MorningStar
| Model | Average ⬆️ | ARC | HellaSwag | MMLU | TruthfulQA |
|---|---|---|---|---|---|
| NewstaR/Morningstar-13b-hf 📑 | 59.93 | 59.04 | 81.93 | 54.63 | 44.12 |
Model Details
- Model name: MorningStar
- Model type: LLaMa 2 (13 billion parameters)
Intended Use
- Text generation
- Content creation
- Conversational agent
Capabilities
MorningStar is optimized for natural language processing tasks like text generation and dialogue. It can produce fluent, coherent text across a variety of topics.
Limitations
- May generate incorrect or nonsensical text
- Lacks true language understanding
- Potential for generating biased or unsafe content
Training Data
Details on MorningStar's training data are unavailable. It was likely trained on a large corpus of text data scraped from the internet.
Ethical Considerations
- Large language models like MorningStar carry risks around bias, toxicity, and misinformation.
- Model outputs should be monitored and filtered before use in real applications.
- Avoid harmful or unethical prompts.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 50.48 |
| ARC (25-shot) | 59.04 |
| HellaSwag (10-shot) | 81.93 |
| MMLU (5-shot) | 54.63 |
| TruthfulQA (0-shot) | 44.12 |
| Winogrande (5-shot) | 74.51 |
| GSM8K (5-shot) | 15.24 |
| DROP (3-shot) | 23.87 |
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