Update README.md

README.md

@@ -8654,12 +8654,11 @@ language:
 ---
 
 # Mini-GTE
+## Overview
+This is the first model developed by QTACK and serves as a proof of concept for our distillation approach! Built upon a distillbert-based architecture, Mini-GTE is distilled from GTE and designed for efficiency without sacrificing accuracy at only 66M parameters. As a standalone sentence transformer, it ranks 2nd on the MTEB classic leaderboard in the <100M parameter category and 63rd overall which makes it a strong choice for real-time query encoding, semantic search, and similarity tasks.
 
-This is a distillbert-based model trained from GTE-base. It can be used as a faster query encoder for the GTE series or as a standalone unit (MTEB scores are for standalone). 
 
 ## Model Details
-
-### Model Description
 - **Model Type:** Sentence Transformer
 - **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
 - **Maximum Sequence Length:** 512 tokens
@@ -8667,20 +8666,24 @@ This is a distillbert-based model trained from GTE-base. It can be used as a fas
 - **Similarity Function:** Cosine Similarity
 
 ## Usage
+- Optimized for quick inference
+- Great at quickly generating high quality encodings
+- Easy to plug and play since it is distilled from GTE
 
-### Direct Usage (Sentence Transformers)
-
-First install the Sentence Transformers library:
+## Getting Started
+### Installation
+Mini-GTE is built on the [Sentence Transformers](https://www.sbert.net/) framework. To install the required packages, run:
 
 ```bash
 pip install -U sentence-transformers
 ```
+### Quick Start
+Here's a quick example to get you started:
 
-Then you can load this model and run inference.
 ```python
 from sentence_transformers import SentenceTransformer
 
-# Download from the 🤗 Hub
+# Download directly from Hugging Face
 model = SentenceTransformer("sentence_transformers_model_id")
 # Run inference
 sentences = [
@@ -8689,54 +8692,14 @@ sentences = [
     'He drove to the stadium.',
 ]
 embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 768]
+print(embeddings.shape) # Expected: [3, 768]
 
-# Get the similarity scores for the embeddings
+# Compute the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
-print(similarities.shape)
-# [3, 3]
+print(similarities.shape) # Expected: [3, 3]
 ```
 
-<!--
-### Direct Usage (Transformers)
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-### Downstream Usage (Sentence Transformers)
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-
 ## Training Details
-
-### Framework Versions
 - Python: 3.10.12
 - Sentence Transformers: 3.3.1
 - Transformers: 4.48.0.dev0
@@ -8746,23 +8709,15 @@ You can finetune this model on your own dataset.
 - Tokenizers: 0.21.0
 
 ## Citation
+```bibtex
+@misc{mini-gte2025,
+  title={Mini-GTE: A Fast and Efficient Distilled Sentence Transformer},
+  author={QTACK},
+  year={2025},
+  note={Available on the Hugging Face Hub}
+}
+```
 
-### BibTeX
-
-<!--
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-## Model Card Contact
+## Getting Help
 
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+For any questions, suggestions, or issues, please contact the QTACK team directly through our [contact page](https://www.qtack.com/contact).