sglangv0.5.2 & support Qwen3-Next-80B-A3B-Instruct

benchmark/long_json_decode/build_dataset.py

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+import json
+
+import transformers
+import wikipedia
+
+name = "meta-llama/Llama-2-7b-chat-hf"
+t = transformers.AutoTokenizer.from_pretrained(name)
+city_names = ["los angles", "london", "tokyo", "beijing", "singapore"]
+
+
+for city_name in city_names:
+    content = str(wikipedia.page(city_name).content)
+    content = content.replace("\n\n", "\n")
+
+    tokens = t.encode(content)
+
+    truncate_len = int((10000 / len(tokens)) * len(content))
+    truncate_content = content[:truncate_len]
+    truncate_tokens = t.encode(truncate_content)
+
+    # Count token
+    print(
+        f"city_name: {city_name}, #tokens: {len(tokens)}, #truncate tokens: {len(truncate_tokens)}"
+    )
+
+    with open("questions.jsonl", "a") as fout:
+        fout.write(json.dumps({"document": truncate_content}) + "\n")