cstr/multilingual-e5-small-GGUF

multilingual-e5-small GGUF

GGUF format of intfloat/multilingual-e5-small for use with CrispEmbed and Ollama.

Files

File	Quantization	Size
multilingual-e5-small-q4_k.gguf	Q4_K	0 MB
multilingual-e5-small-q8_0.gguf	Q8_0	0 MB
multilingual-e5-small.gguf	F32	0 MB

Recommended: Q8_0 for quality (cos vs HF: 0.9999), Q4_K for size (0.990).

Quick Start

CrispEmbed

./crispembed -m multilingual-e5-small "Hello world"
./crispembed-server -m multilingual-e5-small --port 8080

Ollama (with CrispStrobe fork)

# Create model
echo "FROM multilingual-e5-small-q8_0.gguf" > Modelfile
ollama create multilingual-e5-small -f Modelfile

# Embed
curl http://localhost:11434/api/embed -d '{"model":"multilingual-e5-small","input":["Hello world"]}'

Python (CrispEmbed)

from crispembed import CrispEmbed
model = CrispEmbed("multilingual-e5-small-q8_0.gguf")
vectors = model.encode(["Hello world", "Goodbye world"])

Model Details

Property	Value
Architecture	BERT
Parameters	118M
Embedding Dimension	384
Layers	12
Pooling	mean
Tokenizer	SentencePiece
Language	multilingual
Q8_0 vs HuggingFace	0.9999
Q4_K vs HuggingFace	0.990

Server API

CrispEmbed server supports four API dialects:

• POST /embed — native
• POST /v1/embeddings — OpenAI-compatible
• POST /api/embed — Ollama-compatible
• POST /api/embeddings — Ollama legacy

Credits

• Original model: intfloat/multilingual-e5-small
• Inference: CrispEmbed (MIT, ggml-based)