Hermes-3-Llama-3.1-8B-GGUF
Maintainer: NousResearch
75
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| Property | Value |
|---|---|
| Run this model | Run 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
Hermes-3-Llama-3.1-8B-GGUF is the latest version of the Hermes series of large language models (LLMs) developed by NousResearch. It is a generalist model with advanced capabilities in areas like agentic behavior, roleplaying, reasoning, multi-turn conversation, and long-context coherence. The Hermes series is focused on aligning LLMs to the user, providing powerful steering capabilities and control to the end user.
Model inputs and outputs
Hermes-3-Llama-3.1-8B-GGUF uses the ChatML prompt format, which enables a more structured system for engaging the LLM in multi-turn chat dialogue. This format allows for the use of system prompts, which can guide rules, roles, and stylistic choices for the model.
Inputs
- Text-based prompts in the ChatML format
Outputs
- Text-based responses in the ChatML format
Capabilities
Hermes-3-Llama-3.1-8B-GGUF is competitive, if not superior, to the Llama-3.1 Instruct models in general capabilities. It has improvements across the board, including more powerful and reliable function calling, structured output capabilities, generalist assistant capabilities, and better code generation skills.
What can I use it for?
Hermes-3-Llama-3.1-8B-GGUF can be used for a wide range of natural language processing tasks, such as text generation, summarization, translation, and question answering. Its advanced capabilities make it well-suited for use cases that require agentic behavior, roleplaying, or long-form, coherent responses.
Things to try
Experiment with the ChatML prompt format to explore the model's capabilities in structured, multi-turn dialogue. Try giving the model different personas or roles to see how it adapts its responses. Additionally, test the model's abilities in tasks that require reasoning, long-context understanding, and structured 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!
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