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# GPT-3: Few-Shot Learning for Language Models




## 💻 Installation

`pip install gpt3-torch`

---


## Code Example

Here's an illustrative code snippet that showcases GPT-3 in action:


```python
import torch
from gpt3.gp3 import GPT3

# Generate a random input sequence
x = torch.randint(0, 256, (1, 1024)).cuda()

# Initialize GPT-3 model
model = GPT3()

# Pass the input sequence through the model
output = model(x)
```


### 📚 Training

```python
from gpt3 import train

train()

```

For further instructions, refer to the [Training SOP](DOCs/TRAINING.md).


1. Set the environment variables:
   - `ENTITY_NAME`: Your wandb project name
   - `OUTPUT_DIR`: Directory to save the weights (e.g., `./weights`)
   - `MASTER_ADDR`: For distributed training
   - `MASTER_PORT` For master port distributed training
   - `RANK`- Number of nodes services
   - `WORLD_SIZE` Number of gpus

2. Configure the training:
   - Accelerate Config
   - Enable Deepspeed 3
   - Accelerate launch train_distributed_accelerate.py

For more information, refer to the [Training SOP](DOCs/TRAINING.md).




---

Welcome to the repository for GPT-3: Few-Shot Learning for Language Models! This repository provides code examples and insights related to the groundbreaking paper "Language Models are Few-Shot Learners" by Tom B. Brown et al. Explore the potential of GPT-3, a language model with 175 billion parameters, and its remarkable few-shot learning capabilities. Below, we provide an overview of key concepts, practical code snippets, and the paper's findings.

## Introduction

In recent years, Natural Language Processing (NLP) has witnessed remarkable progress through pre-training language models on vast text corpora and fine-tuning them for specific tasks. However, these models still demand substantial task-specific data to excel. This paper introduces a paradigm shift by unveiling the concept of few-shot learning for language models. Discover how the scale of the model impacts its performance, akin to humans learning from just a few examples or simple instructions.

## Methodology

This paper introduces GPT-3, an autoregressive language model with a groundbreaking scale of 175 billion parameters. The authors assess GPT-3's few-shot learning capabilities by subjecting it to various tasks without any gradient updates or fine-tuning. The model's understanding of tasks and demonstrations is achieved solely through text interactions.

## Results

The paper presents compelling results highlighting GPT-3's prowess in few-shot learning:

- **Translation**
- **Question-answering**
- **Cloze tasks**
- **On-the-fly reasoning**
- **Domain adaptation tasks**

Furthermore, GPT-3 excels in tasks that involve unscrambling words, incorporating novel words into sentences, and performing 3-digit arithmetic. While demonstrating its potential, the paper acknowledges areas where GPT-3's few-shot learning encounters challenges, opening avenues for future enhancement. Additionally, methodological concerns related to training language models on extensive web corpora are discussed.

## Conclusion

The study concludes that scaling up model size, as exemplified by GPT-3, substantially elevates few-shot learning capabilities. GPT-3 achieves competitive results compared to state-of-the-art fine-tuning approaches. The authors delve into the broader implications of GPT-3's capabilities, including its potential to generate human-like text. The paper emphasizes the need for ongoing research to address challenges in challenging few-shot learning tasks and to address methodological concerns associated with large web corpora training.

For a comprehensive understanding of the paper's methodologies, insights, and findings, refer to the original publication: [Language Models are Few-Shot Learners](https://doi.org/arXiv.2005.14165).

If you find this repository valuable, consider starring it or contributing to foster continual exploration and discourse in the field of NLP and few-shot learning.