File size: 1,832 Bytes
555121c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
def test_model():
print("Loading Lbai-1-preview model...")
model_path = "."
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_path)
print("Model loaded successfully!\n")
# Test prompts - you can add more
test_prompts = [
"Diagnosis MRI Image-processing model result: Mild demented, confidence (%76.5), risk (%9.4) - interpret this output."
]
for i, prompt in enumerate(test_prompts, 1):
print(f"\n{'='*60}")
print(f"TEST {i}/{len(test_prompts)}")
print('='*60)
print(f"INPUT PROMPT:\n{prompt}\n")
print("Generating response...\n")
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
input_length = inputs['input_ids'].shape[1]
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=150,
temperature=0.7,
top_p=0.9,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
generated_text = tokenizer.decode(outputs[0][input_length:], skip_special_tokens=True)
print("-"*60)
print("FULL OUTPUT (Input + Generated):")
print("-"*60)
print(full_response)
print("\n" + "-"*60)
print("GENERATED TEXT ONLY (Model's response):")
print("-"*60)
print(generated_text)
print("="*60)
print("\n\nAll tests completed successfully!")
if __name__ == "__main__":
test_model()
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