|
import torch |
|
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig |
|
|
|
model_id = "clibrain/Llama-2-7b-ft-instruct-es" |
|
|
|
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to("cuda") |
|
tokenizer = AutoTokenizer.from_pretrained(model_id) |
|
|
|
def create_instruction(instruction, input_data=None, context=None): |
|
sections = { |
|
"Instrucci贸n": instruction, |
|
"Entrada": input_data, |
|
"Contexto": context, |
|
} |
|
|
|
system_prompt = "A continuaci贸n hay una instrucci贸n que describe una tarea, junto con una entrada que proporciona m谩s contexto. Escriba una respuesta que complete adecuadamente la solicitud.\n\n" |
|
prompt = system_prompt |
|
|
|
for title, content in sections.items(): |
|
if content is not None: |
|
prompt += f"### {title}:\n{content}\n\n" |
|
|
|
prompt += "### Respuesta:\n" |
|
|
|
return prompt |
|
|
|
|
|
def generate( |
|
instruction, |
|
input=None, |
|
context=None, |
|
max_new_tokens=128, |
|
temperature=0.1, |
|
top_p=0.75, |
|
top_k=40, |
|
num_beams=4, |
|
**kwargs |
|
): |
|
|
|
prompt = create_instruction(instruction, input, context) |
|
print(prompt.replace("### Respuesta:\n", "")) |
|
inputs = tokenizer(prompt, return_tensors="pt") |
|
input_ids = inputs["input_ids"].to("cuda") |
|
attention_mask = inputs["attention_mask"].to("cuda") |
|
generation_config = GenerationConfig( |
|
temperature=temperature, |
|
top_p=top_p, |
|
top_k=top_k, |
|
num_beams=num_beams, |
|
**kwargs, |
|
) |
|
with torch.no_grad(): |
|
generation_output = model.generate( |
|
input_ids=input_ids, |
|
attention_mask=attention_mask, |
|
generation_config=generation_config, |
|
return_dict_in_generate=True, |
|
output_scores=True, |
|
max_new_tokens=max_new_tokens, |
|
early_stopping=True |
|
) |
|
s = generation_output.sequences[0] |
|
output = tokenizer.decode(s) |
|
return output.split("### Respuesta:")[1].lstrip("\n") |
|
|
|
instruction = "Dame una lista de lugares a visitar en Espa帽a." |
|
print(generate(instruction)) |
|
|