Hugging Face's logo

open-source-dimas
/
modelo-jogo-velha-zero

Text Generation
Portuguese
English
code-generation
from-scratch
Model card Files
xet
 Community
open-source-dimas commited on
Commit
9cb4eda
·
verified ·
1 Parent(s): 92c5bfe

Publicar modelo de jogo da velha treinado do zero

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Files changed (9) hide show
  1. README.md +30 -0
  2. VALIDACAO_JOGO_GERADO.md +5 -0
  3. conversar_zero.py +54 -0
  4. jogo_gerado_pelo_modelo.html +1 -0
  5. modelo_jogo_zero_publicado_v2.pt +3 -0
  6. requirements_zero.txt +2 -0
  7. tokenizer.json +4459 -0
  8. treinar_gpt_zero.py +252 -0
  9. usar_gpt_zero.py +62 -0
README.md ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: mit
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+ language:
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+ - pt
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+ - en
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+ tags:
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+ - text-generation
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+ - code-generation
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+ - from-scratch
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+ ---
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+
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+ # Modelo de jogo da velha treinado do zero
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+
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+ Este é um pequeno modelo causal treinado a partir de pesos aleatórios para responder a pedidos de geração de código web, com foco em um jogo simples da velha. Ele não usa Qwen, Llama, GPT ou qualquer peso pré-treinado. O tokenizer também foi treinado localmente.
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+
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+ ## Uso
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+
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+ ```bash
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+ python3 usar_gpt_zero.py --checkpoint modelo_jogo_zero_publicado_v2.pt --system "Você é um assistente local útil e educado." --pergunta "Crie um jogo simples da velha em HTML, CSS e JavaScript." --device cpu
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+ ```
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+
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+ O arquivo `jogo_gerado_pelo_modelo.html` é uma amostra gerada pelo checkpoint. A validação registra que o HTML abriu no navegador, alternou X e O e detectou uma vitória.
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+
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+ ## Dados
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+
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+ O treinamento combinou diálogos curados deste projeto com um subconjunto filtrado do dataset `cfahlgren1/react-code-instructions`, publicado sob licença MIT: https://huggingface.co/datasets/cfahlgren1/react-code-instructions. O subconjunto foi usado para ampliar exemplos de aplicações web; os exemplos específicos da velha foram escritos para este projeto.
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+
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+ ## Limitações
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+
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+ Este é um modelo educacional pequeno, com aproximadamente 751 mil parâmetros. Ele não é um assistente geral e pode produzir código incompleto para pedidos fora dos padrões aprendidos.
VALIDACAO_JOGO_GERADO.md ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
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+ # Validação do jogo gerado pelo modelo
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+
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+ O checkpoint `modelo_jogo_zero_publicado_v2.pt` gerou `jogo_gerado_pelo_modelo.html`. A página abriu no navegador com nove botões, mensagem inicial `Vez de X` e o tabuleiro visível. O primeiro clique colocou `X` na primeira casa e mudou o status para `Vez de O`, confirmando que o JavaScript foi executado.
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+ Após o primeiro clique, o segundo clique colocou `O` e o terceiro clique colocou `X`; o status alternou corretamente entre os jogadores. O HTML gerado pelo modelo passou na checagem de sintaxe JavaScript com `node --check`.
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+ Na sequência de teste, X ocupou as casas 1, 4 e 7; o jogo exibiu `X venceu!`. A detecção de vitória funciona no HTML gerado pelo modelo.
conversar_zero.py ADDED
@@ -0,0 +1,54 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ #!/usr/bin/env python3
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+ """Chat interativo usando exclusivamente pesos treinados do zero."""
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+
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+ from __future__ import annotations
5
+
6
+ import argparse
7
+ from pathlib import Path
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+
9
+ import torch
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+
11
+ from usar_gpt_zero import carregar
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+ from treinar_gpt_zero import gerar
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+
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+
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+ SYSTEM_DEFAULT = "Você é um assistente local útil e educado."
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+
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+
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+ def main() -> None:
19
+ parser = argparse.ArgumentParser(description="Conversa com o GPT treinado do zero.")
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+ parser.add_argument("--checkpoint", type=Path, default=Path("modelo_dialogos_multi_v2.pt"))
21
+ parser.add_argument("--system", default=SYSTEM_DEFAULT)
22
+ parser.add_argument("--tokens", type=int, default=80)
23
+ parser.add_argument("--temperature", type=float, default=0.05)
24
+ parser.add_argument("--top-k", type=int, default=20)
25
+ parser.add_argument("--top-p", type=float, default=0.9)
26
+ parser.add_argument("--repetition-penalty", type=float, default=1.15)
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+ parser.add_argument("--device", choices=["auto", "cpu", "cuda"], default="auto")
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+ args = parser.parse_args()
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+
30
+ if args.device == "auto":
31
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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+ else:
33
+ device = torch.device(args.device)
34
+ modelo, tokenizer, checkpoint, bos_id, eos_id = carregar(args.checkpoint, device)
35
+ print("Modelo treinado do zero pronto. Digite /sair para terminar.")
36
+ historico: list[tuple[str, str]] = []
37
+ while True:
38
+ pergunta = input("Você: ").strip()
39
+ if pergunta == "/sair":
40
+ break
41
+ historico.append(("user", pergunta))
42
+ partes = [f"<|system|>{args.system}<|end|>"]
43
+ for indice, (papel, conteudo) in enumerate(historico[-4:]):
44
+ partes.append(f"<|{papel}|>{conteudo}<|end|>")
45
+ if indice == len(historico[-4:]) - 1 and papel == "user":
46
+ partes.append("<|assistant|>")
47
+ prompt = "".join(partes)
48
+ resposta = gerar(modelo, tokenizer, prompt, device, args.tokens, args.temperature, args.top_k, args.top_p, bos_id, eos_id, args.repetition_penalty)
49
+ historico.append(("assistant", resposta))
50
+ print(f"Modelo: {resposta}\n")
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+
52
+
53
+ if __name__ == "__main__":
54
+ main()
jogo_gerado_pelo_modelo.html ADDED
@@ -0,0 +1 @@
 
 
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+ <!doctype html><style>button{width:60px;height:60px;font-size:30px}</style><div id="g"></div><p id="m">Vez de X</p><script>let b=Array(9).fill(''),p='X',g=document.querySelector('#g'),m=document.querySelector('#m');function d(){g.innerHTML=b.map((x,i)=>`<button onclick="j(${i})">${x}</button>`).join('')}function j(i){if(b[i])return;b[i]=p;d();let w=[[0,1,2],[3,4,5],[6,7,8],[0,3,6],[1,4,7],[2,5,8],[0,4,8],[2,4,6]].some(a=>a.every(k=>b[k]==p));if(w)m.textContent=p+' venceu!';else if(b.every(x=>x))m.textContent='Empate!';else p=p=='X'?'O':'X',m.textContent='Vez de '+p}d();</script>
modelo_jogo_zero_publicado_v2.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:c51a512c3d18ef83d28c5e3beaa98085b3293b8658694f216d7bdfd6e440b781
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+ size 3019103
requirements_zero.txt ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ torch
2
+ tokenizers
tokenizer.json ADDED
@@ -0,0 +1,4459 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 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+ [
4150
+ "Ġp",
4151
+ "l"
4152
+ ],
4153
+ [
4154
+ "Ġb",
4155
+ "g"
4156
+ ],
4157
+ [
4158
+ "Y",
4159
+ "ou"
4160
+ ],
4161
+ [
4162
+ "Ġcol",
4163
+ "or"
4164
+ ],
4165
+ [
4166
+ "c",
4167
+ "y"
4168
+ ],
4169
+ [
4170
+ "vic",
4171
+ "es"
4172
+ ],
4173
+ [
4174
+ "t",
4175
+ "om"
4176
+ ],
4177
+ [
4178
+ "Ġ",
4179
+ "00"
4180
+ ],
4181
+ [
4182
+ "Ġvisual",
4183
+ "ly"
4184
+ ],
4185
+ [
4186
+ ")",
4187
+ "{"
4188
+ ],
4189
+ [
4190
+ "us",
4191
+ "tify"
4192
+ ],
4193
+ [
4194
+ "Ġex",
4195
+ "per"
4196
+ ],
4197
+ [
4198
+ "Ġin",
4199
+ "tera"
4200
+ ],
4201
+ [
4202
+ "E",
4203
+ "ff"
4204
+ ],
4205
+ [
4206
+ "pp",
4207
+ "ort"
4208
+ ],
4209
+ [
4210
+ "Ġap",
4211
+ "ar"
4212
+ ],
4213
+ [
4214
+ "col",
4215
+ "s"
4216
+ ],
4217
+ [
4218
+ "Ġm",
4219
+ "ar"
4220
+ ],
4221
+ [
4222
+ "f",
4223
+ "o"
4224
+ ],
4225
+ [
4226
+ "Ġt",
4227
+ "ag"
4228
+ ],
4229
+ [
4230
+ "ul",
4231
+ "d"
4232
+ ],
4233
+ [
4234
+ "Ġo",
4235
+ "s"
4236
+ ],
4237
+ [
4238
+ "Eff",
4239
+ "ect"
4240
+ ],
4241
+ [
4242
+ "p",
4243
+ "re"
4244
+ ],
4245
+ [
4246
+ "c",
4247
+ "tive"
4248
+ ],
4249
+ [
4250
+ "v",
4251
+ "ent"
4252
+ ],
4253
+ [
4254
+ "Ġ",
4255
+ "|"
4256
+ ],
4257
+ [
4258
+ "iz",
4259
+ "es"
4260
+ ],
4261
+ [
4262
+ "ĠاÙĦ",
4263
+ "Ùħ"
4264
+ ],
4265
+ [
4266
+ "ave",
4267
+ "g"
4268
+ ],
4269
+ [
4270
+ "ÙĬ",
4271
+ "Ø©"
4272
+ ],
4273
+ [
4274
+ "aveg",
4275
+ "ador"
4276
+ ],
4277
+ [
4278
+ "Ġ`",
4279
+ "<"
4280
+ ],
4281
+ [
4282
+ "Ġcle",
4283
+ "an"
4284
+ ],
4285
+ [
4286
+ "á",
4287
+ "gra"
4288
+ ],
4289
+ [
4290
+ "ĠtÃŃtul",
4291
+ "o"
4292
+ ],
4293
+ [
4294
+ "ti",
4295
+ "v"
4296
+ ],
4297
+ [
4298
+ "o",
4299
+ "uld"
4300
+ ],
4301
+ [
4302
+ "a",
4303
+ "ke"
4304
+ ],
4305
+ [
4306
+ "ar",
4307
+ "t"
4308
+ ],
4309
+ [
4310
+ "6",
4311
+ "4"
4312
+ ],
4313
+ [
4314
+ "ess",
4315
+ "ages"
4316
+ ],
4317
+ [
4318
+ "re",
4319
+ "d"
4320
+ ],
4321
+ [
4322
+ "a",
4323
+ "ult"
4324
+ ],
4325
+ [
4326
+ "Ġuse",
4327
+ "Effect"
4328
+ ],
4329
+ [
4330
+ "Ġan",
4331
+ "y"
4332
+ ],
4333
+ [
4334
+ "ab",
4335
+ "ility"
4336
+ ],
4337
+ [
4338
+ "Ġe",
4339
+ "ff"
4340
+ ],
4341
+ [
4342
+ "Ġ",
4343
+ "H"
4344
+ ],
4345
+ [
4346
+ "h",
4347
+ "is"
4348
+ ],
4349
+ [
4350
+ "Ġas",
4351
+ "s"
4352
+ ],
4353
+ [
4354
+ "Ġsty",
4355
+ "ling"
4356
+ ],
4357
+ [
4358
+ "d",
4359
+ "al"
4360
+ ],
4361
+ [
4362
+ "Ġin",
4363
+ "cl"
4364
+ ],
4365
+ [
4366
+ "Ġ=",
4367
+ "=="
4368
+ ],
4369
+ [
4370
+ "im",
4371
+ "ations"
4372
+ ],
4373
+ [
4374
+ "Ġ",
4375
+ "O"
4376
+ ],
4377
+ [
4378
+ "s",
4379
+ "m"
4380
+ ],
4381
+ [
4382
+ ".",
4383
+ "</"
4384
+ ],
4385
+ [
4386
+ "s",
4387
+ "te"
4388
+ ],
4389
+ [
4390
+ "g",
4391
+ "ress"
4392
+ ],
4393
+ [
4394
+ "p",
4395
+ "ath"
4396
+ ],
4397
+ [
4398
+ "Ġw",
4399
+ "ill"
4400
+ ],
4401
+ [
4402
+ "]",
4403
+ ");"
4404
+ ],
4405
+ [
4406
+ "uc",
4407
+ "cess"
4408
+ ],
4409
+ [
4410
+ "h",
4411
+ "es"
4412
+ ],
4413
+ [
4414
+ "k",
4415
+ "ing"
4416
+ ],
4417
+ [
4418
+ "Ġs",
4419
+ "izes"
4420
+ ],
4421
+ [
4422
+ "ĠT",
4423
+ "his"
4424
+ ],
4425
+ [
4426
+ "Ġc",
4427
+ "ri"
4428
+ ],
4429
+ [
4430
+ "gra",
4431
+ "ph"
4432
+ ],
4433
+ [
4434
+ "Ġfunctional",
4435
+ "ity"
4436
+ ],
4437
+ [
4438
+ "Ġinter",
4439
+ "f"
4440
+ ],
4441
+ [
4442
+ "w",
4443
+ "e"
4444
+ ],
4445
+ [
4446
+ "Ġpro",
4447
+ "j"
4448
+ ],
4449
+ [
4450
+ "hes",
4451
+ "ive"
4452
+ ],
4453
+ [
4454
+ "ar",
4455
+ "e"
4456
+ ]
4457
+ ]
4458
+ }
4459
+ }
treinar_gpt_zero.py ADDED
@@ -0,0 +1,252 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ """Treina um pequeno GPT causal do zero com tokenizer BPE local."""
3
+
4
+ from __future__ import annotations
5
+
6
+ import argparse
7
+ import json
8
+ import math
9
+ import random
10
+ from pathlib import Path
11
+
12
+ import torch
13
+ from tokenizers import Tokenizer
14
+ from torch import nn
15
+ from torch.nn import functional as F
16
+
17
+
18
+ class GPTZero(nn.Module):
19
+ def __init__(self, vocab_size: int, block_size: int, d_model: int, nhead: int, num_layers: int, dropout: float):
20
+ super().__init__()
21
+ self.block_size = block_size
22
+ self.token_embedding = nn.Embedding(vocab_size, d_model)
23
+ self.position_embedding = nn.Embedding(block_size, d_model)
24
+ camada = nn.TransformerEncoderLayer(
25
+ d_model=d_model,
26
+ nhead=nhead,
27
+ dim_feedforward=4 * d_model,
28
+ dropout=dropout,
29
+ activation="gelu",
30
+ batch_first=True,
31
+ norm_first=True,
32
+ )
33
+ self.transformer = nn.TransformerEncoder(camada, num_layers=num_layers)
34
+ self.norm = nn.LayerNorm(d_model)
35
+ self.lm_head = nn.Linear(d_model, vocab_size, bias=False)
36
+ self.lm_head.weight = self.token_embedding.weight
37
+ self.apply(self._init_weights)
38
+
39
+ @staticmethod
40
+ def _init_weights(modulo):
41
+ if isinstance(modulo, (nn.Linear, nn.Embedding)):
42
+ nn.init.normal_(modulo.weight, mean=0.0, std=0.02)
43
+ if isinstance(modulo, nn.Linear) and modulo.bias is not None:
44
+ nn.init.zeros_(modulo.bias)
45
+ elif isinstance(modulo, nn.LayerNorm):
46
+ nn.init.ones_(modulo.weight)
47
+ nn.init.zeros_(modulo.bias)
48
+
49
+ def forward(self, indices: torch.Tensor, alvos: torch.Tensor | None = None):
50
+ _, comprimento = indices.shape
51
+ if comprimento > self.block_size:
52
+ raise ValueError("A sequência excede o contexto do modelo.")
53
+ posicoes = torch.arange(comprimento, device=indices.device)
54
+ x = self.token_embedding(indices) + self.position_embedding(posicoes)[None, :, :]
55
+ mascara = torch.triu(
56
+ torch.ones(comprimento, comprimento, device=indices.device, dtype=torch.bool),
57
+ diagonal=1,
58
+ )
59
+ x = self.transformer(x, mask=mascara)
60
+ logits = self.lm_head(self.norm(x))
61
+ perda = None
62
+ if alvos is not None:
63
+ perda = F.cross_entropy(logits.reshape(-1, logits.size(-1)), alvos.reshape(-1))
64
+ return logits, perda
65
+
66
+
67
+ def carregar_textos(path: Path) -> list[str]:
68
+ textos = []
69
+ with path.open(encoding="utf-8") as arquivo:
70
+ for linha in arquivo:
71
+ if linha.strip():
72
+ registro = json.loads(linha)
73
+ if isinstance(registro.get("text"), str):
74
+ textos.append(registro["text"])
75
+ return textos
76
+
77
+
78
+ def ids_textos(textos: list[str], tokenizer: Tokenizer, bos_id: int, eos_id: int) -> list[torch.Tensor]:
79
+ return [torch.tensor([bos_id] + tokenizer.encode(texto).ids + [eos_id], dtype=torch.long) for texto in textos]
80
+
81
+
82
+ def amostrar_lote(streams: list[torch.Tensor], block_size: int, batch_size: int, device: torch.device):
83
+ possiveis = [stream for stream in streams if len(stream) > block_size]
84
+ if not possiveis:
85
+ raise ValueError("Nenhum exemplo é maior que o contexto escolhido.")
86
+ xs, ys = [], []
87
+ for _ in range(batch_size):
88
+ stream = random.choice(possiveis)
89
+ inicio = random.randint(0, len(stream) - block_size - 1)
90
+ janela = stream[inicio : inicio + block_size + 1]
91
+ xs.append(janela[:-1])
92
+ ys.append(janela[1:])
93
+ return torch.stack(xs).to(device), torch.stack(ys).to(device)
94
+
95
+
96
+ @torch.no_grad()
97
+ def avaliar(modelo, streams, block_size, batch_size, batches, device):
98
+ modelo.eval()
99
+ perdas = []
100
+ for _ in range(batches):
101
+ xb, yb = amostrar_lote(streams, block_size, batch_size, device)
102
+ _, perda = modelo(xb, yb)
103
+ perdas.append(float(perda))
104
+ modelo.train()
105
+ return sum(perdas) / len(perdas)
106
+
107
+
108
+ def selecionar(logits: torch.Tensor, temperature: float, top_k: int, top_p: float) -> torch.Tensor:
109
+ logits = logits / max(temperature, 1e-5)
110
+ if top_k > 0:
111
+ valores, indices = torch.topk(logits, min(top_k, logits.size(-1)))
112
+ filtrados = torch.full_like(logits, -float("inf"))
113
+ filtrados.scatter_(1, indices, valores)
114
+ logits = filtrados
115
+ if 0 < top_p < 1:
116
+ valores, indices = torch.sort(logits, descending=True)
117
+ probabilidades = F.softmax(valores, dim=-1)
118
+ acumuladas = torch.cumsum(probabilidades, dim=-1)
119
+ remover = acumuladas > top_p
120
+ remover[:, 1:] = remover[:, :-1].clone()
121
+ remover[:, 0] = False
122
+ valores[remover] = -float("inf")
123
+ filtrados = torch.full_like(logits, -float("inf"))
124
+ filtrados.scatter_(1, indices, valores)
125
+ logits = filtrados
126
+ return torch.multinomial(F.softmax(logits, dim=-1), num_samples=1)
127
+
128
+
129
+ @torch.no_grad()
130
+ def gerar(modelo, tokenizer, prompt: str, device: torch.device, max_new_tokens: int, temperature: float, top_k: int, top_p: float, bos_id: int, eos_id: int, repetition_penalty: float = 1.0) -> str:
131
+ ids = [bos_id] + tokenizer.encode(prompt).ids
132
+ contexto = torch.tensor([ids], dtype=torch.long, device=device)
133
+ fim_id = tokenizer.token_to_id("<|end|>")
134
+ modelo.eval()
135
+ for _ in range(max_new_tokens):
136
+ entrada = contexto[:, -modelo.block_size :]
137
+ logits, _ = modelo(entrada)
138
+ logits = logits[:, -1, :]
139
+ if repetition_penalty > 1.0:
140
+ vistos = torch.unique(contexto[0])
141
+ logits[:, vistos] = torch.where(logits[:, vistos] < 0, logits[:, vistos] * repetition_penalty, logits[:, vistos] / repetition_penalty)
142
+ proximo = selecionar(logits, temperature, top_k, top_p)
143
+ contexto = torch.cat([contexto, proximo], dim=1)
144
+ token_id = int(proximo.item())
145
+ if token_id == eos_id or (fim_id is not None and token_id == fim_id):
146
+ break
147
+ return tokenizer.decode(contexto[0].tolist()[len(ids):], skip_special_tokens=True)
148
+
149
+
150
+ def main() -> None:
151
+ parser = argparse.ArgumentParser()
152
+ parser.add_argument("--data-dir", type=Path, required=True)
153
+ parser.add_argument("--tokenizer", type=Path, required=True)
154
+ parser.add_argument("--steps", type=int, default=3000)
155
+ parser.add_argument("--batch-size", type=int, default=8)
156
+ parser.add_argument("--block-size", type=int, default=256)
157
+ parser.add_argument("--d-model", type=int, default=192)
158
+ parser.add_argument("--nhead", type=int, default=6)
159
+ parser.add_argument("--num-layers", type=int, default=4)
160
+ parser.add_argument("--dropout", type=float, default=0.1)
161
+ parser.add_argument("--lr", type=float, default=3e-4)
162
+ parser.add_argument("--eval-interval", type=int, default=250)
163
+ parser.add_argument("--eval-batches", type=int, default=10)
164
+ parser.add_argument("--seed", type=int, default=42)
165
+ parser.add_argument("--checkpoint", type=Path, default=Path("modelo_gpt_zero.pt"))
166
+ parser.add_argument("--resume", type=Path, default=None, help="checkpoint do próprio GPT do zero para continuar")
167
+ parser.add_argument("--device", choices=["auto", "cpu", "cuda"], default="auto")
168
+ args = parser.parse_args()
169
+
170
+ random.seed(args.seed)
171
+ torch.manual_seed(args.seed)
172
+ if torch.cuda.is_available():
173
+ torch.cuda.manual_seed_all(args.seed)
174
+ if args.device == "auto":
175
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
176
+ else:
177
+ device = torch.device(args.device)
178
+ if device.type == "cuda" and not torch.cuda.is_available():
179
+ raise RuntimeError("CUDA foi solicitado, mas não está disponível.")
180
+
181
+ tokenizer = Tokenizer.from_file(str(args.tokenizer))
182
+ bos_id = tokenizer.token_to_id("<bos>")
183
+ eos_id = tokenizer.token_to_id("<eos>")
184
+ if bos_id is None or eos_id is None:
185
+ raise ValueError("Tokenizer sem <bos> ou <eos>.")
186
+ train_textos = carregar_textos(args.data_dir / "train_conversacional.jsonl")
187
+ val_textos = carregar_textos(args.data_dir / "validation_conversacional.jsonl")
188
+ test_textos = carregar_textos(args.data_dir / "test_conversacional.jsonl")
189
+ train_streams = ids_textos(train_textos, tokenizer, bos_id, eos_id)
190
+ val_streams = ids_textos(val_textos, tokenizer, bos_id, eos_id)
191
+ test_streams = ids_textos(test_textos, tokenizer, bos_id, eos_id)
192
+ # O corpus é concatenado para permitir janelas maiores que um exemplo isolado.
193
+ # Os marcadores <bos>/<eos> preservam as fronteiras entre diálogos.
194
+ train_streams = [torch.cat(train_streams)]
195
+ val_streams = [torch.cat(val_streams)]
196
+ test_streams = [torch.cat(test_streams)]
197
+
198
+ modelo = GPTZero(tokenizer.get_vocab_size(), args.block_size, args.d_model, args.nhead, args.num_layers, args.dropout).to(device)
199
+ if args.resume is not None:
200
+ estado = torch.load(args.resume, map_location=device, weights_only=False)
201
+ if estado.get("pretrained", True):
202
+ raise ValueError("O checkpoint indicado não está marcado como treinado do zero.")
203
+ modelo.load_state_dict(estado["model_state"])
204
+ print(f"Checkpoint do zero retomado: {args.resume}")
205
+ otimizador = torch.optim.AdamW(modelo.parameters(), lr=args.lr, betas=(0.9, 0.95), weight_decay=0.1)
206
+ total_parametros = sum(param.numel() for param in modelo.parameters())
207
+ melhor_validacao = math.inf
208
+
209
+ print(f"Dispositivo: {device}")
210
+ print(f"Pesos inicializados aleatoriamente; nenhum modelo pré-treinado é carregado.")
211
+ print(f"Vocabulário: {tokenizer.get_vocab_size()} tokens")
212
+ print(f"Parâmetros: {total_parametros:,}")
213
+ print(f"Tokens: treino={sum(map(len, train_streams))}, validação={sum(map(len, val_streams))}, teste={sum(map(len, test_streams))}")
214
+
215
+ for passo in range(1, args.steps + 1):
216
+ xb, yb = amostrar_lote(train_streams, args.block_size, args.batch_size, device)
217
+ _, perda = modelo(xb, yb)
218
+ otimizador.zero_grad(set_to_none=True)
219
+ perda.backward()
220
+ torch.nn.utils.clip_grad_norm_(modelo.parameters(), 1.0)
221
+ otimizador.step()
222
+ if passo == 1 or passo % args.eval_interval == 0 or passo == args.steps:
223
+ treino = avaliar(modelo, train_streams, args.block_size, args.batch_size, args.eval_batches, device)
224
+ validacao = avaliar(modelo, val_streams, args.block_size, args.batch_size, args.eval_batches, device)
225
+ print(f"passo={passo:06d} treino={treino:.4f} validação={validacao:.4f}", flush=True)
226
+ if validacao < melhor_validacao:
227
+ melhor_validacao = validacao
228
+ torch.save(
229
+ {
230
+ "model_state": modelo.state_dict(),
231
+ "vocab_size": tokenizer.get_vocab_size(),
232
+ "block_size": args.block_size,
233
+ "d_model": args.d_model,
234
+ "nhead": args.nhead,
235
+ "num_layers": args.num_layers,
236
+ "dropout": args.dropout,
237
+ "tokenizer": str(args.tokenizer),
238
+ "validacao": validacao,
239
+ "seed": args.seed,
240
+ "pretrained": False,
241
+ },
242
+ args.checkpoint,
243
+ )
244
+
245
+ print(f"Checkpoint do zero salvo em: {args.checkpoint}")
246
+ prompt = "<|system|>Você é um assistente útil.<|end|><|user|>Olá! Quem é você?<|end|><|assistant|>"
247
+ print("Amostra:")
248
+ print(gerar(modelo, tokenizer, prompt, device, 120, 0.7, 40, 0.9, bos_id, eos_id))
249
+
250
+
251
+ if __name__ == "__main__":
252
+ main()
usar_gpt_zero.py ADDED
@@ -0,0 +1,62 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ """Gera respostas usando exclusivamente o GPT treinado do zero."""
3
+
4
+ from __future__ import annotations
5
+
6
+ import argparse
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+ from pathlib import Path
8
+
9
+ import torch
10
+ from tokenizers import Tokenizer
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+
12
+ from treinar_gpt_zero import GPTZero, gerar
13
+
14
+
15
+ def carregar(checkpoint_path: Path, device: torch.device):
16
+ checkpoint = torch.load(checkpoint_path, map_location=device, weights_only=False)
17
+ tokenizer_path = Path(checkpoint["tokenizer"])
18
+ if not tokenizer_path.is_absolute():
19
+ tokenizer_path = checkpoint_path.parent / tokenizer_path
20
+ tokenizer = Tokenizer.from_file(str(tokenizer_path))
21
+ modelo = GPTZero(
22
+ checkpoint["vocab_size"],
23
+ checkpoint["block_size"],
24
+ checkpoint["d_model"],
25
+ checkpoint["nhead"],
26
+ checkpoint["num_layers"],
27
+ checkpoint["dropout"],
28
+ ).to(device)
29
+ modelo.load_state_dict(checkpoint["model_state"])
30
+ modelo.eval()
31
+ bos_id = tokenizer.token_to_id("<bos>")
32
+ eos_id = tokenizer.token_to_id("<eos>")
33
+ return modelo, tokenizer, checkpoint, bos_id, eos_id
34
+
35
+
36
+ def main() -> None:
37
+ parser = argparse.ArgumentParser()
38
+ parser.add_argument("--checkpoint", type=Path, default=Path("modelo_gpt_zero_stage2.pt"))
39
+ parser.add_argument("--pergunta", default="Olá! Tudo bem? Quem é você?")
40
+ parser.add_argument("--system", default="Você é um assistente útil e claro.")
41
+ parser.add_argument("--tokens", type=int, default=180)
42
+ parser.add_argument("--temperature", type=float, default=0.45)
43
+ parser.add_argument("--top-k", type=int, default=40)
44
+ parser.add_argument("--top-p", type=float, default=0.9)
45
+ parser.add_argument("--repetition-penalty", type=float, default=1.15)
46
+ parser.add_argument("--device", choices=["auto", "cpu", "cuda"], default="auto")
47
+ args = parser.parse_args()
48
+ if args.device == "auto":
49
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
50
+ else:
51
+ device = torch.device(args.device)
52
+ modelo, tokenizer, checkpoint, bos_id, eos_id = carregar(args.checkpoint, device)
53
+ prompt = f"<|system|>{args.system}<|end|><|user|>{args.pergunta}<|end|><|assistant|>"
54
+ resposta = gerar(modelo, tokenizer, prompt, device, args.tokens, args.temperature, args.top_k, args.top_p, bos_id, eos_id, args.repetition_penalty)
55
+ print(f"Checkpoint: {args.checkpoint}")
56
+ print(f"Pré-treinado: {checkpoint.get('pretrained')}")
57
+ print(f"Pergunta: {args.pergunta}")
58
+ print(f"Modelo: {resposta}")
59
+
60
+
61
+ if __name__ == "__main__":
62
+ main()