tt

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+import torch
+
+# Load model and tokenizer with trust_remote_code=True
+model_id = "PowerInfer/SmallThinker-21BA3B-Instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(
+    model_id,
+    trust_remote_code=True  # Required for models with custom code
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="cpu",              # Run on CPU
+    torch_dtype=torch.float32,     # Use float32 on CPU
+    trust_remote_code=True         # Allow custom code execution
+)
+
+# Create text generation pipeline
+generator = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    device=-1  # CPU
+)
+
+# Define the chat function
+def chat(prompt, max_new_tokens=256, temperature=0.7):
+    output = generator(
+        prompt,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    return output[0]["generated_text"]
+
+# Launch the Gradio interface
+gr.Interface(
+    fn=chat,
+    inputs=[
+        gr.Textbox(label="Prompt", lines=4, placeholder="Ask anything..."),
+        gr.Slider(32, 512, value=256, step=16, label="Max New Tokens"),
+        gr.Slider(0.1, 1.5, value=0.7, step=0.1, label="Temperature")
+    ],
+    outputs=gr.Textbox(label="Response"),
+    title="💬 SmallThinker-21BA3B-Instruct",
+    description="Run PowerInfer/SmallThinker-21BA3B-Instruct locally on CPU using Hugging Face + Gradio"
+).launch()

requirements.txt

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+gradio>=4.44.0
+torch>=2.1.0
+transformers==4.53.3
+accelerate>=0.27.0
+sentencepiece  # required for some tokenizers
+safetensors    # faster & safer model loading
+hf_xet