[](https://discord.gg/qUtxnK2NMf)
# GPT4
The open source implementation of the base model behind GPT-4 from OPENAI [Language + Multi-Modal], click here for the [Research Paper](https://arxiv.org/pdf/2303.08774.pdf)
# Installation
`pip install gpt4-torch`
# Usage
Here's an illustrative code snippet that showcases GPT-3 in action:
```python
import torch
from gpt4 import GPT4
# Generate a random input sequence
x = torch.randint(0, 256, (1, 1024)).cuda()
# Initialize GPT-3 model
model = GPT4()
# Pass the input sequence through the model
output = model(x)
```
## MultiModal Iteration
* Pass in text and and image tensors into GPT4
```python
import torch
from gpt4.gpt4 import GPT4MultiModal
#usage
img = torch.randn(1, 3, 256, 256)
text = torch.randint(0, 20000, (1, 1024))
model = GPT4MultiModal()
output = model(text, img)
```
# 📚 Training
```python
from gpt4 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).
Raw data
{
"_id": null,
"home_page": "https://github.com/kyegomez/gpt3",
"name": "gpt4-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/ac/00/bd320b816761bdd79d7170151b4e04a1224e893550c632761c0b3c743bad/gpt4_torch-0.0.3.tar.gz",
"platform": null,
"description": "[](https://discord.gg/qUtxnK2NMf)\n\n\n\n# GPT4\nThe open source implementation of the base model behind GPT-4 from OPENAI [Language + Multi-Modal], click here for the [Research Paper](https://arxiv.org/pdf/2303.08774.pdf)\n\n\n# Installation\n`pip install gpt4-torch`\n\n\n# Usage\n\nHere's an illustrative code snippet that showcases GPT-3 in action:\n\n\n```python\nimport torch\nfrom gpt4 import GPT4\n\n# Generate a random input sequence\nx = torch.randint(0, 256, (1, 1024)).cuda()\n\n# Initialize GPT-3 model\nmodel = GPT4()\n\n# Pass the input sequence through the model\noutput = model(x)\n```\n\n## MultiModal Iteration\n* Pass in text and and image tensors into GPT4\n```python\nimport torch\nfrom gpt4.gpt4 import GPT4MultiModal\n\n#usage\nimg = torch.randn(1, 3, 256, 256)\ntext = torch.randint(0, 20000, (1, 1024))\n\n\nmodel = GPT4MultiModal()\noutput = model(text, img)\n\n```\n\n\n# \ud83d\udcda Training\n\n```python\nfrom gpt4 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",
"bugtrack_url": null,
"license": "MIT",
"summary": "GPT4 - Pytorch",
"version": "0.0.3",
"project_urls": {
"Homepage": "https://github.com/kyegomez/gpt3"
},
"split_keywords": [
"artificial intelligence",
"attention mechanism",
"transformers"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "6eb3b670acb4b361f07ff2756939d3676db22d2d42ce2883a45ce0cd3dbd2586",
"md5": "c215fe941a9acdd2486d4f8523fd0315",
"sha256": "ce7dec810c55b4022634ca276e7f5a82939b845e57287e508b23bdb6fb533d39"
},
"downloads": -1,
"filename": "gpt4_torch-0.0.3-py3-none-any.whl",
"has_sig": false,
"md5_digest": "c215fe941a9acdd2486d4f8523fd0315",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6,<4.0",
"size": 28098,
"upload_time": "2023-08-26T03:19:27",
"upload_time_iso_8601": "2023-08-26T03:19:27.786997Z",
"url": "https://files.pythonhosted.org/packages/6e/b3/b670acb4b361f07ff2756939d3676db22d2d42ce2883a45ce0cd3dbd2586/gpt4_torch-0.0.3-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "ac00bd320b816761bdd79d7170151b4e04a1224e893550c632761c0b3c743bad",
"md5": "3466f84d98ffe8327c03339e3f2aae69",
"sha256": "04801544de0ad4c9a2a1b8b91d07b9f363774077921a2df48dfaad9fecc8ea5a"
},
"downloads": -1,
"filename": "gpt4_torch-0.0.3.tar.gz",
"has_sig": false,
"md5_digest": "3466f84d98ffe8327c03339e3f2aae69",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6,<4.0",
"size": 28797,
"upload_time": "2023-08-26T03:19:29",
"upload_time_iso_8601": "2023-08-26T03:19:29.093214Z",
"url": "https://files.pythonhosted.org/packages/ac/00/bd320b816761bdd79d7170151b4e04a1224e893550c632761c0b3c743bad/gpt4_torch-0.0.3.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-08-26 03:19:29",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "kyegomez",
"github_project": "gpt3",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "gpt4-torch"
}
JSON
