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from transformers import AutoTokenizer, AutoModelForCausalLM
class SmolLM:
def __init__(self, model_path="HuggingFaceTB/SmolLM2-1.7B-Instruct"):
self.available = True
try:
print(f"[INFO] Loading model tokenizer from {model_path}")
self.tokenizer = AutoTokenizer.from_pretrained(model_path)
print(f"[INFO] Loading model from {model_path}")
self.model = AutoModelForCausalLM.from_pretrained(model_path)
print("[INFO] Model loaded successfully")
except Exception as e:
print(f"[ERROR] Failed to load model '{model_path}': {e}")
self.available = False
def predict(self, prompt):
if not self.available:
print("[WARN] LLama model unavailable, returning default weight 0.5")
return "0.5"
try:
print(f"[INFO] Generating response for prompt: {prompt[:100]}...", flush=True)
inputs = self.tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
outputs = self.model.generate(**inputs, max_length=150, num_return_sequences=1)
response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f"[INFO] Generated response: {response[:100]}...", flush=True)
return response
except Exception as e:
print(f"[ERROR] LLama model inference failed: {e}")
return "0.5"
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