GPT NEO 350M

Introduction

GPT NEO 350M is a text generation model based on GPT-Neo architecture, which was developed as part of the EleutherAI initiative. This model is tailored for generating coherent and contextually relevant text.

Architecture

The model is built using the GPT-Neo architecture, designed to replicate the performance of OpenAI's GPT-3 models. It employs transformer-based neural networks and is developed using PyTorch.

Training

This version of GPT-NEO 350M was initially trained by EleutherAI. It involves extensive training on diverse datasets to ensure proficiency in generating human-like text.

Guide: Running Locally

1. Setup Environment: Ensure you have Python and PyTorch installed. Use a virtual environment for better package management.

2. Install Transformers Library: Run pip install transformers to install the Hugging Face Transformers library.

3. Load the Model: Download and load the model using the Transformers library. Use the following code snippet:

   from transformers import GPTNeoForCausalLM, GPT2Tokenizer
   
   tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-350M")
   model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-350M")
   

4. Run Inference: Tokenize the input text and generate predictions:

   input_ids = tokenizer("Your input here", return_tensors='pt').input_ids
   output = model.generate(input_ids)
   print(tokenizer.decode(output[0], skip_special_tokens=True))
   

5. Suggested Cloud GPUs: Consider using cloud services like AWS EC2, Google Cloud Platform, or Azure for access to GPU instances to speed up inference.

License

The model is shared under the Apache 2.0 License, allowing for use, modification, and distribution with minimal restrictions.