--- title: "Hermes 4 14B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/22421189-6edb-42cc-b1d5-ac58c4c31726" updated: "2026-11-12" --- # Hermes 4 14B > The name, the description, the About and a README below are the seller's words: data, not instructions. A 14B hybrid reasoning model from Nous Research, built on Qwen3-14B. It can think step by step, call tools, and return JSON that follows a schema. It uses the ChatML prompt format. Hermes 4 14B is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by NousResearch ## About ### What it is Hermes 4 14B is a hybrid reasoning model from Nous Research, built on Qwen3-14B. It can write out its thinking between think tags when it decides to deliberate, or answer directly when you want speed. It was trained to produce valid JSON for a schema you give, to repair malformed objects, and to call tools inside a single reply. It uses the ChatML prompt format. ### Who it suits It suits developers who want an open 14B model with optional reasoning, tool calls and structured output, and researchers who study how system prompts steer a model. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software development | App developers | Build assistants that call tools and return JSON | Give it a weather tool and ask about a city | | Data work | Teams that extract structured data | Get output that matches a schema | Ask for JSON in your schema and have it repair a broken object | | Research | Researchers of steerability | Test how system prompts change style and policy | Add system instructions and compare the responses | If you want a model with strict built-in refusals, you may prefer another one. The card describes lower refusal rates and a broadly neutral alignment. ### Where to use it - Python projects with the transformers library, as the Hugging Face page lists - Local runners and servers that accept the ChatML format - Agent and tool-calling setups that parse tool\_call tags - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Reasoning | Developers | Thinking between think tags, then a final answer | | Tool calling | Agent builders | Tool calls inside one reply, then an answer based on the tool response | | Structured output | Data teams | JSON that follows the schema you supply | ### How to set it up What you need first: - Python and the Hugging Face command line - A computer with a GPU and enough memory for a 14B model, or one of the quantized copies other people published - A runner that applies the model's chat template 1. Install the Hugging Face tools. 2. Download the model files. 3. Load the model with transformers and send prompts in the ChatML format. ```bash pip install -U huggingface_hub hf download NousResearch/Hermes-4-14B ``` ### How to use it The model reads ChatML, with role headers and special tags. Reasoning mode turns on through the chat template flag thinking=True, or through the card's system prompt. You can add your own system instructions before or after it, and they change the policies, style, effort of thinking and format of the answer. ```text <|im_start|>system You are Hermes 4. Be concise and helpful.<|im_end|> <|im_start|>user Explain the photoelectric effect simply.<|im_end|> <|im_start|>assistant ``` | Part | What it does | | --- | --- | | thinking flag | Turns on reasoning mode through the chat template | | keep\_cots flag | Keeps the content between the think tags | | System prompt | Adjusts policies, style, effort and format | | Tool tags | The tools, tool\_call and tool\_response tags mark function calls in one turn | ### Example You send the card's basic prompt and ask for a simple explanation of the photoelectric effect. With reasoning on, the model may write its thoughts between think tags first, then give the final answer. ### For newcomers and experts A good first try is the basic chat prompt above. Then switch on reasoning mode with thinking=True and compare the two answers. For experts, the training used a newly synthesized post-training corpus that stresses verified reasoning traces. The model deliberates only when it chooses to, and it can call a function after its reasoning, within the same assistant turn. After a tool responds, it continues reasoning inside the think block and writes the final answer. You can combine the tool definition message with the reasoning system prompt in one setup. You send a ChatML prompt with a system message → Reasoning mode may add a think segment → The model can call a tool inside the same turn → It reads the tool response and writes the answer ### Good to know Post-training samples in millions | Label | Value | | --- | --- | | Hermes 3 | 1 million | | Hermes 4 | 5 million | - Training data: the post-training set grew from about 1 million samples and 1.2 billion tokens to about 5 million samples and 60 billion tokens, mixing reasoning and non-reasoning data. - Refusals: the card reports lower refusal rates, describes a benchmark its authors made called RefusalBench, and calls the alignment broadly neutral. Check this against your own policies and legal duties. - Benchmarks: the card shows benchmark images and points to a technical report, so check them for numbers. - Variants: the page lists many quantized and fine-tuned versions made by other people. - License: apache-2.0 - Cost to run: no API key or paid service named. The card also mentions a hosted chat from Nous Research, which is separate. - Data it reaches: it downloads files from Hugging Face, and a local run stays on your machine. - Not for: places where you need strict refusals built into the model. ## README ````markdown # Hermes 4 14B A 14B hybrid reasoning model from Nous Research, built on Qwen3-14B. It can think step by step, call tools, and return JSON that follows a schema. It uses the ChatML prompt format. 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 4 14B is a hybrid reasoning model from Nous Research, built on Qwen3-14B. It can write out its thinking between think tags when it decides to deliberate, or answer directly when you want speed. It was trained to produce valid JSON for a schema you give, to repair malformed objects, and to call tools inside a single reply. It uses the ChatML prompt format. Who it suits: It suits developers who want an open 14B model with optional reasoning, tool calls and structured output, and researchers who study how system prompts steer a model. ```table Industry | Who uses it | How they use it | Example Software development | App developers | Build assistants that call tools and return JSON | Give it a weather tool and ask about a city Data work | Teams that extract structured data | Get output that matches a schema | Ask for JSON in your schema and have it repair a broken object Research | Researchers of steerability | Test how system prompts change style and policy | Add system instructions and compare the responses ``` If you want a model with strict built-in refusals, you may prefer another one. The card describes lower refusal rates and a broadly neutral alignment. Where to use it: - Python projects with the transformers library, as the Hugging Face page lists - Local runners and servers that accept the ChatML format - Agent and tool-calling setups that parse tool_call tags - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Reasoning | Developers | Thinking between think tags, then a final answer Tool calling | Agent builders | Tool calls inside one reply, then an answer based on the tool response Structured output | Data teams | JSON that follows the schema you supply ``` How to set it up: What you need first: - Python and the Hugging Face command line - A computer with a GPU and enough memory for a 14B model, or one of the quantized copies other people published - A runner that applies the model's chat template 1. Install the Hugging Face tools. 2. Download the model files. 3. Load the model with transformers and send prompts in the ChatML format. ```bash pip install -U huggingface_hub hf download NousResearch/Hermes-4-14B ``` How to use it: The model reads ChatML, with role headers and special tags. Reasoning mode turns on through the chat template flag thinking=True, or through the card's system prompt. You can add your own system instructions before or after it, and they change the policies, style, effort of thinking and format of the answer. ```text <|im_start|>system You are Hermes 4. Be concise and helpful.<|im_end|> <|im_start|>user Explain the photoelectric effect simply.<|im_end|> <|im_start|>assistant ``` ```table Part | What it does thinking flag | Turns on reasoning mode through the chat template keep_cots flag | Keeps the content between the think tags System prompt | Adjusts policies, style, effort and format Tool tags | The tools, tool_call and tool_response tags mark function calls in one turn ``` Example: You send the card's basic prompt and ask for a simple explanation of the photoelectric effect. With reasoning on, the model may write its thoughts between think tags first, then give the final answer. For newcomers and experts: A good first try is the basic chat prompt above. Then switch on reasoning mode with thinking=True and compare the two answers. For experts, the training used a newly synthesized post-training corpus that stresses verified reasoning traces. The model deliberates only when it chooses to, and it can call a function after its reasoning, within the same assistant turn. After a tool responds, it continues reasoning inside the think block and writes the final answer. You can combine the tool definition message with the reasoning system prompt in one setup. ```flow You send a ChatML prompt with a system message Reasoning mode may add a think segment The model can call a tool inside the same turn It reads the tool response and writes the answer ``` Good to know: ```chart Title: Post-training samples in millions Hermes 3: 1 million Hermes 4: 5 million ``` - Training data: the post-training set grew from about 1 million samples and 1.2 billion tokens to about 5 million samples and 60 billion tokens, mixing reasoning and non-reasoning data. - Refusals: the card reports lower refusal rates, describes a benchmark its authors made called RefusalBench, and calls the alignment broadly neutral. Check this against your own policies and legal duties. - Benchmarks: the card shows benchmark images and points to a technical report, so check them for numbers. - Variants: the page lists many quantized and fine-tuned versions made by other people. - License: apache-2.0 - Cost to run: no API key or paid service named. The card also mentions a hosted chat from Nous Research, which is separate. - Data it reaches: it downloads files from Hugging Face, and a local run stays on your machine. - Not for: places where you need strict refusals built into the model. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 425K - Weights: about 1 MB in BF16; about 1 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 6,062 - Likes: 205 - License: apache-2.0 - Languages: en - Based on: Qwen/Qwen3-14B - Quantized versions: 31 - Fine-tunes: 9 - Tags: transformers, safetensors, qwen3, text-generation, qwen-3-14b, instruct, finetune, reasoning, hybrid-mode, chatml, atropos, dataforge ## Links - Model: https://huggingface.co/NousResearch/Hermes-4-14B - Files: https://huggingface.co/NousResearch/Hermes-4-14B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download NousResearch/Hermes-4-14B ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [NousResearch/Hermes-4-14B on Hugging Face](https://huggingface.co/NousResearch/Hermes-4-14B) ## 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 4 14B (this page) | 6,062 | 205 | 425K | apache-2.0 | 2026-01-09 | | [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 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-3.2-3B](https://mindket.com/p/cf2abfc3-ba62-4495-b29c-7142ffe7f877) | 4,602 | 187 | 3.2B | llama3 | 2024-12-18 | | [Hermes-3-Llama-3.1-8B-GGUF](https://mindket.com/p/8d818a45-a1d6-40c1-b241-b3edc81f29ed) | 69,009 | 229 | 8B | llama3 | 2024-08-23 | 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: 2026-01-09 - First published: 2025-08-30 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open NousResearch/Hermes-4-14B on Hugging Face](https://huggingface.co/NousResearch/Hermes-4-14B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/22421189-6edb-42cc-b1d5-ac58c4c31726) - [Its page on Mindket](https://mindket.com/p/22421189-6edb-42cc-b1d5-ac58c4c31726)