Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# ---------------------------------------------------------
+# 1) Points to your Hugging Face repo and subfolder:
+#    "wuhp/myr1" is the repository
+#    "myr1" is the subfolder where the config/tokenizer/model are located.
+# ---------------------------------------------------------
+MODEL_REPO = "wuhp/myr1"
+SUBFOLDER = "myr1"
+
+# ---------------------------------------------------------
+# 2) Load the tokenizer and model from the Hub
+#    - trust_remote_code=True allows custom config & modeling files.
+# ---------------------------------------------------------
+tokenizer = AutoTokenizer.from_pretrained(
+    MODEL_REPO,
+    subfolder=SUBFOLDER,         # important because the model files sit inside 'myr1'
+    trust_remote_code=True
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_REPO,
+    subfolder=SUBFOLDER,         # also needed here
+    trust_remote_code=True,
+    device_map="auto",           # automatically place model layers on GPU(s) if available
+    torch_dtype=torch.float16,    # or "auto", "float32", "bfloat16", etc. as your hardware supports
+    low_cpu_mem_usage=True
+)
+
+# Put the model in evaluation mode
+model.eval()
+
+
+def generate_text(prompt, max_length=128, temperature=0.7, top_p=0.9):
+    """
+    Generate text from your DeepSeekR1 model, given an input prompt.
+    """
+    # Convert to token IDs and move to model device (GPU/CPU)
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+    # Generate output
+    with torch.no_grad():
+        output_ids = model.generate(
+            **inputs,
+            max_length=max_length,
+            temperature=temperature,
+            top_p=top_p,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id,  # or set to a real pad_token_id if your model uses one
+        )
+
+    # Decode the tokens back into a string
+    return tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+
+# ---------------------------------------------------------
+# 3) Build Gradio UI
+# ---------------------------------------------------------
+demo = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(
+            lines=5,
+            label="Enter your prompt",
+            placeholder="Type something for the DeepSeek model..."
+        ),
+        gr.Slider(64, 1024, step=1, value=128, label="Max Length"),
+        gr.Slider(0.0, 1.5, step=0.1, value=0.7, label="Temperature"),
+        gr.Slider(0.0, 1.0, step=0.05, value=0.9, label="Top-p"),
+    ],
+    outputs="text",
+    title="DeepSeek-R1 Gradio Demo",
+    description=(
+        "This Gradio interface loads the DeepSeek model from Hugging Face and lets you "
+        "generate text by entering a prompt. Adjust parameters to see how output changes."
+    )
+)
+
+if __name__ == "__main__":
+    demo.launch()