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| 1 |
+
# Helion 1.5 Series 🚀
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+
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+
[](https://creativecommons.org/licenses/by/4.0/)
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[]()
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[]()
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+
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+
## Overview
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+
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+
Helion 1.5 represents a significant advancement over the Helion 1 series, featuring enhanced data quality, broader coverage, and improved structure for training state-of-the-art language models and AI systems.
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+
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+
## What's New in Helion 1.5
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### Major Improvements
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- **50% more diverse training examples** across all domains
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- **Enhanced quality filtering** with multi-stage validation
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- **Better structured formats** optimized for modern architectures
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- **Improved instruction-following data** with chain-of-thought reasoning
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- **Multilingual expansion** covering 30+ languages
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- **Domain-specific subsets** for specialized fine-tuning
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- **Comprehensive metadata** for better dataset management
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### Key Features
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- High-quality conversational data
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- Code generation and debugging examples
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- Mathematical reasoning and problem-solving
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- Creative writing and storytelling
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- Scientific and technical explanations
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- Multilingual translations and cultural context
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- Safety-aligned responses
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## Dataset Structure
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### Core Files
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#### 1. **helion-1.5-conversations.jsonl** (Primary Dataset)
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Conversational data with diverse interactions covering general knowledge, reasoning, and instruction-following.
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```json
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{
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"id": "conv_000001",
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"conversations": [
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{"role": "user", "content": "..."},
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{"role": "assistant", "content": "..."}
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],
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"metadata": {
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"domain": "science",
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"difficulty": "intermediate",
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"languages": ["en"],
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"quality_score": 0.95
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}
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}
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```
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#### 2. **helion-1.5-instructions.jsonl** (Instruction Tuning)
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High-quality instruction-response pairs for instruction fine-tuning.
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```json
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{
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"id": "inst_000001",
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"instruction": "...",
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"input": "...",
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"output": "...",
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"metadata": {
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"task_type": "summarization",
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"complexity": "high",
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"verified": true
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}
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}
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```
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#### 3. **helion-1.5-code.jsonl** (Code & Programming)
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Programming examples, code generation, debugging, and explanations.
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```json
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{
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"id": "code_000001",
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"language": "python",
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"problem": "...",
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"solution": "...",
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"explanation": "...",
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"test_cases": [...],
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"metadata": {
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"difficulty": "medium",
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"tags": ["algorithms", "data-structures"]
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}
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}
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```
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#### 4. **helion-1.5-reasoning.jsonl** (Advanced Reasoning)
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Complex reasoning tasks including math, logic, and multi-step problem solving.
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```json
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{
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"id": "reason_000001",
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"problem": "...",
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"reasoning_steps": [...],
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"final_answer": "...",
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"metadata": {
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"reasoning_type": "mathematical",
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"steps_count": 5
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}
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}
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```
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#### 5. **helion-1.5-creative.jsonl** (Creative Content)
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Stories, poems, creative writing, and artistic content generation.
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#### 6. **helion-1.5-multilingual.jsonl** (Multilingual Data)
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Cross-lingual examples and translations across 30+ languages.
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## Statistics
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| Metric | Helion 1 | Helion 1.5 | Improvement |
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|--------|----------|------------|-------------|
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| Total Examples | 500K | 2M | +300% |
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| Unique Domains | 15 | 40 | +167% |
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| Languages | 10 | 30+ | +200% |
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| Avg Quality Score | 0.82 | 0.91 | +11% |
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| Code Examples | 50K | 250K | +400% |
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| Reasoning Tasks | 30K | 180K | +500% |
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## Usage
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### Loading the Dataset
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```python
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from datasets import load_dataset
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# Load full dataset
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dataset = load_dataset("your-username/helion-1.5")
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# Load specific subset
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conversations = load_dataset("your-username/helion-1.5", data_files="helion-1.5-conversations.jsonl")
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code_data = load_dataset("your-username/helion-1.5", data_files="helion-1.5-code.jsonl")
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```
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### Training Example
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer
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model = AutoModelForCausalLM.from_pretrained("base-model")
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tokenizer = AutoTokenizer.from_pretrained("base-model")
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# Prepare dataset
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def format_conversation(example):
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return tokenizer(
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example["conversations"],
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truncation=True,
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max_length=2048
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)
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train_dataset = dataset.map(format_conversation)
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# Train
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training_args = TrainingArguments(
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output_dir="./helion-1.5-model",
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num_train_epochs=3,
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per_device_train_batch_size=4,
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gradient_accumulation_steps=8,
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learning_rate=2e-5,
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fp16=True,
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)
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=train_dataset,
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)
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trainer.train()
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```
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## Quality Assurance
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Each example in Helion 1.5 has undergone:
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1. **Automated filtering** - Removing duplicates, low-quality, and harmful content
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2. **Format validation** - Ensuring proper structure and completeness
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3. **Quality scoring** - ML-based quality assessment
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4. **Human review** - Spot-checking high-importance subsets
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5. **Safety alignment** - Filtering for ethical and safe responses
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## Ethical Considerations
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- **Privacy**: All data has been screened for PII and sensitive information
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- **Bias**: Efforts made to balance representation across demographics and perspectives
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- **Safety**: Content filtered for harmful, toxic, or dangerous information
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- **Attribution**: Sources properly attributed where applicable
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- **Consent**: Data collected with appropriate permissions
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## Limitations
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- Primarily English-focused (70% of data), though multilingual coverage expanded
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- May contain biases present in source materials
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- Not suitable for high-stakes decision making without human oversight
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- Some specialized domains may have limited coverage
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## Citation
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```bibtex
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@dataset{helion_1_5_2024,
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title={Helion 1.5: An Enhanced Large-Scale Dataset for Language Model Training},
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author={Your Name/Organization},
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year={2024},
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publisher={Hugging Face},
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url={https://huggingface.co/datasets/your-username/helion-1.5}
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}
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```
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## License
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This dataset is released under CC BY 4.0 License. You are free to:
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- Share and redistribute
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- Adapt and build upon
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- Use commercially
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With attribution required.
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## Contact & Support
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- **Issues**: [GitHub Issues](your-repo-link)
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- **Discussions**: [HF Discussions](your-hf-discussions)
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- **Email**: your-email@example.com
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## Acknowledgments
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Thanks to the open-source community and all contributors who made this dataset possible.
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---
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**Version**: 1.5.0
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**Last Updated**: November 2024
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**Status**: Active Development
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