初始化项目，由ModelHub XC社区提供模型

Model: UUFO-Aigis/PicoLAiNN-100M-SmallDataset
Source: Original Platform

Inferencecode.py

@@ -0,0 +1,35 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+MODEL_NAME = "UUFO-Aigis/Pico-OpenLAiNN-100M" #Replace 100M with 250M or 500M if you prefer those models.
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+
+def generate_text(prompt, model, tokenizer, max_length=512, temperature=1, top_k=50, top_p=0.95):
+    inputs = tokenizer.encode(prompt, return_tensors="pt")
+
+    outputs = model.generate(
+        inputs,
+        max_length=max_length,
+        temperature=temperature,
+        top_k=top_k,
+        top_p=top_p,
+        do_sample=True
+    )
+
+
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_text
+
+def main():
+    # Define your prompt
+    prompt = "According to all known laws of aviation, there is no way a bee should be able to fly."
+
+    generated_text = generate_text(prompt, model, tokenizer)
+
+    print(generated_text)
+
+if __name__ == "__main__":
+    main()