From 47e8db525d6933c3094f75a5148c1588859e83c9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=95=D0=B2=D0=B3=D0=B5=D0=BD=D0=B8=D1=8F=20=D0=A1=D1=83?= =?UTF-8?q?=D1=85=D0=BE=D0=B4=D0=BE=D0=BB=D1=8C=D1=81=D0=BA=D0=B0=D1=8F?= Date: Tue, 13 May 2025 18:13:37 +0200 Subject: [PATCH] added link to the release --- qdrant-landing/content/articles/miniCOIL.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/qdrant-landing/content/articles/miniCOIL.md b/qdrant-landing/content/articles/miniCOIL.md index ca77add30..6cd923b53 100644 --- a/qdrant-landing/content/articles/miniCOIL.md +++ b/qdrant-landing/content/articles/miniCOIL.md @@ -232,8 +232,13 @@ Here are the specific characteristics of the miniCOIL model we trained based on | **Training Parameters** | **Epochs**: 60
**Optimizer**: Adam with a learning rate of 1e-4
**Validation set**: 20% | Each word was **trained on just one CPU**, and it took approximately fifty seconds per word to train. -We released this version of a miniCOIL in [our FastEmbed library](https://qdrant.tech/documentation/fastembed/). -TBD MINICOIL in FASTEMBED CC ANDREY/GEORGE ADD LINK BEFORE PUBLISHING. +We included this `minicoil-v1` version in the [v0.7.0 release of our FastEmbed library](https://github.com/qdrant/fastembed). + +You can check an example of `minicoil-v1` usage with FastEmbed in the [HuggingFace card](https://huggingface.co/Qdrant/minicoil-v1). + + ## Results