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app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, pipeline
+
+model_name = "dinghface/olmo3-190m-zh-sft"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# 收集结束标记 id
+eos_token_ids = []
+if tokenizer.eos_token_id is not None:
+    eos_token_ids.append(tokenizer.eos_token_id)
+im_end_id = tokenizer.convert_tokens_to_ids("<|im_end|>")
+if im_end_id is not None and im_end_id != tokenizer.unk_token_id:
+    eos_token_ids.append(im_end_id)
+
+pipe = pipeline(
+    "text-generation",
+    model=model_name,
+    tokenizer=tokenizer,
+    model_kwargs={"torch_dtype": torch.bfloat16, "attn_implementation": "sdpa"},
+)
+
+
+def predict(message):
+    messages = [{"role": "user", "content": message}]
+    output = pipe(
+        messages,
+        max_new_tokens=256,
+        do_sample=True,
+        temperature=0.7,
+        top_k=50,
+        top_p=0.9,
+        repetition_penalty=1.2,
+        return_full_text=False,
+        eos_token_id=eos_token_ids,  # 关键：告诉 pipeline 遇到这些 token 就停
+    )
+    return output[0]["generated_text"]
+
+
+gr.Interface(fn=predict, inputs="text", outputs="text").launch(ssr_mode=False)