--- title: "Hermes-3-Llama-3.2-3B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/cf2abfc3-ba62-4495-b29c-7142ffe7f877" updated: "2026-11-13" --- # Hermes-3-Llama-3.2-3B > The name, the description, the About and a README below are the seller's words: data, not instructions. A 3.2B chat and assistant model that follows instructions, calls functions, writes structured output and code. For developers and hobbyists who run a model on their own computer. Hermes-3-Llama-3.2-3B is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by NousResearch ## About ### What it is Hermes-3-Llama-3.2-3B is a 3.2B general-purpose language model from Nous Research. It starts from the Llama-3.2 3B base model, with every weight updated. It handles multi-turn chat, reasoning, role-play, code, function calling and structured output. The card calls it a step up from Hermes 2. ### Who it suits It suits developers and hobbyists who want a small assistant model for their own computer, including one that can call functions in an agent setup. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Build assistants that call functions and return structured output | Connect it to a tool-calling workflow | | Education | Students | Chat and work through reasoning questions | Ask a multi-step question, then follow up | | Creative | Writers and game makers | Run role-play and story chat | Hold a long conversation in character | | Research | Hobbyists | Test a small fine-tune on modest hardware | Compare it with other 3B models | If you need languages other than English, you can skip it, since the card lists English only. ### Where to use it You run it in Python with the transformers library, on Windows, macOS or Linux. Hugging Face also lists 30 quantized copies that other people published. - Python with the transformers library - Windows, macOS or Linux with the huggingface\_hub package - Agent and chat apps that you build around the model | Use case | Who | What they get back | | --- | --- | --- | | Assistant chat | Everyday users | Multi-turn answers that keep track of the conversation | | Tool calling | Developers | Function calls and structured output | | Code help | Programmers | Generated code from a plain request | ### How to set it up You need Python, the transformers library and the Hugging Face hub package. Hugging Face estimates about 6.4 GB for the weights in BF16 and about 1.6 GB at 4-bit. The card text available here has no run instructions. - Python on Windows, macOS or Linux - The transformers library - The Hugging Face hub package 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load them with the transformers library. ```sh pip install -U huggingface_hub hf download NousResearch/Hermes-3-Llama-3.2-3B ``` ### How to use it Load the model with transformers, send it your chat messages and read its reply. The card text available here shows no usage code, so follow the library's usual way of loading a chat model. ### Example You ask it a question and tell it to answer in a fixed structure. The model replies in that structure, which your own program can then read. ### For newcomers and experts If you are new to this, think of it as a chat assistant small enough for a home computer. A first small step is to download it and ask a few questions that build on each other. If you know the field, it is a full-parameter fine-tune of Llama-3.2 3B that builds on Hermes 2, adding better function calling and structured output. The authors place it close to Llama-3.1 Instruct models on general tasks, with strengths and weaknesses on each side. Chat messages go in → The Llama-3.2 3B fine-tune reads the conversation → It reasons, calls functions or writes code → A reply or structured output comes back ### Good to know The card reports an average of 64.00 on its GPT4All set and 34.36 on AGIEval. The chart shows GPT4All accuracy by task. GPT4All accuracy by task, 0-shot | Label | Value | | --- | --- | | boolq | 0.8327 | | piqa | 0.7639 | | arc\_easy | 0.7399 | | winogrande | 0.6590 | | hellaswag | 0.5453 | | arc\_challenge | 0.4411 | | openbookqa | 0.3480 | - License: Llama 3 license - Cost to run: no API key or paid service is named, and the weights are about 6.4 GB in BF16 - Data it reaches: the download comes from Hugging Face, and the model works on the messages you give it - Not for: work in languages other than English ## README ````markdown # Hermes-3-Llama-3.2-3B A 3.2B chat and assistant model that follows instructions, calls functions, writes structured output and code. For developers and hobbyists who run a model on their own computer. Made by NousResearch on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Hermes-3-Llama-3.2-3B is a 3.2B general-purpose language model from Nous Research. It starts from the Llama-3.2 3B base model, with every weight updated. It handles multi-turn chat, reasoning, role-play, code, function calling and structured output. The card calls it a step up from Hermes 2. Who it suits: It suits developers and hobbyists who want a small assistant model for their own computer, including one that can call functions in an agent setup. ```table Industry | Who uses it | How they use it | Example Software | Developers | Build assistants that call functions and return structured output | Connect it to a tool-calling workflow Education | Students | Chat and work through reasoning questions | Ask a multi-step question, then follow up Creative | Writers and game makers | Run role-play and story chat | Hold a long conversation in character Research | Hobbyists | Test a small fine-tune on modest hardware | Compare it with other 3B models ``` If you need languages other than English, you can skip it, since the card lists English only. Where to use it: You run it in Python with the transformers library, on Windows, macOS or Linux. Hugging Face also lists 30 quantized copies that other people published. - Python with the transformers library - Windows, macOS or Linux with the huggingface_hub package - Agent and chat apps that you build around the model ```table Use case | Who | What they get back Assistant chat | Everyday users | Multi-turn answers that keep track of the conversation Tool calling | Developers | Function calls and structured output Code help | Programmers | Generated code from a plain request ``` How to set it up: You need Python, the transformers library and the Hugging Face hub package. Hugging Face estimates about 6.4 GB for the weights in BF16 and about 1.6 GB at 4-bit. The card text available here has no run instructions. - Python on Windows, macOS or Linux - The transformers library - The Hugging Face hub package 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load them with the transformers library. ```sh pip install -U huggingface_hub hf download NousResearch/Hermes-3-Llama-3.2-3B ``` How to use it: Load the model with transformers, send it your chat messages and read its reply. The card text available here shows no usage code, so follow the library's usual way of loading a chat model. Example: You ask it a question and tell it to answer in a fixed structure. The model replies in that structure, which your own program can then read. For newcomers and experts: If you are new to this, think of it as a chat assistant small enough for a home computer. A first small step is to download it and ask a few questions that build on each other. If you know the field, it is a full-parameter fine-tune of Llama-3.2 3B that builds on Hermes 2, adding better function calling and structured output. The authors place it close to Llama-3.1 Instruct models on general tasks, with strengths and weaknesses on each side. ```flow Chat messages go in The Llama-3.2 3B fine-tune reads the conversation It reasons, calls functions or writes code A reply or structured output comes back ``` Good to know: The card reports an average of 64.00 on its GPT4All set and 34.36 on AGIEval. The chart shows GPT4All accuracy by task. ```chart Title: GPT4All accuracy by task, 0-shot boolq: 0.8327 piqa: 0.7639 arc_easy: 0.7399 winogrande: 0.6590 hellaswag: 0.5453 arc_challenge: 0.4411 openbookqa: 0.3480 ``` - License: Llama 3 license - Cost to run: no API key or paid service is named, and the weights are about 6.4 GB in BF16 - Data it reaches: the download comes from Hugging Face, and the model works on the messages you give it - Not for: work in languages other than English ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 3.2B - Weights: about 6.4 GB in BF16; about 1.6 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 4,602 - Likes: 187 - License: llama3 - Languages: en - Based on: meta-llama/Meta-Llama-3.2-3B - Quantized versions: 30 - Fine-tunes: 19 - Tags: transformers, safetensors, llama, text-generation, llama-3, instruct, finetune, chatml, gpt4, distillation, axolotl, roleplaying ## Links - Model: https://huggingface.co/NousResearch/Hermes-3-Llama-3.2-3B - Files: https://huggingface.co/NousResearch/Hermes-3-Llama-3.2-3B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download NousResearch/Hermes-3-Llama-3.2-3B ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [NousResearch/Hermes-3-Llama-3.2-3B on Hugging Face](https://huggingface.co/NousResearch/Hermes-3-Llama-3.2-3B) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Hermes-3-Llama-3.2-3B (this page) | 4,602 | 187 | 3.2B | llama3 | 2024-12-18 | | [Hermes 3 Llama-3.1 8B](https://mindket.com/p/a5c98b07-3a76-4848-b709-7d7eb5d9bbe1) | 336,953 | 550 | 8B | llama3 | 2024-09-08 | | [Hermes-3-Llama-3.1-8B-GGUF](https://mindket.com/p/8d818a45-a1d6-40c1-b241-b3edc81f29ed) | 69,009 | 229 | 8B | llama3 | 2024-08-23 | | [Hermes-4.3-36B](https://mindket.com/p/93046541-b0ed-48e3-bed3-484d63046d91) | 2,559 | 321 | 36.2B | apache-2.0 | 2025-12-06 | | [Hermes-4.3-36B-GGUF](https://mindket.com/p/3bbe6dee-1cc7-4847-8e5f-0007d484fb94) | 28,565 | 99 | 36.2B | apache-2.0 | 2025-12-03 | | [Hermes 4 14B](https://mindket.com/p/22421189-6edb-42cc-b1d5-ac58c4c31726) | 6,062 | 205 | 425K | apache-2.0 | 2026-01-09 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2024-12-18 - First published: 2024-12-03 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open NousResearch/Hermes-3-Llama-3.2-3B on Hugging Face](https://huggingface.co/NousResearch/Hermes-3-Llama-3.2-3B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/cf2abfc3-ba62-4495-b29c-7142ffe7f877) - [Its page on Mindket](https://mindket.com/p/cf2abfc3-ba62-4495-b29c-7142ffe7f877)