Update Readme: formatting and usage with infinity

README.md

@@ -2665,11 +2665,11 @@ binary_docs_embeddings = quantize_embeddings(docs_embeddings, precision="ubinary
 
 similarities = cos_sim(query_embedding, docs_embeddings)
 print('similarities:', similarities)
-
+```
 
 ### Transformers
 
-
+```python
 from typing import Dict
 
 import torch
@@ -2717,18 +2717,19 @@ embeddings = pooling(outputs, inputs, 'cls')
 
 similarities = cos_sim(embeddings[0], embeddings[1:])
 print('similarities:', similarities)
-
+```
 
 ### Transformers.js
 
 If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using:
 
+```
 npm i @xenova/transformers
-
+```
 
 You can then use the model to compute embeddings like this:
 
-
+```javascript
 import { pipeline, cos_sim } from '@xenova/transformers';
 
 // Create a feature extraction pipeline
@@ -2750,13 +2751,13 @@ const output = await extractor(docs, { pooling: 'cls' });
 const [source_embeddings, ...document_embeddings ] = output.tolist();
 const similarities = document_embeddings.map(x => cos_sim(source_embeddings, x));
 console.log(similarities); // [0.7919578577247139, 0.6369278664248345, 0.16512018371357193, 0.3620778366720027]
-
+```
 
 ### Using API
 
 You can use the model via our API as follows:
 
-
+```python
 from mixedbread_ai.client import MixedbreadAI, EncodingFormat
 from sklearn.metrics.pairwise import cosine_similarity
 import os
@@ -2778,9 +2779,17 @@ res = mxbai.embeddings(
 
 encoded_embeddings = res.data[0].embedding
 print(res.dimensions, encoded_embeddings.ubinary, encoded_embeddings.float_, encoded_embeddings.int_8)
-
+```
 
 The API comes with native int8 and binary quantization support! Check out the [docs](https://mixedbread.ai/docs) for more information.
+
+### Infinity
+```bash
+docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \
+michaelf34/infinity:0.0.68 \
+v2 --model-id mixedbread-ai/mxbai-embed-large-v1 --revision "main" --dtype float16 --engine torch --port 7997
+```
+
 ## Evaluation
 As of March 2024, our model archives SOTA performance for Bert-large sized models on the [MTEB](https://huggingface.co/spaces/mteb/leaderboard). It ourperforms commercial models like OpenAIs text-embedding-3-large and matches the performance of model 20x it's size like the [echo-mistral-7b](https://huggingface.co/jspringer/echo-mistral-7b-instruct-lasttoken). Our model was trained with no overlap of the MTEB data, which indicates that our model generalizes well across several domains, tasks and text length. We know there are some limitations with this model, which will be fixed in v2.