Gpt-neox vs Open-Assistant

Detailed comparison of features, pros, cons, and usage

EleutherAI/gpt-neox is a powerful open-source toolkit for training large language models, offering flexibility and scalability, while LAION-AI/Open-Assistant is a collaborative project aimed at creating an open-source AI assistant, with a focus on ethical development and community involvement, though it may be less mature in terms of model performance compared to gpt-neox.

Gpt-neox

An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries

7,276

Open-Assistant

OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.

37,391

Gpt-neox Pros and Cons

Pros

Highly scalable and efficient implementation of GPT-style models
Supports training on multiple GPUs and nodes for large-scale language models
Includes advanced features like mixed-precision training and model parallelism
Well-documented codebase with detailed instructions for setup and usage

Cons

Requires significant computational resources for training large models
May be complex to set up and configure for users new to distributed training
Limited to GPT-style models, not suitable for other architectures
Potential challenges in fine-tuning or adapting the model for specific downstream tasks

Open-Assistant Pros and Cons

Pros

Open-source and collaborative approach to developing an AI assistant
Aims to create a more transparent and ethical alternative to proprietary AI models
Leverages community contributions for diverse training data and model improvements
Potential for customization and adaptation to specific use cases

Cons

May lack the resources and computing power of larger tech companies
Could face challenges in achieving performance comparable to proprietary models
Potential for inconsistent quality due to diverse community contributions
May require significant ongoing maintenance and moderation efforts

Gpt-neox Code Examples

Model Training

This snippet demonstrates how to initialize and train a GPT-NeoX model:

from megatron.neox_arguments import NeoXArgs
from megatron.training import pretrain


args = NeoXArgs.from_ymls(["configs/your_config.yml"])


pretrain(neox_args=args)

Text Generation

Here's how to generate text using a trained GPT-NeoX model:

from megatron.text_generation_utils import generate_samples_from_prompt


model, neox_args = initialize_model_from_config("path/to/config.yml")


prompt = "Once upon a time"
generated_text = generate_samples_from_prompt(
    neox_args=neox_args,
    model=model,
    text=prompt,
    maximum_tokens=100,
    recompute=False
)
print(generated_text)

Custom Dataset Loading

This snippet shows how to load a custom dataset for training:

from megatron import mpu
from megatron.data.gpt2_dataset import GPT2Dataset


class CustomDataset(GPT2Dataset):
    def __init__(self, neox_args, data_prefix, num_samples, seq_length, seed):
        super().__init__(neox_args, data_prefix, num_samples, seq_length, seed)
        # Add custom initialization here

    def __getitem__(self, idx):
        # Implement custom data loading logic
        return item


train_data = CustomDataset(neox_args, "path/to/data", num_samples, seq_length, seed)

Open-Assistant Code Examples

Data Processing

This snippet showcases how the project processes and filters datasets:

def filter_dataset(dataset: Dataset, filters: List[Filter]) -> Dataset:
    for filter in filters:
        dataset = dataset.filter(filter.filter_fn)
    return dataset

filtered_dataset = filter_dataset(
    raw_dataset,
    [
        MinWordsFilter(min_words=3),
        MaxWordsFilter(max_words=100),
        LanguageFilter(languages=["en"]),
    ]
)

Model Training

Here's an example of how the project sets up and trains a language model:

model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

trainer = Trainer(
    model=model,
    args=TrainingArguments(output_dir="./results", num_train_epochs=3),
    train_dataset=train_dataset,
    tokenizer=tokenizer,
)

trainer.train()

Inference

This snippet demonstrates how to use the trained model for inference:

def generate_response(prompt: str, max_length: int = 100) -> str:
    input_ids = tokenizer.encode(prompt, return_tensors="pt")
    output = model.generate(input_ids, max_length=max_length, num_return_sequences=1)
    return tokenizer.decode(output[0], skip_special_tokens=True)

response = generate_response("What is the capital of France?")
print(response)

Gpt-neox Quick Start

Installation

To get started with GPT-NeoX, follow these steps:

Clone the repository:

git clone https://github.com/EleutherAI/gpt-neox.git
cd gpt-neox

Install the required dependencies:
```
pip install -r requirements.txt
```
Install the project in editable mode:
```
pip install -e .
```

Basic Usage

Here's a simple example of how to use GPT-NeoX for text generation:

from gpt_neox import GPTNeoX, GPTNeoXConfig


model = GPTNeoX.from_pretrained("EleutherAI/gpt-neox-20b")


input_text = "Once upon a time"
generated_text = model.generate(input_text, max_length=100)

print(generated_text)

This example loads a pre-trained GPT-NeoX model and generates text based on the given input prompt.

Next Steps

Explore the configs directory for various model configurations
Check out the documentation for advanced usage and fine-tuning options
Join the EleutherAI Discord for community support and discussions

Open-Assistant Quick Start

Installation

To get started with Open-Assistant, follow these steps:

Clone the repository:

git clone https://github.com/LAION-AI/Open-Assistant.git
cd Open-Assistant

Set up a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

Install the required dependencies:
```
pip install -r requirements.txt
```

Basic Usage

Once you have installed Open-Assistant, you can start using it with these steps:

Prepare your dataset:
- Ensure your data is in the correct format (refer to the documentation for specifics)
- Place your dataset in the appropriate directory

Run the training script:

python train.py --config configs/default_config.yaml

Use the trained model for inference:

from open_assistant import OpenAssistant

model = OpenAssistant.load_from_checkpoint("path/to/checkpoint.ckpt")
response = model.generate("Your prompt here")
print(response)

For more detailed information and advanced usage, please refer to the official documentation.

Top Related Projects

DeepSpeed

39,112

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Pros of DeepSpeed

Highly optimized for large-scale distributed training
Supports various optimization techniques like ZeRO, pipeline parallelism, and 3D parallelism
Integrates well with popular deep learning frameworks like PyTorch and Hugging Face Transformers

Cons of DeepSpeed

Steeper learning curve compared to gpt-neox and Open-Assistant
Primarily focused on training optimization, less emphasis on model architecture or assistant-like functionality

Code Comparison

gpt-neox:

from megatron.neox_arguments import NeoXArgs
from megatron.global_vars import set_global_variables, get_tokenizer
from megatron.neox_model import GPTNeoX

Open-Assistant:

from oasst_data import ExampleDialogue, Message
from oasst_model import OAModel
from oasst_inference import generate_response

DeepSpeed:

import deepspeed
model_engine, optimizer, _, _ = deepspeed.initialize(
    args=args, model=model, model_parameters=params)

The code snippets highlight the different focus areas of each project. gpt-neox emphasizes model architecture, Open-Assistant focuses on dialogue and inference, while DeepSpeed concentrates on distributed training optimization.

transformers

146,142

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

Pros of transformers

Extensive model support: Covers a wide range of transformer-based models
Well-documented and actively maintained: Regular updates and comprehensive documentation
Easy integration: Seamless use with PyTorch and TensorFlow

Cons of transformers

Steeper learning curve: More complex for beginners due to its extensive features
Resource-intensive: Can be demanding on computational resources for larger models

Code Comparison

transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

gpt-neox

from transformers import GPTNeoXForCausalLM, GPTNeoXTokenizerFast

model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b")
tokenizer = GPTNeoXTokenizerFast.from_pretrained("EleutherAI/gpt-neox-20b")

Open-Assistant

# No direct model loading code available
# Open-Assistant focuses on creating an open-source AI assistant
# rather than providing a standalone model library

transformers offers a more versatile and widely-applicable solution for various transformer models, while gpt-neox specializes in large-scale language models. Open-Assistant, on the other hand, aims to create an open-source AI assistant rather than providing a model library.

t5x

2,856

Pros of t5x

Highly modular and flexible architecture for training and evaluating T5 models
Extensive documentation and examples for various use cases
Seamless integration with Google's JAX and Flax libraries for efficient training

Cons of t5x

Steeper learning curve compared to gpt-neox and Open-Assistant
Limited to T5-based models, while gpt-neox focuses on GPT-style architectures
Less community-driven development compared to Open-Assistant

Code Comparison

t5x:

import t5x
model = t5x.models.EncoderDecoderModel(...)
trainer = t5x.trainer.Trainer(...)
trainer.train(...)

gpt-neox:

from megatron.neox_arguments import NeoXArgs
from megatron.global_vars import set_global_variables
args = NeoXArgs.from_ymls("configs/your_config.yml")
set_global_variables(args)

Open-Assistant:

from oasst_data import ExampleDialog
from oasst_model import OAModel
model = OAModel.from_pretrained("open-assistant/oasst-sft-1-pythia-12b")
response = model.generate(ExampleDialog(...))

The code snippets highlight the different approaches:

t5x focuses on encoder-decoder models with a flexible training setup
gpt-neox emphasizes configuration-based model creation
Open-Assistant provides a more user-friendly interface for dialogue generation

fairseq

31,682

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

Pros of fairseq

Comprehensive toolkit for sequence modeling tasks
Supports a wide range of architectures and tasks
Well-documented and actively maintained by Facebook AI Research

Cons of fairseq

Steeper learning curve compared to more specialized repositories
May be overkill for projects focused solely on language models

Code Comparison

fairseq:

from fairseq.models.transformer import TransformerModel
model = TransformerModel.from_pretrained('/path/to/model', 'checkpoint.pt')
model.eval()
tokens = model.encode('Hello world')
output = model.generate(tokens, beam=5)[0]

gpt-neox:

from transformers import GPTNeoXForCausalLM, GPTNeoXTokenizerFast
model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b")
tokenizer = GPTNeoXTokenizerFast.from_pretrained("EleutherAI/gpt-neox-20b")
input_ids = tokenizer.encode("Hello world", return_tensors="pt")
output = model.generate(input_ids, max_length=50)

Open-Assistant:

# No direct code comparison available as Open-Assistant
# focuses on creating an open-source AI assistant
# rather than providing a standalone language model API

fairseq offers a more versatile toolkit for various sequence modeling tasks, while gpt-neox is specialized for large language models. Open-Assistant aims to create an open-source AI assistant, making it less comparable in terms of direct model usage.

llama

58,578

Inference code for Llama models

Pros of llama

Developed by Facebook's research team, potentially benefiting from extensive resources and expertise
Focuses on large language models, which may offer advanced natural language processing capabilities
Likely has strong integration with Facebook's AI ecosystem

Cons of llama

Less open-source community involvement compared to gpt-neox and Open-Assistant
May have more restrictive licensing terms due to its association with a large tech company
Potentially less flexible for customization and adaptation to specific use cases

Code comparison

gpt-neox

from transformers import GPTNeoXForCausalLM, GPTNeoXTokenizerFast

model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b")
tokenizer = GPTNeoXTokenizerFast.from_pretrained("EleutherAI/gpt-neox-20b")

Open-Assistant

from oasst_shared.model_configs import ModelConfig
from oasst_shared.schemas import protocol as protocol_schema

config = ModelConfig(name="oasst-sft-1-pythia-12b", model_path="OpenAssistant/oasst-sft-1-pythia-12b")

llama

from transformers import LlamaForCausalLM, LlamaTokenizer

model = LlamaForCausalLM.from_pretrained("facebook/llama-7b")
tokenizer = LlamaTokenizer.from_pretrained("facebook/llama-7b")

petals

9,741

🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading

Pros of Petals

Focuses on distributed inference, allowing large language models to run on consumer hardware
Implements a novel approach to federated learning for language models
Provides a user-friendly API for interacting with distributed models

Cons of Petals

Limited model selection compared to GPT-NeoX and Open-Assistant
May have higher latency due to distributed nature of inference
Less extensive documentation and community support than the other projects

Code Comparison

GPT-NeoX:

from transformers import GPTNeoXForCausalLM, GPTNeoXTokenizerFast

model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b")
tokenizer = GPTNeoXTokenizerFast.from_pretrained("EleutherAI/gpt-neox-20b")

Open-Assistant:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5")
tokenizer = AutoTokenizer.from_pretrained("OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5")

Petals:

import petals

model = petals.AutoDistributedModelForCausalLM.from_pretrained("bigscience/bloom")
tokenizer = petals.AutoTokenizer.from_pretrained("bigscience/bloom")