AI Model Trainer with EleutherAI/gpt-j-6b for Chameleon Shop Codes

The Trainer use actually the best chatgpt alternative model on huggingface. Here is the Training notice from Original Source:

This model was trained for 402 billion tokens over 383,500 steps on TPU v3-256 pod. It was trained as an autoregressive language model, using cross-entropy loss to maximize the likelihood of predicting the next token correctly.

Dataset Links:

import os
import pandas as pd
import torch
from sklearn.model_selection import train_test_split
from transformers import TrainingArguments, Trainer, AutoModelForCausalLM, AutoTokenizer

checkpoint = "EleutherAI/gpt-j-6b"  # Model checkpoint updated

device = "cuda" if torch.cuda.is_available() else "cpu"

model = AutoModelForCausalLM.from_pretrained(checkpoint, trust_remote_code=True)

tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(checkpoint, trust_remote_code=True)

# Load your data
data = pd.read_csv(os.getcwd() + 'chameleon_base_dataset20230530-1942.csv')

# Prepare your data
# You need to decide how to use your CSV data to create training examples for the model.
# For example, you might concatenate the 'description', 'code', and 'explanation' fields into a single string.
texts = data['description'] + ' ' + data['code'] + ' ' + data['explanation']
labels = data['classname']

# Tokenize your data
inputs = tokenizer(texts.tolist(), padding=True, truncation=True, max_length=512, return_tensors='pt')
inputs['labels'] = torch.tensor(labels.tolist())  # assuming labels are numerical

# Split data into training and validation sets
train_inputs, val_inputs, train_labels, val_labels = train_test_split(inputs, labels, test_size=0.2)

# Define training arguments
training_args = TrainingArguments(
    output_dir='./results',          # output directory
    num_train_epochs=3,              # total number of training epochs
    per_device_train_batch_size=16,  # batch size per device during training
    per_device_eval_batch_size=64,   # batch size for evaluation
    warmup_steps=500,                # number of warmup steps for learning rate scheduler
    weight_decay=0.01,               # strength of weight decay
    logging_dir='./logs',            # directory for storing logs
    load_best_model_at_end=True,     # load the best model when finished training (default metric is loss)
    # but you can specify `metric_for_best_model` argument to change to accuracy, f1, etc.

# Initialize our Trainer
trainer = Trainer(

# Training

# Saving the fine-tuned model

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