docs: Update main docs

README.md

@@ -9,17 +9,16 @@
 <div align="center">
   <h2>
     <a href="https://huggingface.co/minishlab"><strong>🤗 Models</strong></a> |
-    <a href="https://github.com/MinishLab/model2vec/tree/main/tutorials"><strong>📚 Tutorials</strong></a> |
-    <a href="https://minishlab.github.io/"><strong>🌐 Blog</strong></a> |
+    <a href="https://minish.ai/packages/model2vec/introduction"><strong>📖 Docs</strong></a> |
     <a href="https://github.com/MinishLab/model2vec/blob/main/results/README.md"><strong>🏆 Results</strong></a> |
-    <a href="https://github.com/MinishLab/model2vec/blob/main/docs"><strong>📖 Docs</strong></a>
-  </h2>
+    <a href="https://github.com/MinishLab/model2vec/tree/main/tutorials"><strong>📚 Tutorials</strong></a> |
+    <a href="https://minish.ai/blog"><strong>🌐 Blog</strong></a>
 </div>
 
 <div align="center">
   <h2>
     <a href="https://pypi.org/project/model2vec/"><img src="https://img.shields.io/pypi/v/model2vec?color=%23007ec6&label=pypi%20package" alt="Package version"></a>
-    <a href="https://pypi.org/project/model2vec/"><img src="https://img.shields.io/pypi/pyversions/model2vec" alt="Supported Python versions"></a>
+    <a href="https://minish.ai/packages/model2vec/introduction"><img src="https://img.shields.io/badge/docs-minish.ai-blue.svg" alt="Docs"></a>
     <a href="https://pepy.tech/project/model2vec">
       <img src="https://static.pepy.tech/badge/model2vec" alt="Downloads">
     </a>
@@ -32,14 +31,17 @@
     <a href="https://github.com/MinishLab/model2vec/blob/main/LICENSE">
       <img src="https://img.shields.io/badge/license-MIT-green" alt="License - MIT">
     </a>
+    <a href="https://github.com/MinishLab/model2vec/stargazers">
+      <img src="https://img.shields.io/github/stars/minishlab/model2vec.svg" alt=Stars">
+    </a>
   </h2>
 </div>
 
 
 
 
 
-Model2Vec is a technique to turn any sentence transformer into a really small static model, reducing model size by a factor up to 50 and making the models up to 500 times faster, with a small drop in performance. Our [best model](https://huggingface.co/minishlab/potion-base-8M) is the most performant static embedding model in the world. See our results [here](results/README.md), or dive in to see how it works.
+Model2Vec is a technique to turn any sentence transformer into a small, fast static embedding model. Model2Vec reduces model size by a factor up to 50 and makes models up to 500 times faster, with a small drop in performance. Our [best model](https://huggingface.co/minishlab/potion-base-8M) is the most performant static embedding model in the world. See our [results](results/README.md), read our [docs](https://minish.ai/packages/model2vec/introduction), or dive in to see how it works.
 
 <div align="center">
 <h3>
@@ -69,15 +71,14 @@ embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to ever
 # Make sequences of token embeddings
 token_embeddings = model.encode_as_sequence(["It's dangerous to go alone!", "It's a secret to everybody."])
 ```
-
-Instead of using one of our models, you can also distill your own Model2Vec model from a Sentence Transformer model. First, install the `distillation` extras with:
+For advanced usage, see our [inference docs](https://minish.ai/packages/model2vec/inference). Instead of using one of our models, you can also distill your own Model2Vec model from a Sentence Transformer model. First, install the `distillation` extras with:
 
 ```bash
 pip install model2vec[distill]
 ```
 
 
- Then, you can distill a model in ~30 seconds on a CPU with the following code snippet:
+Then, you can distill a model in ~30 seconds on a CPU with the following code snippet:
 
 ```python
 from model2vec.distill import distill
@@ -89,7 +90,7 @@ m2v_model = distill(model_name="BAAI/bge-base-en-v1.5", pca_dims=256)
 m2v_model.save_pretrained("m2v_model")
 ```
 
-After distillation, you can also fine-tune your own classification models on top of the distilled model, or on a pre-trained model. First, make sure you install the `training` extras with:
+For advanced usage, see our [distillation docs](https://minish.ai/packages/model2vec/distillation), which includes some [distillation best practices](https://minish.ai/packages/model2vec/distillation#distillation-best-practices). After distillation, you can also fine-tune your own classification models on top of the distilled model, or on a pre-trained model. First, make sure you install the `training` extras with:
 
 ```bash
 pip install model2vec[train]
@@ -115,13 +116,13 @@ classifier.fit(ds["train"]["text"], ds["train"]["label"])
 classification_report = classifier.evaluate(ds["test"]["text"], ds["test"]["label"])
 ```
 
-For advanced usage, please refer to our [usage documentation](https://github.com/MinishLab/model2vec/blob/main/docs/usage.md).
+For advanced usage, see our [training docs](https://minish.ai/packages/model2vec/training).
 
 ## Updates & Announcements
 
 - **23/05/2025**: We released [potion-multilingual-128M](https://huggingface.co/minishlab/potion-multilingual-128M), a multilingual model trained on 101 languages. It is the best performing static embedding model for multilingual tasks, and is capable of generating embeddings for any text in any language. The results can be found in our [results](results/README.md#mmteb-results-multilingual) section.
 
-- **01/05/2025**: We released backend support for `BPE` and `Unigram` tokenizers, along with quantization and dimensionality reduction. New Model2Vec models are now 50% of the original models, and can be quantized to int8 to be 25% of the size, without loss of performance.
+- **01/05/2025**: We released backend support for `BPE` and `Unigram` tokenizers, along with quantization and dimensionality reduction. New Model2Vec models are now 50% of the original models size, and can be quantized to int8 to be 25% of the size, without loss of performance.
 
 - **12/02/2025**: We released **Model2Vec training**, allowing you to fine-tune your own classification models on top of Model2Vec models. Find out more in our [training documentation](https://github.com/MinishLab/model2vec/blob/main/model2vec/train/README.md) and [results](results/README.md#training-results).
 
@@ -137,24 +138,18 @@ For advanced usage, please refer to our [usage documentation](https://github.com
 - **Lightning-fast Inference**: up to 500 times faster on CPU than the original model.
 - **Fast, Dataset-free Distillation**: distill your own model in 30 seconds on a CPU, without a dataset.
 - **Fine-tuning**: fine-tune your own classification models on top of Model2Vec models.
-- **Integrated in many popular libraries**: Model2Vec is integrated direclty into popular libraries such as [Sentence Transformers](https://github.com/UKPLab/sentence-transformers) and [LangChain](https://github.com/langchain-ai/langchain). For more information, see our [integrations documentation](https://github.com/MinishLab/model2vec/blob/main/docs/integrations.md).
+- **Integrated in many popular libraries**: Model2Vec is integrated direclty into popular libraries such as [Sentence Transformers](https://github.com/UKPLab/sentence-transformers) and [LangChain](https://github.com/langchain-ai/langchain). For more information, see our [integrations documentation](https://minish.ai/packages/model2vec/integrations).
 - **Tightly integrated with HuggingFace hub**: easily share and load models from the HuggingFace hub, using the familiar `from_pretrained` and `push_to_hub`. Our own models can be found [here](https://huggingface.co/minishlab).
 
 ## What is Model2Vec?
 
 Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Like BPEmb, it can create subword embeddings, but with much better performance. Distillation doesn't need _any_ data, just a vocabulary and a model.
 
-The core idea is to forward pass a vocabulary through a sentence transformer model, creating static embeddings for the indiviudal tokens. After this, there are a number of post-processing steps we do that results in our best models. For a more extensive deepdive, please refer to the following resources:
-- Our initial [Model2Vec blog post](https://huggingface.co/blog/Pringled/model2vec). Note that, while this post gives a good overview of the core idea, we've made a number of substantial improvements since then.
-- Our [Tokenlearn blog post](https://minishlab.github.io/tokenlearn_blogpost/). This post describes the Tokenlearn method we used to train our [potion models](https://huggingface.co/collections/minishlab/potion-6721e0abd4ea41881417f062).
-- Our official [documentation](https://github.com/MinishLab/model2vec/blob/main/docs/what_is_model2vec.md). This document provides a high-level overview of how Model2Vec works.
+The core idea is to forward pass a vocabulary through a sentence transformer model, creating static embeddings for the indiviudal tokens. After this, there are a number of post-processing steps we do that results in our best models, as well as an optional pre-training step to further boost performance. For a more extensive deepdive, please refer to our [official documentation on how Model2Vec works](https://minish.ai/packages/model2vec/introduction#how-mode2vec-works).
 
 ## Documentation
 
-Our official documentation can be found [here](https://github.com/MinishLab/model2vec/blob/main/docs/README.md). This includes:
-- [Usage documentation](https://github.com/MinishLab/model2vec/blob/main/docs/usage.md): provides a technical overview of how to use Model2Vec.
-- [Integrations documentation](https://github.com/MinishLab/model2vec/blob/main/docs/integrations.md): provides examples of how to use Model2Vec in various downstream libraries.
-- [Model2Vec technical documentation](https://github.com/MinishLab/model2vec/blob/main/docs/what_is_model2vec.md): provides a high-level overview of how Model2Vec works.
+Our official documentation can be found [here](https://minish.ai/packages/model2vec/introduction). This includes in-depth documentation on [inference](https://minish.ai/packages/model2vec/inference), [distillation](https://minish.ai/packages/model2vec/distillation), [training](https://minish.ai/packages/model2vec/training), and [integrations](https://minish.ai/packages/model2vec/integrations).
 
 
 ## Model List

docs/README.md

@@ -1,6 +1,8 @@
 # Documentation
 
-This directory contains the documentation for Model2Vec. The documentation is formatted in Markdown. The documentation is organized as follows:
-- [usage.md](https://github.com/MinishLab/model2vec/blob/main/docs/usage.md): This document provides a technical overview of how to use Model2Vec.
-- [integrations.md](https://github.com/MinishLab/model2vec/blob/main/docs/integrations.md): This document provides examples of how to use Model2Vec in various downstream libraries.
-- [what_is_model2vec.md](https://github.com/MinishLab/model2vec/blob/main/docs/what_is_model2vec.md): This document provides a high-level overview of how Model2Vec works.
+Model2Vec's extensive documentation can be found on [our documentation website](https://minish.ai/packages/model2vec/introduction). This includes in-depth documentation on:
+- [Inference](https://minish.ai/packages/model2vec/inference)
+- [Distillation](https://minish.ai/packages/model2vec/distillation)
+- [Training](https://minish.ai/packages/model2vec/training)
+- [Integrations](https://minish.ai/packages/model2vec/integrations)
+- [How Model2Vec works](https://minish.ai/packages/model2vec/introduction#how-mode2vec-works)