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DeepHermes-3-Llama-3-8B-Pre…/README.md
ModelHub XC 706d255abe 初始化项目,由ModelHub XC社区提供模型
Model: brittlewis12/DeepHermes-3-Llama-3-8B-Preview-GGUF
Source: Original Platform
2026-09-14 16:40:16 +08:00

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base_model, pipeline_tag, inference, language, license, model_creator, model_name, model_type, quantized_by, tags
base_model pipeline_tag inference language license model_creator model_name model_type quantized_by tags
NousResearch/DeepHermes-3-Llama-3-8B-Preview text-generation true
en
llama3 NousResearch DeepHermes-3-Llama-3-8B-Preview llama brittlewis12
reasoning
llama
llama3

DeepHermes 3 Preview GGUF

deephermes3

Original model: DeepHermes-3-Llama-3-8B-Preview

Model creator: NousResearch

DeepHermes 3 Preview is the latest version of our flagship Hermes series of LLMs by Nous Research, and one of the first models in the world to unify Reasoning (long chains of thought that improve answer accuracy) and normal LLM response modes into one model. We have also improved LLM annotation, judgement, and function calling.

This is a preview Hermes with early reasoning capabilities, distilled from R1 across a variety of tasks that benefit from reasoning and objectivity. Some quirks may be discovered! Please let us know any interesting findings or issues you discover!

This repo contains GGUF format model files for the NousResearch’s DeepHermes 3 Preview.

What is GGUF?

GGUF is a file format for representing AI models. It is the third version of the format, introduced by the llama.cpp team on August 21st 2023.

Converted with llama.cpp build 4710 (revision 8a8c4ce), using autogguf-rs.

Prompt template: Llama 3 Chat

<|start_header_id|>system<|end_header_id|>

{{system_prompt}}<|eot_id|><|start_header_id|>user<|end_header_id|>

{{prompt}}<|eot_id|><|start_header_id|>assistant<|end_header_id|>


Reasoning System Prompt

You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the problem and deliberate with yourself via systematic reasoning processes to help come to a correct solution prior to answering. You should enclose your thoughts and internal monologue inside <think> </think> tags, and then provide your solution or response to the problem.

Function calling template

<|start_header_id|>system<|end_header_id|>

You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> {"type": "function", "function": {"name": "myFunc", "description": "myFunc(myParam: myVal) -> str - an explanation of what your function does and when to use it.\\n\\n    Args:\\n        myParam (myVal): explanation of the parameter.\\n\\n    Returns:\\n        str: an explanation of the return value.\\n", "parameters": {"type": "object", "properties": {"myParam": {"type": "myVal"}}, "required": ["myParam"]}}}  </tools> Use the following pydantic model json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
<tool_call>
{"arguments": <args-dict>, "name": <function-name>}
</tool_call><|eot_id|><|start_header_id|>user<|end_header_id|>

{{prompt}}<|eot_id|><|start_header_id|>assistant<|end_header_id|>


Example tool call assistant message

<tool_call>
{"arguments": {"myParam": "myVal"}, "name": "myFunc"}
</tool_call><|eot_id|><|start_header_id|>tool<|end_header_id|>

Example tool response message

<tool_response>
{"name": "myFunc", "content": "myFunc output here (json, string, etc)"}
</tool_response>
<|eot_id|><|start_header_id|>assistant<|end_header_id|>


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Original Model Evaluation

GQPA Diamond and MATH-Hard

deephermes3 vs llama3.1-8b-inst