Nous-Hermes-2-Mistral-7B-DPO
Maintainer: NousResearch
147
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| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model overview
The Nous-Hermes-2-Mistral-7B-DPO model is a 7 billion parameter language model developed by NousResearch that has been fine-tuned using Direct Preference Optimization (DPO). This model is an improved version of the Teknium/OpenHermes-2.5-Mistral-7B model, with better performance across a variety of benchmarks including AGIEval, BigBench Reasoning, GPT4All, and TruthfulQA.
The model was trained on 1,000,000 high-quality instruction-following conversations, primarily using synthetic data from GPT-4 as well as other open datasets curated by Nous Research. This has resulted in a versatile model capable of engaging in open-ended dialogue, completing tasks, and generating coherent text across a wide range of domains.
Model inputs and outputs
Inputs
- Text prompts that can be in natural language or structured format (e.g. ChatML)
- The model can accept multi-turn conversations and handle context appropriately
Outputs
- Coherent, contextual text responses
- Ability to generate long-form responses and engage in open-ended dialogue
- Structured outputs like JSON and code, in addition to natural language
Capabilities
The Nous-Hermes-2-Mistral-7B-DPO model demonstrates strong performance across a variety of benchmarks, surpassing the original OpenHermes 2.5 model. For example, the model can engage in detailed discussions about weather patterns, generate nested JSON structures, and roleplay as a Taoist master, showcasing its diverse capabilities.
What can I use it for?
This model can be a valuable tool for a wide range of applications, from content generation to task completion. Potential use cases include:
- Creative writing and storytelling
- Dialogue systems and chatbots
- Code generation and programming assistance
- Data analysis and visualization
- Education and tutoring
- Customer service and support
Things to try
One interesting aspect of this model is its ability to maintain a consistent persona and engage in multi-turn conversations. You could try prompting the model to roleplay as a specific character or entity, and see how it responds and adapts to the context. Additionally, the model's strong performance on structured outputs like JSON could make it useful for building applications that require programmatic interfaces.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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