--- title: "Mistral-7B-Instruct-v0.1" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f36c3d38-c1ea-4846-a79e-4a1f8e2a132c" updated: "2026-11-13" --- # Mistral-7B-Instruct-v0.1 > The name, the description, the About and a README below are the seller's words: data, not instructions. A 7B instruction-tuned chat model from Mistral, fine-tuned from Mistral-7B-v0.1 on public conversation datasets. Made for developers who want a small open model that follows prompts. Mistral-7B-Instruct-v0.1 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 mistralai ## About ### What it is This model takes the Mistral-7B-v0.1 text generator and fine-tunes it to follow instructions, using several public conversation datasets. It has 7.2B parameters. The card presents it as a fast proof that the base model tunes well. Its design uses grouped-query attention, sliding-window attention and a byte-fallback BPE tokenizer. ### Who it suits It suits developers and researchers who want a small open chat model to test prompts, build prototypes or fine-tune further. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | App developers | Prototype a chat assistant on an open 7B model | A help bot for a small product | | Research | NLP researchers | Use it as a baseline for fine-tuning or comparison | Comparing a tuned variant with this one | | Education | Students | Learn how instruction formats and chat templates work | Building a prompt with the instruction tags | | Data work | Analysts | Draft or rewrite short texts locally | Rewriting a short note in a plainer tone | Skip it if you need a current, strongly aligned assistant, since the card presents this release as a demonstration. ### Where to use it - Python scripts using the transformers library - Python scripts using the mistral\_inference library - Windows, macOS and Linux, with a GPU for the card's examples | Use case | Who | What they get back | | --- | --- | --- | | Chat replies | Developers | A written answer to a prompt | | Prompt experiments | Researchers | Output you can compare across settings | | Fine-tuning base | Developers | A starting point for a custom model | ### How to set it up - Python, with transformers, and mistral\_common for the card's tokenizer examples - About 14.5 GB of disk space for the BF16 weights, or about 3.6 GB at 4-bit as an estimate - A GPU, since the card's example moves the model to cuda - If an unknown model type error appears, use a newer transformers release, 4.33.4 or later 1. Install the Hugging Face tools. 2. Download the model files to your computer. 3. Load the model with transformers and send it a prompt. ```sh pip install -U huggingface_hub hf download mistralai/Mistral-7B-Instruct-v0.1 ``` ### How to use it You wrap each instruction in the INST tags, starting the first one with a begin-of-sentence token. The simplest way is the chat template in transformers, which builds that format from a list of messages. The card also shows the mistral\_common tokenizer. ```python from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.protocol.instruct.messages import UserMessage from mistral_common.protocol.instruct.request import ChatCompletionRequest mistral_models_path = "MISTRAL_MODELS_PATH" tokenizer = MistralTokenizer.v1() completion_request = ChatCompletionRequest(messages=[UserMessage(content="Explain Machine Learning to me in a nutshell.")]) tokens = tokenizer.encode_chat_completion(completion_request).tokens ``` ```python from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") model.to("cuda") generated_ids = model.generate(tokens, max_new_tokens=1000, do_sample=True) # decode with mistral tokenizer result = tokenizer.decode(generated_ids[0].tolist()) print(result) ``` ```text text = "[INST] What is your favourite condiment? [/INST]" ``` | Tool | What it does | | --- | --- | | mistral\_common | Encodes a chat request into tokens with the reference tokenizer | | mistral\_inference | Loads the model from a folder and generates tokens | | transformers | Loads the model and generates text with generate | | apply\_chat\_template | Builds the INST format from a list of messages | ### Example You ask the model for a short explanation of machine learning, as the card does. The script encodes the request, the model generates up to 1000 new tokens, and the script decodes and prints the reply. ### For newcomers and experts This is a small chat model that expects a particular prompt shape. A good first try is the card's nutshell question, run through the transformers code. If you know the field, the card notes that the transformers tokenizer may not match the mistral\_common reference exactly, and it invites fixes. The model uses grouped-query and sliding-window attention. The first instruction starts with a begin-of-sentence id, and the assistant reply ends with an end-of-sentence id. Wrap the instruction in INST tags → Encode the prompt into tokens → Model generates new tokens → Decode the tokens into text ### Good to know - Format: the first instruction starts with a begin-of-sentence id and later ones do not. - Tokenizer: the transformers tokenizer may differ from the mistral\_common reference. - Troubleshooting: a KeyError for mistral means an older transformers, which 4.33.4 or later fixes. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: it downloads model files from Hugging Face and works on the text you give it. - Not for: a polished production assistant, since the card calls it a quick demonstration. ## README ````markdown # Mistral-7B-Instruct-v0.1 A 7B instruction-tuned chat model from Mistral, fine-tuned from Mistral-7B-v0.1 on public conversation datasets. Made for developers who want a small open model that follows prompts. Made by mistralai 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: This model takes the Mistral-7B-v0.1 text generator and fine-tunes it to follow instructions, using several public conversation datasets. It has 7.2B parameters. The card presents it as a fast proof that the base model tunes well. Its design uses grouped-query attention, sliding-window attention and a byte-fallback BPE tokenizer. Who it suits: It suits developers and researchers who want a small open chat model to test prompts, build prototypes or fine-tune further. ```table Industry | Who uses it | How they use it | Example Software | App developers | Prototype a chat assistant on an open 7B model | A help bot for a small product Research | NLP researchers | Use it as a baseline for fine-tuning or comparison | Comparing a tuned variant with this one Education | Students | Learn how instruction formats and chat templates work | Building a prompt with the instruction tags Data work | Analysts | Draft or rewrite short texts locally | Rewriting a short note in a plainer tone ``` Skip it if you need a current, strongly aligned assistant, since the card presents this release as a demonstration. Where to use it: - Python scripts using the transformers library - Python scripts using the mistral_inference library - Windows, macOS and Linux, with a GPU for the card's examples ```table Use case | Who | What they get back Chat replies | Developers | A written answer to a prompt Prompt experiments | Researchers | Output you can compare across settings Fine-tuning base | Developers | A starting point for a custom model ``` How to set it up: - Python, with transformers, and mistral_common for the card's tokenizer examples - About 14.5 GB of disk space for the BF16 weights, or about 3.6 GB at 4-bit as an estimate - A GPU, since the card's example moves the model to cuda - If an unknown model type error appears, use a newer transformers release, 4.33.4 or later 1. Install the Hugging Face tools. 2. Download the model files to your computer. 3. Load the model with transformers and send it a prompt. ```sh pip install -U huggingface_hub hf download mistralai/Mistral-7B-Instruct-v0.1 ``` How to use it: You wrap each instruction in the INST tags, starting the first one with a begin-of-sentence token. The simplest way is the chat template in transformers, which builds that format from a list of messages. The card also shows the mistral_common tokenizer. ```python from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.protocol.instruct.messages import UserMessage from mistral_common.protocol.instruct.request import ChatCompletionRequest mistral_models_path = "MISTRAL_MODELS_PATH" tokenizer = MistralTokenizer.v1() completion_request = ChatCompletionRequest(messages=[UserMessage(content="Explain Machine Learning to me in a nutshell.")]) tokens = tokenizer.encode_chat_completion(completion_request).tokens ``` ```python from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") model.to("cuda") generated_ids = model.generate(tokens, max_new_tokens=1000, do_sample=True) # decode with mistral tokenizer result = tokenizer.decode(generated_ids[0].tolist()) print(result) ``` ```text text = "[INST] What is your favourite condiment? [/INST]" ``` ```table Tool | What it does mistral_common | Encodes a chat request into tokens with the reference tokenizer mistral_inference | Loads the model from a folder and generates tokens transformers | Loads the model and generates text with generate apply_chat_template | Builds the INST format from a list of messages ``` Example: You ask the model for a short explanation of machine learning, as the card does. The script encodes the request, the model generates up to 1000 new tokens, and the script decodes and prints the reply. For newcomers and experts: This is a small chat model that expects a particular prompt shape. A good first try is the card's nutshell question, run through the transformers code. If you know the field, the card notes that the transformers tokenizer may not match the mistral_common reference exactly, and it invites fixes. The model uses grouped-query and sliding-window attention. The first instruction starts with a begin-of-sentence id, and the assistant reply ends with an end-of-sentence id. ```flow Wrap the instruction in INST tags Encode the prompt into tokens Model generates new tokens Decode the tokens into text ``` Good to know: - Format: the first instruction starts with a begin-of-sentence id and later ones do not. - Tokenizer: the transformers tokenizer may differ from the mistral_common reference. - Troubleshooting: a KeyError for mistral means an older transformers, which 4.33.4 or later fixes. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: it downloads model files from Hugging Face and works on the text you give it. - Not for: a polished production assistant, since the card calls it a quick demonstration. ## 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: 105,736 - Likes: 1,878 - License: apache-2.0 - Based on: mistralai/Mistral-7B-v0.1 - Quantized versions: 31 - Fine-tunes: 332 - Tags: transformers, pytorch, safetensors, mistral, text-generation, finetuned, mistral-common, conversational ## Links - Model: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1 - Files: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download mistralai/Mistral-7B-Instruct-v0.1 ``` 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 [mistralai/Mistral-7B-Instruct-v0.1 on Hugging Face](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) ## 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 | | --- | --- | --- | --- | --- | --- | | Mistral-7B-Instruct-v0.1 (this page) | 105,736 | 1,878 | 7.2B | apache-2.0 | 2025-07-24 | | [Mistral-7B-v0.1](https://mindket.com/p/462400f3-8981-464a-82c5-3c31376c9d1d) | 441,952 | 4,231 | 7.2B | apache-2.0 | 2025-07-24 | | [Mistral-7B-Instruct-v0.3](https://mindket.com/p/f318a676-0a11-4c56-b805-9e3a65afbf27) | 1,771,788 | 3,838 | 7.2B | apache-2.0 | 2025-12-03 | | [PLLuM-12B-chat-2512](https://mindket.com/p/cbe161b0-b5fa-4e5c-8df7-5c706a4b52f4) | 815 | 7 | 415K | apache-2.0 | 2026-06-01 | | [Unity Coder 7B](https://mindket.com/p/f2f91285-da7b-4cf3-a923-cd7b91482cdf) | 146 | 4 | 7.6B | cc-by-4.0 | 2026-04-04 | | [ALLaM-7B-Instruct-preview](https://mindket.com/p/95ed1553-ca4e-4a47-9739-80a0aa93810c) | 93,626 | 207 | 7B | apache-2.0 | 2025-07-14 | 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 · YetAnotherNick · 2024-01-08](https://news.ycombinator.com/item?id=38910761): I made an app that runs Mistral 7B 0.2 LLM locally on iPhone Pros: Look into their official page: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1 There is no system prompt. Unless Llamaindex or some other sources cite something from mistral, I am inclined to believe they just copied it from llama. - [Hacker News · nl · 2024-01-08](https://news.ycombinator.com/item?id=38909947): I made an app that runs Mistral 7B 0.2 LLM locally on iPhone Pros: No this isn't the case. Here's the example from https://www.promptingguide.ai/models/mistral-7b\#chat-templat... \INST What is your favorite condiment? /INST "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking… - [Hacker News · skissane · 2023-11-11](https://news.ycombinator.com/item?id=38235587): Yarn-Mistral-7B-128k: It says it is based on https://huggingface.co/mistralai/Mistral-7B-v0.1 not https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1 so I am guessing "No" - [Hacker News · dragonwriter · 2023-10-01](https://news.ycombinator.com/item?id=37721254): Mistral releases ‘unmoderated’ chatbot via torrent: \> Torrent is probably the most appropriate tech to distribute these models. I dunno, I think Git with Git LFS and a nice web interface is a pretty good way, and, look: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1 And also, I suspect more useful for more people here: GGUF, from 2-bit… - [Hacker News · SushiHippie · 2023-09-30](https://news.ycombinator.com/item?id=37721041): Mistral releases ‘unmoderated’ chatbot via torrent: https://news.ycombinator.com/item?id=37721018 Just tried it on huggingface and the instruct model seems to perform way better. https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1 - [Hacker News · code51 · 2023-09-27](https://news.ycombinator.com/item?id=37679998): Mistral 7B: Here is the response from Instruct model: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1 To track swipes in TypeScript, we can use a library like \`react-native-gesture-handler\`. Here's an example of how to use it to track horizontal and vertical swipes: \`\`\`typescript import React from… - [Hacker News · code51 · 2023-09-27](https://news.ycombinator.com/item?id=37679779): Mistral 7B: What about this one? https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1 - [Hacker News · brucethemoose2 · 2023-09-27](https://news.ycombinator.com/item?id=37678919): Mistral 7B: OK I am hosting the instruct model on the horde now. I will throw up the raw base model too: https://lite.koboldai.net/\# Note that you must switch to instruct mode in the UI, and use the "LLama 2 chat" preset, but you also need a \ token in the instruction (memory) tab:… ## 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: 2025-07-24 - First published: 2023-09-27 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open mistralai/Mistral-7B-Instruct-v0.1 on Hugging Face](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f36c3d38-c1ea-4846-a79e-4a1f8e2a132c) - [Its page on Mindket](https://mindket.com/p/f36c3d38-c1ea-4846-a79e-4a1f8e2a132c)