base",
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"generation": "gemma-2b",
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"total_size": "~1.2GB",
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"quantization": "fp16"
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}
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},
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"adapters": {}
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}
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