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creating your own supervised fine-tuned model similar to chatgpt involves several steps, from data preparation to model training. here’s a step-by-step tutorial on how to do it, including code examples.

step 1: understanding the basics

supervised fine-tuning (sft) is a process where you take a pre-trained language model and fine-tune it on a specific dataset. this helps the model learn domain-specific language or improve its performance on specific tasks.

step 2: prerequisites

before we start, make sure you have the following:

1. python installed on your machine.
2. libraries: `transformers`, `torch`, `datasets`, and `accelerate`.

you can install the required libraries using pip:

```bash
pip install transformers torch datasets accelerate
```

step 3: preparing your dataset

you need a dataset that contains input-output pairs. for example, if you want to fine-tune a model to answer questions, you might have a dataset with questions and their respective answers.

here’s an example dataset format in csv:

```csv
input_text,output_text
"what is the capital of france?","the capital of france is paris."
"what is the largest mammal?","the largest mammal is the blue whale."
```

load this dataset using the `datasets` library:

```python
from datasets import load_dataset

load dataset
dataset = load_dataset('csv', data_files='your_dataset.csv')

inspect the dataset
print(dataset['train'][0])
```

step 4: preprocessing the data

you will need to preprocess the data to fit the model input format. tokenization is crucial here.

```python
from transformers import autotokenizer

load tokenizer
tokenizer = autotokenizer.from_pretrained('gpt2')

tokenize the dataset
def preprocess_function(examples):
return tokenizer(examples['input_text'], text_target=examples['output_text'], truncation=true)

tokenized_dataset = dataset.map(preprocess_function, batched=true)
```

step 5: setting up the model

next, load a pre-trained model for fine-tuning. here, we will use ...

#ChatGPT #SupervisedFineTuning #AITraining

ChatGPT
supervised fine-tuning
SFT
model training
AI customization
natural language processing
machine learning
dataset preparation
prompt engineering
performance optimization
transfer learning
user-specific adaptation
conversational AI
text generation
AI development