Update README.md

README.md

@@ -1,3 +1,45 @@
 ---
 license: mit
+language:
+- en
 ---
+
+# bge-micro-v2-quant
+
+This is the quantized (INT8) ONNX variant of the [bge-micro-v2](https://huggingface.co/TaylorAI/bge-micro-v2) embeddings model created with [DeepSparse Optimum](https://github.com/neuralmagic/optimum-deepsparse) for ONNX export/inference and Neural Magic's [Sparsify](https://github.com/neuralmagic/sparsify) for one-shot quantization.
+
+Current list of sparse and quantized bge ONNX models:
+
+| Links                                                                                               | Sparsification Method |
+| --------------------------------------------------------------------------------------------------- | ---------------------- |
+| [zeroshot/bge-large-en-v1.5-sparse](https://huggingface.co/zeroshot/bge-large-en-v1.5-sparse)     |    Quantization (INT8) & 50% Pruning                    |
+| [zeroshot/bge-large-en-v1.5-quant](https://huggingface.co/zeroshot/bge-large-en-v1.5-quant)     |   Quantization (INT8)                     |
+
+```bash
+pip install -U deepsparse-nightly[sentence_transformers]
+```
+
+```python
+from deepsparse.sentence_transformers import SentenceTransformer
+model = SentenceTransformer('zeroshot/bge-small-en-v1.5-quant', export=False)
+
+# Our sentences we like to encode
+sentences = ['This framework generates embeddings for each input sentence',
+    'Sentences are passed as a list of string.',
+    'The quick brown fox jumps over the lazy dog.']
+
+# Sentences are encoded by calling model.encode()
+embeddings = model.encode(sentences)
+
+# Print the embeddings
+for sentence, embedding in zip(sentences, embeddings):
+    print("Sentence:", sentence)
+    print("Embedding:", embedding.shape)
+    print("")
+```
+
+For further details regarding DeepSparse & Sentence Transformers integration, refer to the [DeepSparse README](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/sentence_transformers).
+
+For general questions on these models and sparsification methods, reach out to the engineering team on our [community Slack](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ).
+
+![;)](https://media.giphy.com/media/bYg33GbNbNIVzSrr84/giphy-downsized-large.gif)