[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)
# 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.
Raw data
{
"_id": null,
"home_page": "https://github.com/kyegomez/gpt3",
"name": "gpt3-torch",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.6,<4.0",
"maintainer_email": "",
"keywords": "artificial intelligence,attention mechanism,transformers",
"author": "Kye Gomez",
"author_email": "kye@apac.ai",
"download_url": "https://files.pythonhosted.org/packages/d6/dd/1a275d8635ed62696c6a1b40cd7c08dd28ee66a41302b7da4a5230ab690f/gpt3_torch-0.0.9.tar.gz",
"platform": null,
"description": "[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)\n\n\n# GPT-3: Few-Shot Learning for Language Models\n\n\n\n\n## \ud83d\udcbb Installation\n\n`pip install gpt3-torch`\n\n---\n\n\n## Code Example\n\nHere's an illustrative code snippet that showcases GPT-3 in action:\n\n\n```python\nimport torch\nfrom gpt3.gp3 import GPT3\n\n# Generate a random input sequence\nx = torch.randint(0, 256, (1, 1024)).cuda()\n\n# Initialize GPT-3 model\nmodel = GPT3()\n\n# Pass the input sequence through the model\noutput = model(x)\n```\n\n\n### \ud83d\udcda Training\n\n```python\nfrom gpt3 import train\n\ntrain()\n\n```\n\nFor further instructions, refer to the [Training SOP](DOCs/TRAINING.md).\n\n\n1. Set the environment variables:\n - `ENTITY_NAME`: Your wandb project name\n - `OUTPUT_DIR`: Directory to save the weights (e.g., `./weights`)\n - `MASTER_ADDR`: For distributed training\n - `MASTER_PORT` For master port distributed training\n - `RANK`- Number of nodes services\n - `WORLD_SIZE` Number of gpus\n\n2. Configure the training:\n - Accelerate Config\n - Enable Deepspeed 3\n - Accelerate launch train_distributed_accelerate.py\n\nFor more information, refer to the [Training SOP](DOCs/TRAINING.md).\n\n\n\n\n---\n\nWelcome 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.\n\n## Introduction\n\nIn 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.\n\n## Methodology\n\nThis 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.\n\n## Results\n\nThe paper presents compelling results highlighting GPT-3's prowess in few-shot learning:\n\n- **Translation**\n- **Question-answering**\n- **Cloze tasks**\n- **On-the-fly reasoning**\n- **Domain adaptation tasks**\n\nFurthermore, 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.\n\n## Conclusion\n\nThe 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.\n\nFor 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).\n\nIf 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.",
"bugtrack_url": null,
"license": "MIT",
"summary": "GPT3 - Pytorch",
"version": "0.0.9",
"project_urls": {
"Homepage": "https://github.com/kyegomez/gpt3"
},
"split_keywords": [
"artificial intelligence",
"attention mechanism",
"transformers"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "ba4aa456e58c70c0cfc6d9341397981f367c639f307200e6c7fb7185cd4242aa",
"md5": "6e48d6d477a5259f87c382b0cd784f6d",
"sha256": "73def4776adcf3d2272656d684e8bee23176d016f8530ae855362cb35108f42a"
},
"downloads": -1,
"filename": "gpt3_torch-0.0.9-py3-none-any.whl",
"has_sig": false,
"md5_digest": "6e48d6d477a5259f87c382b0cd784f6d",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6,<4.0",
"size": 12187,
"upload_time": "2023-09-08T16:18:13",
"upload_time_iso_8601": "2023-09-08T16:18:13.986312Z",
"url": "https://files.pythonhosted.org/packages/ba/4a/a456e58c70c0cfc6d9341397981f367c639f307200e6c7fb7185cd4242aa/gpt3_torch-0.0.9-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "d6dd1a275d8635ed62696c6a1b40cd7c08dd28ee66a41302b7da4a5230ab690f",
"md5": "813473fa7e48e4f086d07cc050734586",
"sha256": "f16c0c2db2c8c3f9df5ce9162b5a79b2cc6cfc96b30673e2ad1730ed40fd963a"
},
"downloads": -1,
"filename": "gpt3_torch-0.0.9.tar.gz",
"has_sig": false,
"md5_digest": "813473fa7e48e4f086d07cc050734586",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6,<4.0",
"size": 13701,
"upload_time": "2023-09-08T16:18:15",
"upload_time_iso_8601": "2023-09-08T16:18:15.159735Z",
"url": "https://files.pythonhosted.org/packages/d6/dd/1a275d8635ed62696c6a1b40cd7c08dd28ee66a41302b7da4a5230ab690f/gpt3_torch-0.0.9.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-09-08 16:18:15",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "kyegomez",
"github_project": "gpt3",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "gpt3-torch"
}