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gte-large-gguf

by ChristianAzinn · source Hugging Face · updated 2024-04-07

mit206 MB~1 GB RAMsource aliveunlabeled

General Text Embeddings (GTE) model. Towards General Text Embeddings with Multi-stage Contrastive Learning

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Source and license

Numbers

Numbers as of 2026-10-02 20:59 UTC, from the source API.

Summary

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Files

filequantsize
gte-large.Q2_K.ggufQ2_K138 MB
gte-large.Q3_K_L.ggufQ3_K_L189 MB
gte-large.Q3_K_M.ggufQ3_K_M173 MB
gte-large.Q3_K_S.ggufQ3_K_S152 MB
gte-large.Q4_0.ggufQ4_0190 MB
gte-large.Q4_K_M.ggufQ4_K_M206 MB
gte-large.Q4_K_S.ggufQ4_K_S194 MB
gte-large.Q5_0.ggufQ5_0226 MB
gte-large.Q5_K_M.ggufQ5_K_M234 MB
gte-large.Q5_K_S.ggufQ5_K_S226 MB
gte-large.Q6_K.ggufQ6_K265 MB
gte-large.Q8_0.ggufQ8_0342 MB
gte-large_fp16.gguf639 MB
gte-large_fp32.gguf1.2 GB

From the source README

gte-large-gguf

Model creator: thenlper

Original model: gte-large

Original Description

General Text Embeddings (GTE) model. Towards General Text Embeddings with Multi-stage Contrastive Learning

The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including information retrieval, semantic textual similarity, text reranking, etc.

Description

This repo contains GGUF format files for the gte-large embedding model.

These files were converted and quantized with llama.cpp PR 5500, commit 34aa045de, on a consumer RTX 4090.

This model supports up to 512 tokens of context.

Compatibility

These files are compatible with llama.cpp as of commit 4524290e8, as well as LM Studio as of version 0.2.19.

Card id model:hf:ChristianAzinn/gte-large-gguf · collected 2026-10-02 20:59 UTC · JSON