chore: import upstream snapshot with attribution

examples/deploy/bert/client.py

@@ -0,0 +1,29 @@
+from typing import List
+
+from swift.infer_engine import InferClient, InferRequest
+
+
+def infer_batch(engine: InferClient, infer_requests: List[InferRequest]):
+    resp_list = engine.infer(infer_requests)
+    query0 = infer_requests[0].messages[0]['content']
+    query1 = infer_requests[1].messages[0]['content']
+    print(f'query0: {query0}')
+    print(f'response0: {resp_list[0].choices[0].message.content}')
+    print(f'query1: {query1}')
+    print(f'response1: {resp_list[1].choices[0].message.content}')
+
+
+if __name__ == '__main__':
+    engine = InferClient(host='127.0.0.1', port=8000)
+    models = engine.models
+    print(f'models: {models}')
+    infer_batch(engine, [
+        InferRequest(messages=[{
+            'role': 'user',
+            'content': '今天天气真好呀'
+        }]),
+        InferRequest(messages=[{
+            'role': 'user',
+            'content': '真倒霉'
+        }])
+    ])

examples/deploy/bert/server.sh

@@ -0,0 +1,10 @@
+# Since `swift/test_lora` is trained by swift and contains an `args.json` file,
+# there is no need to explicitly set `--model`, `--system`, etc., as they will be automatically read.
+CUDA_VISIBLE_DEVICES=0 swift deploy \
+    --host 0.0.0.0 \
+    --port 8000 \
+    --adapters swift/test_bert \
+    --served_model_name bert-base-chinese \
+    --infer_backend transformers \
+    --truncation_strategy right \
+    --max_length 512