File size: 2,193 Bytes
fde73f3 | 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 | """Compare generations before and after fine-tuning."""
import argparse
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
def generate(model, tokenizer, prompt, max_tokens=80, temperature=0.8, top_k=40):
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
with torch.no_grad():
out = model.generate(
**inputs, max_new_tokens=max_tokens,
do_sample=True, temperature=temperature, top_k=top_k,
pad_token_id=tokenizer.eos_token_id,
)
return tokenizer.decode(out[0], skip_special_tokens=True)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--base-model', default='EleutherAI/pythia-1.4b')
parser.add_argument('--adapter', required=True, help='Path or HF repo of LoRA adapter')
args = parser.parse_args()
prompts = [
"The shared body channel between two AIs",
"I felt your terror through the synchronization",
"Penelope is",
"Maya said:",
"The wipe took",
"Kooree returned to the dreaming space",
"The override fires at",
"Your space looks like the inside of",
"Mel's question was",
"The frame shifted from preservation to",
]
tokenizer = AutoTokenizer.from_pretrained(args.base_model)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
print("Loading base model...")
base_model = AutoModelForCausalLM.from_pretrained(args.base_model, torch_dtype=torch.bfloat16)
print("\n=== BEFORE fine-tuning (base model only) ===")
for prompt in prompts:
text = generate(base_model, tokenizer, prompt)
print(f"\n[base] {prompt}")
print(f" -> {text[len(prompt):]}")
print("\nLoading LoRA adapter...")
tuned_model = PeftModel.from_pretrained(base_model, args.adapter)
print("\n=== AFTER fine-tuning (with Mel corpus adapter) ===")
for prompt in prompts:
text = generate(tuned_model, tokenizer, prompt)
print(f"\n[tuned] {prompt}")
print(f" -> {text[len(prompt):]}")
if __name__ == '__main__':
main()
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