The model_fn needs to be implemented by referring the OpenAI documentation. The code is incomplete. I did this story some time back and lost track of it. Now it seems to be all over the internet.

Try this code instead which does the same thing:

Make sure to install the gpt-2-simple library using:

pip install gpt-2-simple

#########################

import gpt_2_simple as gpt2

import tensorflow as tf

##########################

# Download the GPT-2 model (run only once)

gpt2.download_gpt2(model_name="124M")

##########################

# Fine-tune the GPT-2 model

def fine_tune_gpt2(dataset_path, model_save_path, steps=1000):

sess = gpt2.start_tf_sess()

gpt2.finetune(sess,

dataset_path,

model_name="124M",

steps=steps,

restore_from='fresh',

run_name='run1',

print_every=10,

sample_every=200,

save_every=500)

# Save the fine-tuned model

gpt2.save_gpt2(sess, model_save_path)

# Generate text using the fine-tuned GPT-2 model

def generate_text(model_path, length=100, temperature=0.7):

sess = gpt2.start_tf_sess()

gpt2.load_gpt2(sess, model_path)

text = gpt2.generate(sess,

length=length,

temperature=temperature,

prefix=None,

truncate=None,

include_prefix=False,

nsamples=1,

batch_size=1,

return_as_list=True)[0]

return text

# Fine-tune the GPT-2 model

fine_tune_gpt2(dataset_path="path/to/your/dataset.txt",

model_save_path="path/to/save/fine-tuned/model",

steps=1000)

# Generate text using the fine-tuned GPT-2 model

generated_text = generate_text(model_path="path/to/save/fine-tuned/model",

length=100,

temperature=0.7)

print(generated_text)

#######################

You'll need to replace "path/to/your/dataset.txt" with the actual path to your training dataset (stating the obvious). Additionally, specify the desired paths for saving the fine-tuned model and where to load it from for text generation.

Remember that the fine-tuning process can be computationally expensive, especially with larger models and longer training steps. Adjust the steps parameter as needed based on your available resources and the desired level of fine-tuning.

As for the dataset, I suggest you go for something related to what you want your LLM to do (obviously). Use the OpenAI datasets or the HuggingFace datasets.

Don't just cut and paste the code above. The command to download the model and save it to your local system should be run only once. The finetuning should be done just once. The code above is a template. Stating the obvious, but just in case.

You need to decide what you want the LLM to do, and choose a dataset accordingly (I recommend HuggingFace).

Also adjust the number of iterations to decrease computational cost and replace the path_to_data_set and path_to_saved_model with the values specific to your particular example.

The only code you need to call repeatedly (stating the obvious, yet again) is generate_text().