gpt3-torch


Namegpt3-torch JSON
Version 0.0.9 PyPI version JSON
download
home_pagehttps://github.com/kyegomez/gpt3
SummaryGPT3 - Pytorch
upload_time2023-09-08 16:18:15
maintainer
docs_urlNone
authorKye Gomez
requires_python>=3.6,<4.0
licenseMIT
keywords artificial intelligence attention mechanism transformers
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            [![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"
}
        
Elapsed time: 0.12778s