The Nous Hermes Llama 2 70B represents a significant advancement in large language models, built upon the foundation of Meta's Llama 2 70B architecture. This state-of-the-art model has been extensively fine-tuned using a diverse collection of over 300,000 instruction-based training examples, with a particular emphasis on synthetic data generated by GPT-4.
Training and Architecture
The model's training process leveraged an impressive array of high-quality datasets, incorporating contributions from multiple sources including GPTeacher, Wizard LM, the Nous Research Instruct Dataset, GPT4-LLM, Unnatural Instructions, Airoboros, Camel-AI datasets, and CodeAlpaca. This comprehensive training approach was executed using a sophisticated setup featuring 8x H100 80GB machines, with the model configured for a 4096 sequence length.
The development team at Nous Research utilized the Axolotl framework for model construction, demonstrating their commitment to using cutting-edge tools in the development process. The model adheres to the Alpaca prompt format, making it compatible with existing infrastructure designed for similar models.
Features and Capabilities
One of the most notable characteristics of Nous Hermes Llama 2 70B is its ability to generate extended responses while maintaining a lower hallucination rate compared to similar models. This makes it particularly suitable for applications requiring detailed and accurate outputs. The model stands out for its training approach, which deliberately excluded OpenAI censorship mechanisms, potentially offering more flexibility in appropriate use cases.
The model's capabilities have been rigorously evaluated through multiple benchmark suites, including the GPT4All Suite, BigBench Suite, and AGIEval, though specific performance metrics are not publicly disclosed. This comprehensive testing approach helps validate the model's effectiveness across a wide range of tasks and applications.
Implementation and Accessibility
The model is freely available under the MIT license through the Hugging Face platform, making it accessible to researchers and developers worldwide. For practical implementation, users can interact with the model through various interfaces, including LM Studio, which provides a ChatGPT-style interface for easier interaction.
For developers interested in building applications with the model, several example implementations are available, including an Alpaca Discord Bot and an Alpaca Roleplay Discord Bot, demonstrating the model's versatility in different application contexts.
Looking forward, the Nous Research team has expressed their commitment to continuous improvement, with plans to iterate on data quality and filtering techniques to enhance the model's capabilities further.
References
- Axolotl - The framework used to build the model
- LM Studio - ChatGPT-style interface for model interaction
- Alpaca Discord Bot - Example Discord chatbot implementation
- Alpaca Roleplay Discord Bot - Example roleplay Discord chatbot implementation
