This is the collection of open source code snippets in Python which you can use to download, finetune, and generate content with LLM on your laptop:
import tensorflow as tf
import numpy as np
import os
import json
import random
import time
import argparse
with open(os.path.join(args.model_path, "hyperparams.json"), "r") as f:
hyperparams = json.load(f)
model = tf.compat.v1.estimator.Estimator( model_fn=model_fn, model_dir=args.output_path, params=hyperparams,
config=tf.compat.v1.estimator.RunConfig( save_checkpoints_steps=5000, keep_checkpoint_max=10,
save_summary_steps=5000 ))
#Define the input function for the dataset
def input_fn(mode):
dataset = tf.data.TextLineDataset(args.dataset_path)
dataset = dataset.repeat()
dataset = dataset.shuffle(buffer_size=10000)
dataset = dataset.batch(args.batch_size)
dataset = dataset.map(lambda x: tf.strings.substr(x, 0, hparams["n_ctx"]))
iterator = dataset.make_one_shot_iterator()
return iterator.get_next()
#Define the training function
def train():
for epoch in range(args.epochs):
model.train(input_fn=lambda: input_fn(tf.estimator.ModeKeys.TRAIN))
print(f"Epoch {epoch+1} completed.")
#Start the training
train()
#Define the command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument("--model_path", type=str, required=True)
parser.add_argument("--length", type=int, default=110)
parser.add_argument("--temperature", type=float, default=0.8)
args = parser.parse_args()
#Load the fine-tuned model
with open(os.path.join(args.model_path, "hyperparams.json"), "r") as f:
hyperparams = json.load(f)
model_fn = model_fn(hyperparams, tf.estimator.ModeKeys.PREDICT)
model = tf.compat.v1.estimator.Estimator(model_fn=model_fn, model_dir=args.model_path, params=hyperparams)
#Define the generation function
def generate_text(length, temperature):
start_token = "<|startoftext|>"
tokens = tokenizer.convert_tokens_to_ids([start_token])
token_length = len(tokens)
while token_length < length:
prediction_input = np.array(tokens[-hyperparams["n_ctx"]:])
output = list(model.predict(input_fn=lambda: [[prediction_input]]))[0]["logits"]
logits = output[-1] / temperature
logits = logits - np.max(logits)
probs = np.exp(logits) / np.sum(np.exp(logits))
token = np.random.choice(range(hyperparams["n_vocab"]), p=probs)
tokens.append(token)
token_length += 1
output_text = tokenizer.convert_ids_to_tokens(tokens)
output_text = "".join(output_text).replace("▁", " ")
output_text = output_text.replace(start_token, "")
return output_text
#Generate text
text = generate_text(args.length, args.temperature)
print(text)