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1591c33 | 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 | """MicroSupra-10k — inferência de demonstração (mesmos prompts do card original)."""
import os
from huggingface_hub import hf_hub_download
from transformers import LlamaForCausalLM, PreTrainedTokenizerFast
import torch
BASE = os.path.dirname(os.path.abspath(__file__))
OUT = f"{BASE}/out"
tok_path = hf_hub_download("SupraLabs/MicroSupra-1k", "tokenizer.json")
tokenizer = PreTrainedTokenizerFast(
tokenizer_file=tok_path,
bos_token="<s>", eos_token="</s>", pad_token="<pad>", unk_token="<unk>",
)
model = LlamaForCausalLM.from_pretrained(OUT)
model.eval()
print(f"[*] Parâmetros: {sum(p.numel() for p in model.parameters()):,}", flush=True)
prompts = [
"My name is ",
"The main concept of physics is ",
"Question: What is the capital of France?\nAnswer: ",
]
for prompt in prompts:
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
out = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
max_new_tokens=120,
do_sample=True,
temperature=0.35,
top_p=0.85,
repetition_penalty=1.2,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(f"\nPROMPT: {prompt!r}\nOUTPUT: {tokenizer.decode(out[0], skip_special_tokens=True)!r}", flush=True)
print("\n[*] DONE", flush=True) |