Upload README.md with huggingface_hub

README.md

@@ -1,10 +1,10 @@
 ---
 base_model: 
-  - nomic-ai/nomic-embed-text-v1.5
+  - sentence-transformers/all-MiniLM-L6-v2
 language:
   - en
-model_creator: Nomic
-model_name: nomic-embed-text-v1.5
+model_creator: sentence-transformers
+model_name: all-MiniLM-L6-v2
 model_type: bert
 quantized_by: s3dev-ai
 tags:
@@ -13,12 +13,12 @@ tags:
 
 # Overview
 
-This page provides various quantisations of the [base model](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5), in GGUF format.
-- nomic-ai/nomic-embed-text-v1.5
+This page provides various quantisations of the [base model](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2), in GGUF format.
+- sentence-transformers/all-MiniLM-L6-v2
 
 # Model Description
 
-For a full model description, please refer to the [base model's](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) card.
+For a full model description, please refer to the [base model's](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) card.
 
 ## How are the GGUF files created?
 After cloning the author's original base model repository, `llama.cpp` is used to convert the model to a GGML compatible file, using `f32` as the output type; preserving the original fidelity. The model is converted *un-altered*, unless otherwise stated.
@@ -31,7 +31,8 @@ To help visualise the difference in model quantisation (i.e. level of retained f
 
 The underlying [base dataset](https://huggingface.co/datasets/sentence-transformers/stsb) was sampled to 1000 records with a unbiased similarity score distribution. Using the various quantisation levels of this model, embeddings were created for `sentence1` and `sentence2`. Finally, a cosine similarity score was calculated across the two embeddings, and plotted on the graph.   
 
-<!-- Image alignment -->
+<!-- Quantisation image -->
 <div align="center">
-  <img src="imgs/nomic.png" alt="Quantisation Levels" width="90%">
+  <img src="imgs/minilm.png" alt="Quantisation Levels" width="90%">
 </div>
+