dnagpt/llama-dna-sft

from transformers import AutoTokenizer, AutoConfig,AutoModel
from transformers import DataCollatorForLanguageModeling
from transformers import Trainer, TrainingArguments
from transformers import  AutoConfig, AutoModelForCausalLM,LlamaForCausalLM,LlamaTokenizer
from tokenizers import Tokenizer
from datasets import load_dataset

dna_ft_dataset = load_dataset('dnagpt/dna_multi_task_finetune')

data = dna_ft_dataset["train"].train_test_split(train_size=0.1, seed=42)

tokenizer = LlamaTokenizer.from_pretrained("dnagpt/llama-dna-sft") 
tokenizer.pad_token = tokenizer.eos_token

model = LlamaForCausalLM.from_pretrained("dnagpt/llama-dna-sft") #sft

#构建提示词
def format_input(entry):
    instruction_text = (
        f"Below is an instruction that describes a task. "
        f"Write a response that appropriately completes the request."
        f"\n\n### Instruction:\n{entry['instruction']}"
    )

    input_text = f"\n\n### Input:\n{entry['input']}" if entry["input"] else ""

    return instruction_text + input_text + "\n\n### Response:\n"

#构建提示词
def build_prompt(entry):

    input_data = format_input(entry)

    desired_response = entry['output']

    return input_data + desired_response

example = data["test"][0]

prompt = build_prompt(example)

def inference(text, model, tokenizer, max_input_tokens=1000, max_output_tokens=1000):
  # Tokenize
  input_ids = tokenizer.encode(
          text,
          return_tensors="pt",
          truncation=True,
          max_length=max_input_tokens
          # return_attention_mask=True,
  )

  # Generate
  device = model.device
  generated_tokens_with_prompt = model.generate(
    input_ids=input_ids.to(device),
    #max_length=max_output_tokens,
    max_new_tokens=8,
    temperature=0.01  # 控制生成的多样性
  )

  # Decode
  generated_text_with_prompt = tokenizer.decode(generated_tokens_with_prompt[0], skip_special_tokens=True)
  generated_text_answer = generated_text_with_prompt[len(text):]


  return generated_text_answer


input_text = format_input(data["test"][0])

print("input (test):", input_text)

print("real answer:", data["test"][0]["output"])

print("--------------------------\n")

print("model's answer: \n")
print(inference(input_text, model, tokenizer))