gte-small-GGUF

gte-small GGUF

GGUF format of thenlper/gte-small for use with CrispEmbed and Ollama.

Files

Recommended: Q8_0 for quality (cos vs HF: 0.9999), Q4_K for size (0.991).

Quick Start

CrispEmbed

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

Ollama (with CrispStrobe fork)

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

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

Python (CrispEmbed)

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

Model Details

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: thenlper/gte-small
• Inference: CrispEmbed (MIT, ggml-based)

Provenance and EU AI Act Art. 53 note

• Upstream model: thenlper/gte-small — published by thenlper.
• Upstream licence: mit. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
• What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
• Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
• Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.