Nous-Hermes-2-Mistral-7B-DPO-GGUF
Maintainer: NousResearch
52
🤖
| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
Create account to get full access
Model Overview
The Nous-Hermes-2-Mistral-7B-DPO-GGUF is a text-to-text AI model developed by NousResearch. It is an upgraded and improved version of the Nous-Hermes-2-Mistral-7B model, which was trained on over 1 million high-quality instruction/chat pairs. The model has been further optimized through Direct Preference Optimization (DPO) and is available in a GGUF (llama.cpp) version.
Model Inputs and Outputs
Inputs
- Text prompts for a wide range of tasks, from open-ended conversations to specific instructions
Outputs
- Coherent, contextually relevant text responses to the provided prompts
- The model can generate detailed, multi-paragraph responses covering topics like weather patterns, data visualization, and creative writing
Capabilities
The Nous-Hermes-2-Mistral-7B-DPO-GGUF model has shown significant improvements over the original OpenHermes 2.5 model across a variety of benchmarks, including AGIEval, BigBench Reasoning, GPT4All, and TruthfulQA. It exhibits strong general task and conversational capabilities, as demonstrated by the example outputs.
What Can I Use It For?
This model can be useful for a wide range of applications, such as:
- Enhancing chatbots and virtual assistants with more natural and capable responses
- Generating creative content like stories, poems, and code examples
- Assisting with research and analysis tasks by providing summaries and insights
- Improving language understanding and generation for educational or business applications
Things to Try
One interesting aspect of the Nous-Hermes-2-Mistral-7B-DPO-GGUF model is its use of the ChatML prompt format, which allows for more structured and multi-turn interactions. Experimenting with different system prompts and role-playing scenarios can help unlock the model's potential for tasks like function calling and structured JSON output generation.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🔎
Nous-Hermes-2-Mistral-7B-DPO
147
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.
Updated Invalid Date
🏷️
Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF
55
Nous-Hermes-2-Mixtral-8x7B-DPO is the new flagship model from Nous Research. It is a powerful language model trained on over 1,000,000 entries of high-quality data, including GPT-4 generated content and other open datasets. The model achieves state-of-the-art performance across a variety of benchmarks, including GPT4All, AGIEval, and BigBench. This model is an improvement over the base Mixtral 8x7B MoE LLM and surpasses the flagship Mixtral Finetune model in many areas. It is available in both SFT+DPO and SFT-only versions, allowing users to experiment and find the best fit for their needs. Model Inputs and Outputs Inputs Natural language prompts and instructions Outputs Coherent, contextual text responses to prompts Completion of tasks and generation of content Capabilities The Nous-Hermes-2-Mixtral-8x7B-DPO model demonstrates impressive capabilities in a variety of areas, including: Generating detailed and creative content like data visualizations, cyberpunk poems, and backtranslated prompts Performing well on benchmarks that test reasoning, understanding, and task completion Surpassing previous Mixtral models in areas like GPT4All, AGIEval, and BigBench What Can I Use It For? The Nous-Hermes-2-Mixtral-8x7B-DPO model can be used for a wide range of natural language processing tasks, such as: Content creation (e.g., articles, stories, scripts) Chatbot and virtual assistant development Question answering and knowledge retrieval Task completion (e.g., coding, analysis, problem-solving) Prompt engineering and prompt design Additionally, the model's strong performance on benchmarks indicates its potential usefulness for research and development in the field of artificial intelligence. Things to Try Some ideas to explore with the Nous-Hermes-2-Mixtral-8x7B-DPO model include: Experimenting with the different prompt formats, including the ChatML format, to see how it impacts the model's responses Comparing the SFT+DPO and SFT-only versions to determine which works best for your specific use case Integrating the model into chatbot or virtual assistant applications and observing how it performs in conversational interactions Utilizing the model's capabilities in creative writing or data analysis tasks to see the quality and coherence of the generated content Remember to always verify the URLs provided in the prompt before using any external links or resources.
Updated Invalid Date
🖼️
Nous-Hermes-2-Mixtral-8x7B-DPO
372
Nous-Hermes-2-Mixtral-8x7B-DPO is the new flagship Nous Research model trained over the Mixtral 8x7B MoE LLM. The model was trained on over 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape, achieving state of the art performance on a variety of tasks. This is the SFT + DPO version of Mixtral Hermes 2, with an SFT only version also available. The model was developed in collaboration with Together.ai, who sponsored the compute for the many experiments. Similar models include the Hermes-2-Pro-Mistral-7B and the Nous-Hermes-13B which have their own unique capabilities and use cases. Model inputs and outputs Inputs Natural language prompts for text generation Content for tasks like code generation, summarization, and open-ended conversation Outputs Generated text in response to prompts Structured outputs like JSON for tasks like API interaction Responses to open-ended questions and conversation Capabilities The Nous-Hermes-2-Mixtral-8x7B-DPO model has shown strong performance on a variety of benchmarks, including GPT4All, AGIEval, and BigBench. It demonstrates robust text generation capabilities, as showcased by examples like writing code for data visualization, generating cyberpunk poems, and performing backtranslation. The model also excels at function calling and structured JSON output. What can I use it for? The versatile capabilities of Nous-Hermes-2-Mixtral-8x7B-DPO make it useful for a wide range of applications. Some potential use cases include: Automated content generation (articles, stories, poems, etc.) Code generation and AI-assisted programming Conversational AI assistants for customer service or education Data analysis and visualization Specialized task completion via structured outputs (e.g. APIs, JSON) Things to try One interesting thing to explore with Nous-Hermes-2-Mixtral-8x7B-DPO is its ability to engage in multi-turn conversations using the ChatML prompt format. By leveraging system prompts and roles, you can guide the model's responses and prompt it to take on different personas or styles of interaction. This can unlodge novel and creative outputs. Another avenue to investigate is the model's performance on specialized tasks like function calling and JSON output generation. The maintainers have released evaluation datasets and code to test these capabilities, which could inspire new applications and integrations.
Updated Invalid Date
👁️
Hermes-2-Pro-Mistral-7B-GGUF
209
The Hermes-2-Pro-Mistral-7B-GGUF model is an upgraded version of the Nous Hermes 2 language model, developed by NousResearch. It is a 7 billion parameter model that has been fine-tuned on additional datasets, including a Function Calling and JSON Mode dataset, to improve its capabilities in those areas. Compared to the original Nous Hermes 2 model, this model maintains excellent general task and conversation abilities while also excelling at Function Calling and structured JSON outputs. Model inputs and outputs Inputs Free-form text**: The model can take in free-form text prompts or questions as input. Function call requests**: The model can process function call requests using a specific input format, where the requested function and its arguments are provided in a JSON object. JSON data**: The model can take in JSON data as input and generate structured responses. Outputs General text responses**: The model can generate coherent and contextual text responses to a wide variety of prompts and questions. Function call results**: The model can execute function calls and return the results in a structured format. Structured JSON outputs**: The model can generate JSON outputs that adhere to a specified schema. Capabilities The Hermes-2-Pro-Mistral-7B-GGUF model excels at general language understanding and generation tasks, as well as specialized capabilities such as Function Calling and structured JSON output. It has been trained to reliably execute function calls and generate JSON responses that follow a specific schema, making it well-suited for applications that require these capabilities. What can I use it for? The Hermes-2-Pro-Mistral-7B-GGUF model can be used for a variety of applications, including: Conversational AI**: The model's strong general language abilities make it suitable for building chatbots and virtual assistants that can engage in natural conversations. Task automation**: The model's Function Calling capabilities can be leveraged to automate various tasks, such as data processing, API integration, and report generation. Data visualization**: The model's ability to generate structured JSON outputs can be used to create data visualization tools and dashboards. Knowledge integration**: The model's broad knowledge base can be used to build applications that require integrating and reasoning over different types of information. Things to try One interesting thing to try with the Hermes-2-Pro-Mistral-7B-GGUF model is to explore its ability to handle multi-turn function calls. By using the provided prompt format, you can engage the model in a structured dialogue where it can execute a series of related function calls to solve more complex problems. This can be particularly useful for building applications that require a high degree of interactivity and task-oriented capabilities. Another interesting aspect to explore is the model's performance on specialized tasks, such as code generation, technical writing, or scientific reasoning. The model's strong language understanding and generation abilities, combined with its structured output capabilities, may make it well-suited for these types of applications.
Updated Invalid Date