Nous-Hermes-Llama2-70b
| Property | Value |
|---|---|
| License | MIT |
| Training Data | 300,000+ instructions |
| Base Model | Llama-2 70B |
| Training Infrastructure | 8x H100 80GB GPUs |
What is Nous-Hermes-Llama2-70b?
Nous-Hermes-Llama2-70b is a sophisticated language model developed by Nous Research, representing a significant advancement in AI language capabilities. Built on the Llama-2 architecture, this model has been fine-tuned on an extensive dataset of over 300,000 instructions, primarily derived from GPT-4 outputs. The model was trained with a 4096 sequence length and stands out for its exceptional performance in generating detailed responses while maintaining a lower hallucination rate.
Implementation Details
The model employs advanced training techniques including 4-bit quantization with bitsandbytes, utilizing nf4 quantization type and double quantization. The training process leveraged synthetic data from various high-quality sources, including GPTeacher, Nous Instruct, and multiple specialized datasets.
- Follows Alpaca prompt format for consistency
- Trained using bfloat16 compute dtype
- Implements efficient 4-bit quantization
- Optimized for both general and specialized tasks
Core Capabilities
- Enhanced response length and detail
- Reduced hallucination compared to previous versions
- Strong performance in benchmarks (ARC, AGIEval, BigBench)
- Versatile application from creative writing to technical tasks
Frequently Asked Questions
Q: What makes this model unique?
The model distinguishes itself through its extensive training on GPT-4-generated data, absence of traditional censorship mechanisms, and exceptional performance in generating detailed, accurate responses. Its training on diverse, high-quality datasets enables superior knowledge representation and task completion capabilities.
Q: What are the recommended use cases?
The model excels in various applications including creative text generation, complex instruction following, and technical tasks. It's particularly well-suited for applications requiring detailed responses and lower hallucination rates, making it ideal for both commercial and research applications.
