Added example script to use embedding model

examples/embedding_model.py

@@ -0,0 +1,43 @@
+# -----------------------------------------------------------------------------
+#
+# Copyright (c) 2024 Qualcomm Innovation Center, Inc. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# -----------------------------------------------------------------------------
+
+# This is the work example of the Embedding model with the AI 100
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer
+
+from QEfficient import QEFFAutoModel as AutoModel
+
+
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output  # First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+# Sentences we want sentence embeddings for
+sentences = "This is an example sentence"
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
+
+
+qeff_model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
+qeff_model.compile(num_cores=14)
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, return_tensors="pt")
+qeff_output = torch.tensor(qeff_model.generate(encoded_input))
+
+# Perform pooling
+sentence_embeddings = mean_pooling(qeff_output, encoded_input["attention_mask"])
+
+# Normalize embeddings
+sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+
+print("Sentence embeddings:")
+print(sentence_embeddings)