odyssey-labs/Hercules-3-1B

Hercules - Odyssey Labs

Welcome to Hercules, a cutting-edge language model by Odyssey Labs based on the highly performant Falcon 3 architecture. Hercules is designed to deliver powerful, context-aware, and efficient natural language understanding and generation.

Key Features

• State-of-the-Art Performance: Built on Falcon 3, Hercules delivers exceptional accuracy and fluency across a wide range of tasks.
• Fine-Tuned for Versatility: Optimized for applications such as content generation, summarization, question answering, and more.
• Scalable Deployment: Designed for seamless integration into cloud-based, on-premises, and edge solutions.
• Customizable: Easily fine-tune Hercules for specific domains or tasks using your data.

Model Details

• Model Name: Hercules
• Architecture: Falcon 3
• Parameters: 1B
• Training Dataset: Trained on diverse datasets, including open-domain corpora and domain-specific data for balanced generalization.
• License: Apache 2.0

Intended Use

Hercules is designed for a variety of natural language processing (NLP) applications, such as:

• Text Completion
• Creative Writing
• Code Assistance
• Customer Support
• Language Translation
• Knowledge Retrieval

Limitations

While Hercules is powerful, it is important to use it responsibly. The model may:

• Generate incorrect or misleading information.
• Exhibit biases present in the training data.
• Require additional fine-tuning for highly specialized tasks.

We encourage thorough testing and validation before deploying Hercules! in production environments.

Installation

To use Hercules, install the required Python libraries and load the model:

pip install transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load Hercules!
tokenizer = AutoTokenizer.from_pretrained("odyssey-labs/Hercules-3-1B")
model = AutoModelForCausalLM.from_pretrained("odyssey-labs/Hercules-3-1B")

# Example Usage
input_text = "Explain the significance of Hercules in Greek mythology."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Citation

If you use Hercules in your research or applications, please cite it as follows:

@misc{hercules2025,
  author = {Odyssey Labs},
  title = {Hercules},
  year = {2025},
  url = {https://huggingface.co/odyssey-labs}
}

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Thank you for using Hercules We are excited to see what you'll create with it!