NeuralBeagle14-7B
| Property | Value |
|---|---|
| Parameter Count | 7.24B |
| Context Window | 8,000 tokens |
| License | CC-BY-NC-4.0 |
| Base Model | Beagle14-7B |
What is NeuralBeagle14-7B?
NeuralBeagle14-7B is a state-of-the-art language model that represents a significant advancement in the 7B parameter category. It's a DPO (Direct Preference Optimization) fine-tuned version of Beagle14-7B, utilizing the argilla/distilabel-intel-orca-dpo-pairs dataset. The model achieves remarkable performance across various benchmarks, including a 72.95% normalized accuracy on AI2 Reasoning Challenge and 64.55% accuracy on MMLU.
Implementation Details
The model is implemented using LazyMergekit, combining UNA-TheBeagle-7b-v1 and distilabeled-Marcoro14-7B-slerp. It supports multiple chat templates including chatml and Llama's chat template, and is available in various quantized versions (GGUF, GPTQ, AWQ, and EXL2).
- Advanced DPO fine-tuning methodology
- 8k context window capability
- Multiple quantization options for different deployment scenarios
- Compatible with various chat templates
Core Capabilities
- Strong performance in instruction following tasks
- Enhanced reasoning capabilities (70.28% accuracy on GSM8k)
- High truthfulness scores (69.93% on TruthfulQA)
- Effective for both general text generation and specialized tasks
- Suitable for RP and storytelling applications
Frequently Asked Questions
Q: What makes this model unique?
NeuralBeagle14-7B stands out for its top-ranking performance in the 7B category on the Open LLM Leaderboard, particularly excelling in reasoning tasks and instruction following while maintaining high truthfulness scores.
Q: What are the recommended use cases?
The model is particularly well-suited for instruction following, reasoning tasks, role-playing, and storytelling. Its 8k context window makes it valuable for tasks requiring longer context understanding.
