add serve

evaluation/evaluation.py

@@ -19,31 +19,40 @@ import numpy
 
 
 class CustomModel:
-    def __init__(self, model):
+    def __init__(self):
         self.session = requests.Session()
-        self.model = model
 
     
-    def get_simplexity_query2vec_results(self, sentences, embedding_url, model, template):
+    def get_embedding(self, sentece, prompt_name):
-        params = {}
+        embedding_url = "http://127.0.0.1:5010/embed"
-        params["model"] = model
-        params["template"] = template
         headers = {"accept": "application/json"}
+        headers["Content-Type"] = "application/json"
+
         data = {}
+        data["inputs"] = sentece
+        data["normalize"] = True
+        data["prompt_name"] = prompt_name
+        data["truncate"] = False
+        data["truncation_direction"] = "Right"
+
+        response = self.session.post(embedding_url, headers=headers, data=json.dumps(data), timeout=600)
+
+        return response.json()
+
+
+    def get_simplexity_query2vec_results(self, sentences, template):
         
         if len(sentences) < 2000:
             my_range = range
         else:
             my_range = tqdm.trange
         
-        batch_size = 1024
+        batch_size = 64
         vec = []
         for i in my_range(0, len(sentences), batch_size):
             start_idx = i
             stop_idx = min(i+batch_size, len(sentences))
-            data["queries"] = sentences[start_idx:stop_idx]
+            new_vec = self.get_embedding(sentences[start_idx:stop_idx], template)
-            response = self.session.post(embedding_url, headers=headers, params=params, data=json.dumps(data), timeout=600)
-            new_vec = response.json()
             vec += new_vec
         return vec
 
@@ -55,9 +64,6 @@ class CustomModel:
         prompt_type: PromptType | None = None,
         **kwargs,
     ) -> np.ndarray:
-
-        embedding_url = "http://127.0.0.1:5015/embedding"
-        
         if prompt_type == None:
             template = "document"
         elif prompt_type == PromptType.query:
@@ -74,7 +80,7 @@ class CustomModel:
             # embeddings = self.get_simplexity_query2vec_results(batch["text"], embedding_url, model, template)
 
             # all_embeddings += embeddings
-        all_embeddings = self.get_simplexity_query2vec_results(sentences, embedding_url, self.model, template)
+        all_embeddings = self.get_simplexity_query2vec_results(sentences, template)
 
 
         return numpy.array(all_embeddings)
@@ -88,12 +94,7 @@ def is_dataset_cached(dataset_name):
 
 
 def evaluate():
-    model_name = "Qwen3-Embedding-0.6B"
+    model = CustomModel()
-    # model_name = "KaLM-embedding-multilingual-mini-instruct-v2.5"
-    # model_name = "KaLM-Embedding-Gemma3-12B-2511"
-    # model_name = "llama-embed-nemotron-8b"
-    # model_name = "embeddinggemma-300m"
-    model = CustomModel(model_name)
 
     file_path = os.path.dirname(__file__)
     # model = mteb.get_model(model_name)
@@ -140,7 +141,6 @@ def evaluate():
     print("results = " + str(results))
 
 def main():
-    # get_results()
     evaluate()
 
 

serve/qwen/docker-compose.yaml

@@ -0,0 +1,27 @@
+version: "3.8"
+
+services:
+  query_2_vec_Qwen3-Embedding-0.6B:
+    image: ghcr.io/huggingface/text-embeddings-inference:1.8
+    container_name: query_2_vec_Qwen3-Embedding-0.6B
+    restart: unless-stopped
+    entrypoint: /bin/bash
+    command: |
+      -c "bash /app/start_vllm.sh"
+    shm_size: '45600m'
+    ports:
+      - "5010:8080"
+    volumes:
+      - .:/app
+        #- ./../../../../data:/app/data
+      - /home/hediehloo/code/embedding/embedding_model/train/qwen/output/v28-20251223-054407/merged-checkpoint-3707:/app/data/models/Qwen3-Embedding-0.6B/model
+      # - ./../../../logging_config.json:/app/logging_config.json
+      # - ./../../../src:/app/src
+      # - ./../../../logs:/app/logs
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - capabilities: [gpu]
+    stdin_open: true
+    tty: true

serve/qwen/start_vllm.sh

@@ -0,0 +1,20 @@
+export CUDA_VISIBLE_DEVICES=0
+export PYTHONPATH="/app"
+# export VLLM_LOGGING_CONFIG_PATH=/app/logging_config.json
+export TZ="Asia/Tehran"
+
+
+
+# mkdir -p /app/logs
+
+# sleep 200
+
+text-embeddings-router \
+  --model-id /app/data/models/Qwen3-Embedding-0.6B/model \
+  --port 8080 \
+  --dtype float16 \
+  --max-client-batch-size 1024 \
+  --max-concurrent-requests 1024 \
+  --max-batch-requests 1024 \
+  --max-batch-tokens 32768
+