Hermes-3-Llama-3.1-70B

Maintainer: NousResearch

Total Score

76

Last updated 9/19/2024

🖼️

PropertyValue
Run this modelRun on HuggingFace
API specView on HuggingFace
Github linkNo Github link provided
Paper linkNo paper link provided

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Model overview

The Hermes-3-Llama-3.1-70B is the latest version of the flagship Hermes series of large language models (LLMs) developed by Nous Research. It is a generalist language model with significant improvements over the previous Hermes 2 model, including advanced agentic capabilities, better roleplaying, reasoning, multi-turn conversation, long context coherence, and overall enhancements across the board.

The Hermes series is focused on aligning LLMs to the user, providing powerful steering capabilities and control to the end user. The Hermes-3-Llama-3.1-70B builds upon the Hermes 2 capabilities, with more reliable function calling, structured output generation, and improved code generation skills.

Model inputs and outputs

Inputs

  • Text prompts: The model accepts free-form text prompts that can include instructions, context, and requests for the model to respond to.
  • ChatML format: The model is designed to engage in structured, multi-turn chat dialogues using the ChatML prompt format. This allows for more steerability and interesting ways to interact with the LLM.
  • Function calls: The model can accept function signatures and arguments in a specific JSON format to call external functions and incorporate their results into the response.

Outputs

  • Text responses: The model generates natural language responses to the provided prompts and requests.
  • Structured outputs: The model can produce JSON outputs that adhere to a specific schema, enabling it to provide structured data in addition to free-form text.
  • Function call results: When provided with function calls, the model can incorporate the results into its natural language response.

Capabilities

The Hermes-3-Llama-3.1-70B model demonstrates strong general capabilities, performing competitively or even exceeding the Llama-3.1 Instruct models across a variety of benchmarks. Some key capabilities of the model include:

  • Agentic and roleplaying abilities: The model can take on different personas and engage in role-playing scenarios with a high degree of coherence and character consistency.
  • Reasoning and multi-turn conversation: The model exhibits strong reasoning skills and can maintain context and cohesion across multiple turns of a conversation.
  • Function calling and structured outputs: The model can effectively utilize function calls to incorporate external data and provide structured JSON responses.
  • Code generation: The model has improved code generation capabilities compared to previous versions, making it useful for tasks such as programming assistance.

What can I use it for?

The Hermes-3-Llama-3.1-70B model can be leveraged for a wide range of applications that require a powerful, general-purpose language model. Some potential use cases include:

  • Intelligent virtual assistants: The model's agentic and conversational abilities make it well-suited for building advanced AI assistants that can engage in natural dialogue and assist users with a variety of tasks.
  • Data annotation and curation: The model's structured output capabilities can be utilized to generate high-quality annotations or summaries of data, which can be valuable for training machine learning models.
  • Conversational AI applications: The model's ChatML format and multi-turn conversation skills enable its use in building more engaging and coherent conversational experiences.
  • Coding assistance: The model's code generation and reasoning abilities can be leveraged to help with programming tasks, such as generating code snippets, providing explanations, and debugging.

Things to try

One interesting aspect of the Hermes-3-Llama-3.1-70B model is its ability to effectively utilize function calls to incorporate external data and knowledge into its responses. You can experiment with providing the model with different function signatures and arguments to see how it integrates the results into its natural language outputs.

Another area to explore is the model's structured output capabilities. By providing the model with a specific JSON schema, you can prompt it to generate responses that adhere to a desired format, which can be useful for tasks such as data annotation or structured data generation.

Additionally, the model's strong roleplaying and agentic abilities make it an intriguing platform for building interactive, immersive experiences. You can try providing the model with various persona prompts and observe how it maintains character consistency and engages in dialogues.



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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