Brinebreath-Llama-3.1-70B
| Property | Value |
|---|---|
| Parameter Count | 70B |
| Base Model | LLaMA 3.1 |
| Model URL | https://huggingface.co/gbueno86/Brinebreath-Llama-3.1-70B |
| Quantization | Q4_0 |
What is Brinebreath-Llama-3.1-70B?
Brinebreath-Llama-3.1-70B is an advanced language model created through a sophisticated merger of multiple LLaMA 3.1-based models, including Hermes-3, Dracarys, and SauerkrautLM. The model demonstrates significant improvements over the base LLaMA 3.1 70B, particularly showing a 7% increase in MMLU-PRO performance.
Implementation Details
The model utilizes a carefully crafted merging strategy combining four primary models: Meta-Llama-3.1-70B-Instruct, Hermes-3-Llama-3.1-70B, Dracarys-Llama-3.1-70B-Instruct, and VAGOsolutions/Llama-3.1-SauerkrautLM-70b-Instruct. It operates with specific hyperparameters including a temperature of 0.0 for automated tasks and 0.9 for manual testing, with additional optimization parameters like Top-K (40) and Top-P (0.95) sampling.
- Achieves 49% success rate on MMLU-PRO compared to base model's 42%
- Exceptional performance in Psychology (85%) and Biology (80%) categories
- 71% success rate on PubmedQA, showing strong medical knowledge
- Implements repeat sequence penalization (1.05) with 256 token consideration
Core Capabilities
- Strong performance in professional and academic tasks
- Enhanced reasoning in scientific domains
- Improved programming and technical writing capabilities
- Better performance in common sense reasoning tasks
Frequently Asked Questions
Q: What makes this model unique?
The model's unique strength lies in its merged architecture combining multiple high-performing LLaMA 3.1 variants, resulting in superior performance across various professional and academic benchmarks. It shows particular strength in scientific and medical domains.
Q: What are the recommended use cases?
The model excels in professional and academic applications, particularly in fields like psychology, biology, and economics. It's well-suited for technical writing, scientific analysis, and programming tasks, showing strong capabilities in both automated and manual testing scenarios.
