使用 Mistral 微调您的大型语言模型 (LLM)：分步指南

pip install torch transformers datasets

from transformers import automodelforcausallm, autotokenizer# load the mistral model and tokenizermodel_name = "mistralai/mistral-7b"model = automodelforcausallm.from_pretrained(model_name)tokenizer = autotokenizer.from_pretrained(model_name)

from datasets import load_dataset# load your custom datasetdataset = load_dataset("your_dataset")# tokenize the datadef tokenize_function(examples):    return tokenizer(examples["text"], padding="max_length", truncation=true)tokenized_dataset = dataset.map(tokenize_function, batched=true)

from transformers import trainer, trainingarguments# set up training argumentstraining_args = trainingarguments(    output_dir="./results",    num_train_epochs=3,    per_device_train_batch_size=8,    per_device_eval_batch_size=8,    warmup_steps=500,    weight_decay=0.01,    logging_dir="./logs",    logging_steps=10,)# initialize the trainertrainer = trainer(    model=model,    args=training_args,    train_dataset=tokenized_dataset["train"],    eval_dataset=tokenized_dataset["test"],)# start fine-tuningtrainer.train()

# evaluate the modeleval_results = trainer.evaluate()# print the resultsprint(f"perplexity: {eval_results['perplexity']}")

# Save your fine-tuned modeltrainer.save_model("./fine-tuned-mistral")# Load and use the model for inferencemodel = AutoModelForCausalLM.from_pretrained("./fine-tuned-mistral")