--- title: "zephyr-7b-beta" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/6a62079c-59f4-44d0-887f-0e2288c7f859" updated: "2026-11-13" --- # zephyr-7b-beta > The name, the description, the About and a README below are the seller's words: data, not instructions. Chats as a helpful assistant, built by tuning Mistral-7B with direct preference optimization. For developers and hobbyists who want a small open chat model they can run themselves. zephyr-7b-beta is a kit in the Productivity 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 HuggingFaceH4 ## About ### What it is Zephyr-7B-beta is the second model in HuggingFaceH4's Zephyr series of chat assistants. It is a 7.2 billion parameter fine-tune of Mistral-7B-v0.1, trained on public synthetic datasets with direct preference optimization (DPO). The card says removing the built-in alignment from those datasets helped its scores and made it more helpful. ### Who it suits It suits people who want a compact open chat model, from developers adding a self-hosted assistant to researchers studying how DPO shapes a model. The record lists English. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | App developers | Add a small chat assistant they host themselves | A support bot with a custom persona | | Research | Alignment researchers | Study a model tuned with DPO on public synthetic data | Compare it with its alpha predecessor | | Hobby | Local model users | Chat with a 7B model on their own machine | Ask everyday questions in a chosen style | People who need strong coding or maths help can skip it, since the card says it trails proprietary models on those tasks. ### Where to use it - Python with the Hugging Face transformers library, on Windows, macOS or Linux - A hosted demo chat that the model card points to - Your own machine, since the weights are about 14.5 GB in BF16 or an estimated 3.6 GB at 4-bit | Use case | Who | What they get back | | --- | --- | --- | | General chat | Hobbyists | Helpful answers in conversation | | Persona bots | Developers | Replies in a style set by a system message | | Preference tuning research | Researchers | A reference model trained with DPO on UltraFeedback | ### How to set it up You need a few things first. - Python and pip - The transformers library and torch - Memory or disk for the weights, about 14.5 GB in BF16 1. Install the Hugging Face hub tool with pip. 2. Download the model files to your computer. 3. Load the model in Python with the transformers pipeline, as the card shows. ```sh pip install -U huggingface_hub hf download HuggingFaceH4/zephyr-7b-beta ``` ### How to use it You build a pipeline for text generation, then pass a list of messages. A system message sets the persona and a user message holds the question. The card formats them with the tokenizer's chat template before generating. ```python import torch from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto") messages = [ { "role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate", }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] ``` | Tool | What it does | | --- | --- | | pipeline() | Loads the model for text generation in transformers | | torch\_dtype=torch.bfloat16 | Loads the weights in BF16 | | device\_map="auto" | Lets the library place the model on available devices | | messages list | Holds the system persona and the user question | | Chat template | Formats the messages the way the model expects | ### Example You set the system message to a friendly chatbot that answers like a pirate, then ask how many helicopters a human can eat in one sitting. The model returns a text reply, which you should expect in that pirate style. ### For newcomers and experts If you are new, a chat model is a program that answers typed questions. Download the files, run the pipeline example above, and change the system message to see how the reply style changes. Underneath, the model was first fine-tuned on a filtered version of UltraChat, a set of synthetic dialogues made by ChatGPT. It was then aligned with the TRL DPOTrainer on UltraFeedback, which holds 64k prompts with completions ranked by GPT-4. The card reports an MT-Bench score of 7.34 and an AlpacaEval win rate of 90.60 percent. Mistral-7B-v0.1 base model → Fine-tune on filtered UltraChat dialogues → DPO on UltraFeedback rankings → Chat through the tokenizer template ### Good to know - License: MIT. - Cost to run: no API key or paid service named. You supply the hardware, and the weights download from Hugging Face. - Data it reaches: only the prompts you pass in. The card names no file, web or account access. - Not for: complex coding and maths, where the card says it trails proprietary models. The card also warns it is likely to produce problematic text when asked to. MT-Bench score at release | Label | Value | | --- | --- | | Mistral-Instructv0.1 | 6.84 points | | Zephyr-7b-alpha | 6.88 points | | Llama2-Chat 70B | 6.86 points | | Zephyr-7b-beta | 7.34 points | | GPT-3.5-turbo | 7.94 points | | GPT-4 | 8.99 points | ## README ````markdown # zephyr-7b-beta Chats as a helpful assistant, built by tuning Mistral-7B with direct preference optimization. For developers and hobbyists who want a small open chat model they can run themselves. Made by HuggingFaceH4 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: Zephyr-7B-beta is the second model in HuggingFaceH4's Zephyr series of chat assistants. It is a 7.2 billion parameter fine-tune of Mistral-7B-v0.1, trained on public synthetic datasets with direct preference optimization (DPO). The card says removing the built-in alignment from those datasets helped its scores and made it more helpful. Who it suits: It suits people who want a compact open chat model, from developers adding a self-hosted assistant to researchers studying how DPO shapes a model. The record lists English. ```table Industry | Who uses it | How they use it | Example Software | App developers | Add a small chat assistant they host themselves | A support bot with a custom persona Research | Alignment researchers | Study a model tuned with DPO on public synthetic data | Compare it with its alpha predecessor Hobby | Local model users | Chat with a 7B model on their own machine | Ask everyday questions in a chosen style ``` People who need strong coding or maths help can skip it, since the card says it trails proprietary models on those tasks. Where to use it: - Python with the Hugging Face transformers library, on Windows, macOS or Linux - A hosted demo chat that the model card points to - Your own machine, since the weights are about 14.5 GB in BF16 or an estimated 3.6 GB at 4-bit ```table Use case | Who | What they get back General chat | Hobbyists | Helpful answers in conversation Persona bots | Developers | Replies in a style set by a system message Preference tuning research | Researchers | A reference model trained with DPO on UltraFeedback ``` How to set it up: You need a few things first. - Python and pip - The transformers library and torch - Memory or disk for the weights, about 14.5 GB in BF16 1. Install the Hugging Face hub tool with pip. 2. Download the model files to your computer. 3. Load the model in Python with the transformers pipeline, as the card shows. ```sh pip install -U huggingface_hub hf download HuggingFaceH4/zephyr-7b-beta ``` How to use it: You build a pipeline for text generation, then pass a list of messages. A system message sets the persona and a user message holds the question. The card formats them with the tokenizer's chat template before generating. ```python import torch from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto") messages = [ { "role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate", }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] ``` ```table Tool | What it does pipeline() | Loads the model for text generation in transformers torch_dtype=torch.bfloat16 | Loads the weights in BF16 device_map="auto" | Lets the library place the model on available devices messages list | Holds the system persona and the user question Chat template | Formats the messages the way the model expects ``` Example: You set the system message to a friendly chatbot that answers like a pirate, then ask how many helicopters a human can eat in one sitting. The model returns a text reply, which you should expect in that pirate style. For newcomers and experts: If you are new, a chat model is a program that answers typed questions. Download the files, run the pipeline example above, and change the system message to see how the reply style changes. Underneath, the model was first fine-tuned on a filtered version of UltraChat, a set of synthetic dialogues made by ChatGPT. It was then aligned with the TRL DPOTrainer on UltraFeedback, which holds 64k prompts with completions ranked by GPT-4. The card reports an MT-Bench score of 7.34 and an AlpacaEval win rate of 90.60 percent. ```flow Mistral-7B-v0.1 base model Fine-tune on filtered UltraChat dialogues DPO on UltraFeedback rankings Chat through the tokenizer template ``` Good to know: - License: MIT. - Cost to run: no API key or paid service named. You supply the hardware, and the weights download from Hugging Face. - Data it reaches: only the prompts you pass in. The card names no file, web or account access. - Not for: complex coding and maths, where the card says it trails proprietary models. The card also warns it is likely to produce problematic text when asked to. ```chart Title: MT-Bench score at release Mistral-Instructv0.1: 6.84 points Zephyr-7b-alpha: 6.88 points Llama2-Chat 70B: 6.86 points Zephyr-7b-beta: 7.34 points GPT-3.5-turbo: 7.94 points GPT-4: 8.99 points ``` ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 7.2B - Weights: about 14.5 GB in BF16; about 3.6 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 60,487 - Likes: 1,868 - License: mit - Languages: en - Based on: mistralai/Mistral-7B-v0.1 - Quantized versions: 37 - Fine-tunes: 165 - Tags: transformers, pytorch, safetensors, mistral, text-generation, generated_from_trainer, conversational, model-index ## Links - Model: https://huggingface.co/HuggingFaceH4/zephyr-7b-beta - Files: https://huggingface.co/HuggingFaceH4/zephyr-7b-beta/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download HuggingFaceH4/zephyr-7b-beta ``` 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 [HuggingFaceH4/zephyr-7b-beta on Hugging Face](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) ## 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 | | --- | --- | --- | --- | --- | --- | | zephyr-7b-beta (this page) | 60,487 | 1,868 | 7.2B | mit | 2024-10-16 | | [Dulu Malayalam ASR](https://mindket.com/p/cde57ebb-917a-4a9c-baf4-a4a12a025505) | 71 | 2 | — | apache-2.0 | 2026-10-15 | | [GigaChat3.1-10B-A1.8B-GGUF](https://mindket.com/p/3e03c635-d97c-4ad9-9c9e-80af78adeb40) | 19,408 | 95 | 10.7B | mit | 2026-03-25 | | [LFM2.5-350M-home-assistant-sft](https://mindket.com/p/017243ad-59ae-4d9a-aa08-3283bdb44d99) | 2,229 | 8 | 353M | apache-2.0 | 2026-06-28 | | [whisper-large-v3](https://mindket.com/p/6b152b5d-9ebc-46b8-9e49-73c53c31a48d) | 3,489,398 | 6,802 | 1.5B | apache-2.0 | 2024-08-12 | | [MiMo-V2.5-ASR](https://mindket.com/p/102d0907-6ace-4af9-8458-8419f2835765) | 4,626 | 117 | 7.6B | mit | 2026-10-28 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · minimaxir · 2024-03-23](https://news.ycombinator.com/item?id=39801087): Emad Mostaque resigned as CEO of Stability AI: Zephyr: https://huggingface.co/HuggingFaceH4/zephyr-7b-beta Their platform for easy distribution and management of models has sped up the ecosystem more so. - [Hacker News · minimaxir · 2023-12-10](https://news.ycombinator.com/item?id=38594350): Mistral AI Valued at $2B: It's IMO a pedantic distinction. A compiled binary is a bad metaphor because it gives the implication that Mistral-7B is an as-is WYSIWIG project that's not easily modifiable. In contrast, there have been a bunch of new powerful new models created by modifying or finetuning Mistral-7B such as… - [Hacker News · smarvin2 · 2023-12-06](https://news.ycombinator.com/item?id=38547557): Swap OpenAI with any open-source model: Hey guys, We built an open-source AI SDK (Python \& JavaScript) that provides a drop-in replacement for OpenAI’s chat completion endpoint. We'd love to know what you think so we can make switching as easy as possible and get more folks on open-source. You can swap in almost any open-source model on… ## 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: 2024-10-16 - First published: 2023-10-26 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open HuggingFaceH4/zephyr-7b-beta on Hugging Face](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/6a62079c-59f4-44d0-887f-0e2288c7f859) - [Its page on Mindket](https://mindket.com/p/6a62079c-59f4-44d0-887f-0e2288c7f859)