---
tags:
- gpt-neo
- text-generation
- transformers
license: mit
---

# TeamAiko-GPT-Neo-1.3B

This is the `TeamAiko-GPT-Neo-1.3B` model, a customized version of the `EleutherAI/gpt-neo-1.3B` model. This model has been branded and configured for use by Team Aiko. **Note: This is the base version of the model and has not been trained on any specific datasets.**

## Model Details

- **Model Name**: TeamAiko-GPT-Neo-1.3B
- **Base Model**: EleutherAI/gpt-neo-1.3B
- **Architecture**: GPT-Neo
- **Parameters**: 1.3 billion
- **Tokenizer**: AutoTokenizer
- **Framework**: PyTorch

## Usage

To use this model, you can load it with the `transformers` library:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Path to the model folder
model_path = "Team-Aiko/TeamAiko-GPT-Neo-1.3B"

# Set device to CPU to limit RAM usage
device = torch.device("cpu")
torch.set_num_threads(4)  # Limit the number of threads used by PyTorch

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)
model.to(device)  # Move model to CPU

# Test the model with a sample input
input_text = "Once upon a time in a land far, far away"
inputs = tokenizer(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs["input_ids"], max_length=50, num_return_sequences=1, no_repeat_ngram_size=2, early_stopping=True)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Generated text:", generated_text)