miqu-1-70b-sf
| Property | Value |
|---|---|
| Parameter Count | 70B |
| Model Type | LLaMA Architecture |
| License | NOMERGE License |
| Tensor Type | FP16 |
What is miqu-1-70b-sf?
miqu-1-70b-sf is a powerful large language model that represents a dequantized version of the original miqu-1-70b, converted from Q5 to FP16 format and optimized for PyTorch implementation. This model demonstrates exceptional performance across various benchmarks, particularly excelling in reasoning and knowledge-based tasks.
Implementation Details
The model has been specifically optimized with tensor rotations that improve upon previous implementations. It utilizes the LLaMA architecture and requires substantial computational resources for deployment, typically necessitating multiple GPUs for optimal performance.
- Achieves 75.49% accuracy on MMLU (5-shot)
- 88.61% normalized accuracy on HellaSwag (10-shot)
- 67.7% accuracy on GSM8k mathematical reasoning
- 69.38% on TruthfulQA for factual accuracy
Core Capabilities
- Strong performance in reasoning and knowledge tasks
- Excellent results in academic and professional subject matters
- High accuracy in logical reasoning and analysis
- Robust performance in both zero-shot and few-shot scenarios
Frequently Asked Questions
Q: What makes this model unique?
The model's standout feature is its balanced performance across various tasks, particularly its strong showing in both academic (MMLU) and practical reasoning tasks (GSM8k). The unique NOMERGE license also ensures the model's weights remain distinct from other implementations.
Q: What are the recommended use cases?
The model is particularly well-suited for applications requiring strong reasoning capabilities, academic knowledge application, and truthful responses. It performs exceptionally well in scenarios requiring few-shot learning and can handle complex analytical tasks.
