kevin009/llamaRAGdrama
kevin009/llamaRAGdrama is a 7 billion parameter model fine-tuned for Question Answering (Q&A) and Retrieval Augmented Generation (RAG) tasks. It is designed to synthesize text content while maintaining factual reliability, even when processing dramatic or creative inputs. The model achieves an average score of 74.65 on the Open LLM Leaderboard, demonstrating strong performance across various reasoning and language understanding benchmarks. Its primary strength lies in generating factually grounded responses within RAG and Q&A applications.
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kevin009/llamaRAGdrama: Factually Reliable RAG and Q&A
kevin009/llamaRAGdrama is a 7 billion parameter model specifically fine-tuned for Question Answering (Q&A) and Retrieval Augmented Generation (RAG). Its core objective is to synthesize text content that remains factual and reliable, even when dealing with dramatic or creative scenarios.
Key Capabilities
- Factual Reliability: Trained on a diverse dataset including dramatic texts and factual databases, ensuring generated content adheres to real-world facts.
- Q&A and RAG Optimization: Designed for applications requiring accurate information retrieval and synthesis.
- Balanced Training: Data preprocessing distinguished between creative and fact-dependent elements to achieve a balanced output.
Performance Highlights
Evaluated on the Open LLM Leaderboard, kevin009/llamaRAGdrama achieved an average score of 74.65. Notable benchmark results include:
- AI2 Reasoning Challenge (25-Shot): 72.01
- HellaSwag (10-Shot): 88.83
- MMLU (5-Shot): 64.50
- TruthfulQA (0-shot): 70.24
- Winogrande (5-shot): 86.66
- GSM8k (5-shot): 65.66
Intended Use Cases
This model is ideal for:
- Retrieval Augmented Generation (RAG) systems: Where factual accuracy is paramount.
- Question Answering (Q&A) applications: Providing reliable answers from given contexts.
Limitations
While designed for truthfulness, the model may still exhibit biases and inaccuracies. Users should be aware that despite its factual grounding, it may not always be considered entirely "safe" in all content generation scenarios.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.