odyssey-labs/Astral-1-10B

Astral-1

Powerd by Upstage's SOLAR 10.7B

Model Description

Astral-1 is a 10-billion parameter model fine-tuned by Odyssey Labs. Built on the foundation of SOLAR-10.7B-Instruct-v1.0, Astral-1 is designed to excel in instruction-following tasks and demonstrates significant improvements in natural language understanding and generation.

Astral-1 is suitable for a wide variety of applications, including:

• Question answering
• Text summarisation
• Dialogue systems
• Creative writing

The model is fine-tuned with a focus on enhancing usability for non-commercial research and educational purposes.

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Intended Use

• General text generation
• Instruction following
• Single-turn conversations
• Analysis and reasoning tasks

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Training Details

• Built upon: SOLAR's 10.7B instruction-tuned model
• Inherits: SOLAR's robust training methodology
• Maintains: Original model's performance characteristics

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Technical Specifications

• Context Length: 4096 tokens
• Architecture Details:
  • Uses SOLAR's Depth Up-Scaling (DUS)
  • Optimized for efficiency and performance
• Input Format: Supports both plain text and structured prompts
• Output Format: Generated text following input instructions

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Hardware Requirements

Specification	Details
Minimum GPU Memory	12GB
Recommended GPU Memory	16GB for optimal performance
Quantization Options	Support varies by implementation

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Generation Parameters

• Recommended to follow base SOLAR model parameters
• Supports standard transformer generation configurations

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Usage

To use Astral-1, follow the steps below:

Installation

Ensure you have the transformers library installed:

pip install transformers

Code Example

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the model and tokenizer
model_name = "odyssey-labs/Astral-1-10B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define your input prompt
prompt = "Explain the significance of artificial intelligence in healthcare."
inputs = tokenizer(prompt, return_tensors="pt")

# Generate a response
outputs = model.generate(**inputs, max_new_tokens=150)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

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Limitations

While Astral-1 is a powerful model, it has limitations:

• It may generate incorrect or biased outputs.
• Not suitable for real-time or safety-critical applications.
• Commercial use is prohibited under the Astral Research License (ARL-1.0).

Users are encouraged to validate model outputs for their specific use cases.

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License

Astral-1 is released under the Astral Research License (ARL-1.0). This license is based on the CC-BY-NC 4.0 License, as the model is derived from SOLAR-10.7B-Instruct-v1.0. Key terms include:

• Attribution to both Odyssey Labs and Upstage.
• Non-commercial use only.
• Distribution and derivatives must comply with ARL-1.0.

For full details, please review the license terms.

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Acknowledgments

We would like to extend our gratitude to the Upstage AI team for providing the exceptional SOLAR base model, which made this work possible. Special thanks to the AI research community for their valuable contributions.

Citations

If you use Astral-1 in your research, please cite:

@misc{astral-1,
  author = {Odyssey Labs},
  title = {Astral-1: A step forward in GenAI research},
  year = {2025},
  howpublished = {\url{https://huggingface.co/odyssey-labs/Astral-1-10B}},
  note = {Based on SOLAR-10.7B-Instruct-v1.0 by Upstage.}
}

@misc{kim2023solar,
      title={SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling}, 
      author={Dahyun Kim and Chanjun Park and Sanghoon Kim and Wonsung Lee and Wonho Song and Yunsu Kim and Hyeonwoo Kim and Yungi Kim and Hyeonju Lee and Jihoo Kim and Changbae Ahn and Seonghoon Yang and Sukyung Lee and Hyunbyung Park and Gyoungjin Gim and Mikyoung Cha and Hwalsuk Lee and Sunghun Kim},
      year={2023},
      eprint={2312.15166},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{kim2024sdpo,
      title={sDPO: Don't Use Your Data All at Once}, 
      author={Dahyun Kim and Yungi Kim and Wonho Song and Hyeonwoo Kim and Yunsu Kim and Sanghoon Kim and Chanjun Park},
      year={2024},
      eprint={2403.19270},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

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Contact

For questions, feedback, or licensing inquiries, please contact Odyssey Labs:

• Website: https://odyssey-labs.vercel.app/