Upload app.py with huggingface_hub

app.py

@@ -1,69 +1,112 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+import torch
 
+MODEL_NAME = "dinghface/olmo3-190m-zh-full-continue"
 
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+
+def generate(
+    prompt,
+    max_new_tokens,
     temperature,
     top_p,
-    hf_token: gr.OAuthToken,
+    top_k,
+    repetition_penalty,
+    do_sample,
 ):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+    output = pipe(
+        prompt,
+        max_new_tokens=int(max_new_tokens),
+        do_sample=do_sample,
+        temperature=temperature if do_sample else None,
+        top_p=top_p if do_sample else None,
+        top_k=int(top_k) if do_sample else None,
+        repetition_penalty=repetition_penalty,
+    )
+    return output[0]["generated_text"]
 
-    messages = [{"role": "system", "content": system_message}]
 
-    messages.extend(history)
+EXAMPLES = [
+    ["从前有座山，山里有座庙，", 256, 0.8, 0.9, 50, 1.2, True],
+    ["人工智能是", 256, 0.7, 0.9, 50, 1.2, True],
+    ["今天天气不错，我准备", 256, 0.8, 0.9, 50, 1.2, True],
+    ["Python 是一种", 256, 0.7, 0.9, 50, 1.2, True],
+    ["春天来了，万物复苏，", 256, 0.9, 0.95, 50, 1.1, True],
+    ["在很久很久以前，", 256, 0.85, 0.9, 40, 1.2, True],
+    ["The meaning of life is", 256, 0.8, 0.9, 50, 1.2, True],
+    ["deep learning is", 256, 0.7, 0.9, 50, 1.2, True],
+]
 
-    messages.append({"role": "user", "content": message})
+with gr.Blocks(title="OLMo3-190M-zh Continue Pretrain Demo") as demo:
+    gr.Markdown(
+        """
+        # OLMo3-190M-zh 持续预训练 Demo
+        基于 [OLMo3-190M-zh-full](https://huggingface.co/dinghface/olmo3-190m-zh-full) 进行持续预训练的 190M 参数中文模型。
+        输入一段文字，模型会自动续写。
+        """
+    )
 
-    response = ""
+    with gr.Row():
+        with gr.Column(scale=2):
+            prompt = gr.Textbox(
+                label="输入提示词",
+                placeholder="在这里输入文字，模型会继续往下写...",
+                lines=5,
+            )
+            output = gr.Textbox(label="生成结果", lines=10)
+            with gr.Row():
+                submit_btn = gr.Button("生成", variant="primary")
+                clear_btn = gr.Button("清空")
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
+        with gr.Column(scale=1):
+            do_sample = gr.Checkbox(label="启用采样（关闭则为贪心解码）", value=True)
+            max_new_tokens = gr.Slider(
+                minimum=16, maximum=1024, value=256, step=16, label="最大生成长度"
+            )
+            temperature = gr.Slider(
+                minimum=0.1, maximum=2.0, value=0.8, step=0.05, label="Temperature（温度）"
+            )
+            top_p = gr.Slider(
+                minimum=0.1, maximum=1.0, value=0.9, step=0.05, label="Top-p（核采样）"
+            )
+            top_k = gr.Slider(
+                minimum=1, maximum=200, value=50, step=1, label="Top-k"
+            )
+            repetition_penalty = gr.Slider(
+                minimum=1.0, maximum=2.0, value=1.2, step=0.05, label="重复惩罚"
+            )
 
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+    gr.Examples(
+        examples=EXAMPLES,
+        inputs=[prompt, max_new_tokens, temperature, top_p, top_k, repetition_penalty, do_sample],
+    )
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+    gr.Markdown(
+        """
+        ### 参数说明
+        - **Temperature**：越高越随机，越低越确定。1.0 为默认，<1 更保守，>1 更有创意
+        - **Top-p**：核采样，从累积概率达到该值的 token 中采样。1.0 不过滤
+        - **Top-k**：只从概率最高的 k 个 token 中采样。值越大选择越多
+        - **重复惩罚**：>1 时惩罚重复内容，避免循环输出
+        - **启用采样**：关闭后使用贪心解码（每次选概率最高的 token），输出确定但单一
+        """
+    )
 
+    submit_btn.click(
+        fn=generate,
+        inputs=[prompt, max_new_tokens, temperature, top_p, top_k, repetition_penalty, do_sample],
+        outputs=output,
+    )
+    clear_btn.click(fn=lambda: ("", ""), inputs=None, outputs=[prompt, output])
 
 if __name__ == "__main__":
     demo.launch()