---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- conversational
- assistant
- safety
- helpful
library_name: transformers
---

<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.