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DeepXR/Helion-V1

sourceHugging Faceapache-2.0updated 11mo agoView on Hugging Face
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<div align="center"> <img src="https://imgur.com/sk6NekE.png" alt="Helion-V1 Logo" width="100%"/> </div>

Helion-V1

Helion-V1 is a conversational AI model designed to be helpful, harmless, and honest. The model focuses on providing assistance to users in a friendly and safe manner, with built-in safeguards to prevent harmful outputs.

Model Description

  • —Developed by: DeepXR
  • —Model type: Causal Language Model
  • —Language(s): English
  • —License: Apache 2.0
  • —Finetuned from: [Troviku-1.1]

Intended Use

Helion-V1 is designed for:

  • —General conversational assistance
  • —Question answering
  • —Creative writing support
  • —Educational purposes
  • —Coding assistance

Direct Use

The model can be used directly for chat-based applications where safety and helpfulness are priorities.

Out-of-Scope Use

This model should NOT be used for:

  • —Generating harmful, illegal, or unethical content
  • —Medical, legal, or financial advice without proper disclaimers
  • —Impersonating individuals or organizations
  • —Creating misleading or false information

Safeguards

Helion-V1 includes safety mechanisms to:

  • —Refuse harmful requests
  • —Avoid generating dangerous content
  • —Maintain respectful and helpful interactions
  • —Protect user privacy and safety

Usage

python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "DeepXR/Helion-V1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "user", "content": "Hello! Can you help me with a question?"}
]

input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt")
output = model.generate(input_ids, max_length=512)
response = tokenizer.decode(output[0], skip_special_tokens=True)
print(response)

Training Details

Training Data

[Information about training data]

Training Procedure

[Information about training procedure, hyperparameters, etc.]

Evaluation

Testing Data & Metrics

[Information about evaluation metrics and results]

Limitations

  • —The model may occasionally generate incorrect information
  • —Performance may vary across different domains
  • —Context window is limited
  • —May reflect biases present in training data

Ethical Considerations

Helion-V1 has been developed with safety as a priority. However, users should:

  • —Verify critical information from reliable sources
  • —Use appropriate content filtering for sensitive applications
  • —Monitor outputs in production environments
  • —Provide proper attributions when using model outputs

Citation

bibtex
@misc{helion-v1,
  author = {DeepXR},
  title = {Helion-V1: A Safe and Helpful Conversational AI},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/DeepXR/Helion-V1}
}

Contact

For questions or issues, please open an issue on the model repository or contact the development team.