transformers safetensors llama text-generation conversational en license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: apache-2.0
language:
- en
---

# Prometh-222B Model Card

**Prometh-222B** is a state-of-the-art, auto-regressive transformer-based causal language model, boasting an impressive 222 billion parameters. As one of the largest open-source models available, it represents a significant advancement in the field of natural language processing (NLP) and artificial intelligence (AI).

## Model Overview

Prometh-222B has been meticulously merged with other models that were trained on a diverse and extensive dataset, enabling it to generate highly coherent and contextually relevant text across a wide range of topics and styles. Its auto-regressive nature allows for the generation of long-form content that maintains logical consistency and thematic continuity.

## Key Features

- **Extensive Knowledge Base**: Due to its vast number of parameters and comprehensive training data, Prometh-222B exhibits a deep understanding of world knowledge, language nuances, and subject matter expertise.
- **High Coherence in Text Generation**: Generates text that is not only contextually relevant but also maintains coherence over long passages.
- **Versatility**: Demonstrates remarkable versatility across various NLP tasks, including but not limited to text generation, summarization, translation, and more.

## Application Areas

Prometh-222B is ideally suited for applications requiring high-quality text generation, such as:

- Creative writing and storytelling
- Content creation for blogs, articles, and social media
- Automated summarization of documents
- Language translation
- Conversational agents and chatbots

## 💻 Usage Instructions(Inference Testing on going)

```python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from accelerate import Accelerator

# Initialize the Accelerator
accelerator = Accelerator()

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("AIFS/Prometh-222B")
model = AutoModelForCausalLM.from_pretrained("AIFS/Prometh-222B")

# Tell Accelerate to prepare the model
model, tokenizer = accelerator.prepare(model, tokenizer)

# Prepare your text data
prompt = "The future of AI in healthcare is"
input_ids = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=512)

# Use the model and tokenizer to generate text
# Note: We directly use model and tokenizer without pipeline for better control and efficiency in multi-GPU setups.
generated_ids = accelerator.unwrap_model(model).generate(**input_ids, max_length=50, num_return_sequences=3)

# Decode generated ids to text
for g in generated_ids:
    print(tokenizer.decode(g, skip_special_tokens=True))

```

## Technical Specifications

- **Model Architecture**: Transformer-based auto-regressive causal language model
- **Parameters**: 222 billion
- **Training Data**: Diverse datasets encompassing a wide range of topics and domains

## Environmental Impact

We recognize the significant computational resources required to train and deploy models of this scale. We are committed to optimizing the efficiency of Prometh-222B to reduce its environmental impact.

## Ethical Considerations

While Prometh-222B is a powerful tool, it is imperative to use it responsibly, especially given its potential to generate realistic and influential text. Users are urged to consider the ethical implications of their applications, particularly in sensitive areas such as news generation, political discourse, and educational content.

## Model Details and Attribution

- **Developed by**: [IAGO GASPAR]
- **Shared by**: [AIFS]
- **Model type**: Transformer-based auto-regressive causal LM
- **Language(s)**: English (en)
- **License**: Apache-2.0

We welcome feedback, contributions, and collaboration from the community to further improve and responsibly use Prometh-222B.

## Acknowledgments
We want to express our gratitude to the Open Source Community for their invaluable work on the open-source software that made this project possible. 
Their research in the merging of pre-trained models weights was essential to our work.