chore: import upstream snapshot with attribution

2026-07-13 13:39:21 +08:00
commit bc34f6df14
1149 changed files with 328099 additions and 0 deletions
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+import os
+from FlagEmbedding import FlagPseudoMoEModel
+
+
+def test_pseudo_moe_single_device():
+    model_name_or_path = "geevec-ai/geevec-embeddings-1.0-lite"
+
+    model = FlagPseudoMoEModel(
+        model_name_or_path,
+        query_instruction_for_retrieval="Given a question, retrieve passages that answer the question.",
+        query_instruction_format="Instruct: {}\nQuery: {}",
+        domain_for_pseudo_moe="coding",
+        use_fp16=False,
+        use_bf16=True,
+        trust_remote_code=True,
+        devices="cuda:0",  # if you don't have a GPU, you can use "cpu"
+        cache_dir=os.getenv("HF_HUB_CACHE", None),
+    )
+
+    queries = [
+        "how much protein should a female eat",
+        "summit define",
+    ] * 10
+    passages = [
+        "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day.",
+        "Definition of summit for English Language Learners: the highest point of a mountain; the highest level; a meeting between leaders.",
+    ] * 10
+
+    queries_embeddings = model.encode_queries(queries)
+    passages_embeddings = model.encode_corpus(passages)
+
+    cos_scores = queries_embeddings @ passages_embeddings.T
+    print(cos_scores[:2, :2])
+
+
+if __name__ == "__main__":
+    test_pseudo_moe_single_device()
+
+    print("--------------------------------")
+    print("Expected Output:")
+    print("[[0.700  0.246]\n [0.158 0.654]]")