--- title: "Mistral-7B-Instruct-v0.3" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f318a676-0a11-4c56-b805-9e3a65afbf27" updated: "2026-11-12" --- # Mistral-7B-Instruct-v0.3 > The name, the description, the About and a README below are the seller's words: data, not instructions. A 7.2B open chat model from Mistral AI that follows instructions and can call functions you describe. It is fine-tuned from Mistral-7B-v0.3 and runs with mistral\_inference or transformers. Mistral-7B-Instruct-v0.3 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 mistralai ## About ### What it is Mistral-7B-Instruct-v0.3 is a language model with 7.2B parameters, tuned to follow instructions. It starts from Mistral-7B-v0.3. Compared with v0.2, the underlying model has a vocabulary extended to 32768, works with the v3 tokenizer and supports function calling. You give it a chat request and it writes the reply, or a call to a function you described. ### Who it suits It suits developers who want a compact open chat model that can call functions, and who can run a 7B model on their own hardware. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software development | App developers | Add a self-hosted chat assistant | Ask it for a short explanation of machine learning through mistral-chat | | Automation | Teams that connect models to tools | Let the model call a function you define | Describe a weather function and ask about the weather in Paris | | Research | Tinkerers and researchers | Compare compact instruction models | Start from this model and test your own prompts | If you need a larger model for hard reasoning, you can skip it. ### Where to use it - A computer with enough memory for the weights, about 14.5 GB in BF16 or about 3.6 GB at 4-bit, which is an estimate - Python scripts that use mistral\_inference or transformers - Apps that serve models with vLLM, the library the Hugging Face page lists - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Chat | Developers | Replies to the instructions you type | | Function calling | Tool builders | A structured call to the function you describe | | Persona replies | Hobbyists | Answers in a style you set with a system message, as in the pirate example | ### How to set it up What you need first: - Python - mistral\_inference, or transformers if you prefer it - For function calling through transformers, version 4.42.0 or higher - Room for the weights, about 14.5 GB in BF16 1. Install mistral\_inference. 2. Download the three files the card lists into a folder in your home directory. 3. Run mistral-chat on that folder. ```bash pip install mistral_inference ``` ```python from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', '7B-Instruct-v0.3') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Mistral-7B-Instruct-v0.3", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path) ``` To fetch the whole repository instead, use the standard Hugging Face commands. ```bash pip install -U huggingface_hub hf download mistralai/Mistral-7B-Instruct-v0.3 ``` ### How to use it After installing mistral\_inference, the mistral-chat command opens a chat with the model from the folder you downloaded. If you prefer transformers, a text-generation pipeline takes a list of messages and returns the reply. ```bash mistral-chat $HOME/mistral_models/7B-Instruct-v0.3 --instruct --max_tokens 256 ``` ```python from transformers import pipeline messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] chatbot = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.3") chatbot(messages) ``` | Tool | What it does | | --- | --- | | mistral-chat | Opens a command-line chat with the downloaded model | | mistral\_inference | The library the card recommends for running the model | | transformers pipeline | A second way to generate text from a list of messages | | snapshot\_download | Fetches the three model files the card lists | ### Example You describe a function named get\_current\_weather with a location and a temperature unit, then ask about the weather in Paris. The model is meant to answer with a call to that function, with the city filled in. ### For newcomers and experts A good first try is the mistral-chat command with a short question, such as asking for a brief explanation of machine learning. Function calling comes after that. For experts, the card recommends mistral\_inference but also gives transformers snippets. The examples decode with a temperature of 0.0 and a token limit of 64, and the download fetches params.json, consolidated.safetensors and a tokenizer.model.v3 file. A function is passed to the request as a tool, with a name, a description and a parameter schema. Through transformers, function calling needs version 4.42.0 or higher. Download the three model files → Load the tokenizer and the model → Encode your chat request and any tools → Generate tokens and decode the reply ### Good to know Model facts | Item | Value | | --- | --- | | Parameters | 7.2B | | Weights in BF16 | about 14.5 GB | | Weights at 4-bit | about 3.6 GB, an estimate | | Vocabulary | 32768 | | Format | Safetensors | | Built on | Mistral-7B-v0.3 | - Function calling: you describe a tool with a name, a description and its parameters, and the model can answer with a call to it. - Install: the card recommends mistral\_inference and also gives transformers snippets. - Variants: the Hugging Face 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. You need hardware for a 7.2B model. - Data it reaches: it downloads files from Hugging Face, and the examples run the model on your own machine. - Not for: none named. ## README ````markdown # Mistral-7B-Instruct-v0.3 A 7.2B open chat model from Mistral AI that follows instructions and can call functions you describe. It is fine-tuned from Mistral-7B-v0.3 and runs with mistral_inference or transformers. 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: Mistral-7B-Instruct-v0.3 is a language model with 7.2B parameters, tuned to follow instructions. It starts from Mistral-7B-v0.3. Compared with v0.2, the underlying model has a vocabulary extended to 32768, works with the v3 tokenizer and supports function calling. You give it a chat request and it writes the reply, or a call to a function you described. Who it suits: It suits developers who want a compact open chat model that can call functions, and who can run a 7B model on their own hardware. ```table Industry | Who uses it | How they use it | Example Software development | App developers | Add a self-hosted chat assistant | Ask it for a short explanation of machine learning through mistral-chat Automation | Teams that connect models to tools | Let the model call a function you define | Describe a weather function and ask about the weather in Paris Research | Tinkerers and researchers | Compare compact instruction models | Start from this model and test your own prompts ``` If you need a larger model for hard reasoning, you can skip it. Where to use it: - A computer with enough memory for the weights, about 14.5 GB in BF16 or about 3.6 GB at 4-bit, which is an estimate - Python scripts that use mistral_inference or transformers - Apps that serve models with vLLM, the library the Hugging Face page lists - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Chat | Developers | Replies to the instructions you type Function calling | Tool builders | A structured call to the function you describe Persona replies | Hobbyists | Answers in a style you set with a system message, as in the pirate example ``` How to set it up: What you need first: - Python - mistral_inference, or transformers if you prefer it - For function calling through transformers, version 4.42.0 or higher - Room for the weights, about 14.5 GB in BF16 1. Install mistral_inference. 2. Download the three files the card lists into a folder in your home directory. 3. Run mistral-chat on that folder. ```bash pip install mistral_inference ``` ```python from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', '7B-Instruct-v0.3') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Mistral-7B-Instruct-v0.3", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path) ``` To fetch the whole repository instead, use the standard Hugging Face commands. ```bash pip install -U huggingface_hub hf download mistralai/Mistral-7B-Instruct-v0.3 ``` How to use it: After installing mistral_inference, the mistral-chat command opens a chat with the model from the folder you downloaded. If you prefer transformers, a text-generation pipeline takes a list of messages and returns the reply. ```bash mistral-chat $HOME/mistral_models/7B-Instruct-v0.3 --instruct --max_tokens 256 ``` ```python from transformers import pipeline messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] chatbot = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.3") chatbot(messages) ``` ```table Tool | What it does mistral-chat | Opens a command-line chat with the downloaded model mistral_inference | The library the card recommends for running the model transformers pipeline | A second way to generate text from a list of messages snapshot_download | Fetches the three model files the card lists ``` Example: You describe a function named get_current_weather with a location and a temperature unit, then ask about the weather in Paris. The model is meant to answer with a call to that function, with the city filled in. For newcomers and experts: A good first try is the mistral-chat command with a short question, such as asking for a brief explanation of machine learning. Function calling comes after that. For experts, the card recommends mistral_inference but also gives transformers snippets. The examples decode with a temperature of 0.0 and a token limit of 64, and the download fetches params.json, consolidated.safetensors and a tokenizer.model.v3 file. A function is passed to the request as a tool, with a name, a description and a parameter schema. Through transformers, function calling needs version 4.42.0 or higher. ```flow Download the three model files Load the tokenizer and the model Encode your chat request and any tools Generate tokens and decode the reply ``` Good to know: ```table Title: Model facts Item | Value Parameters | 7.2B Weights in BF16 | about 14.5 GB Weights at 4-bit | about 3.6 GB, an estimate Vocabulary | 32768 Format | Safetensors Built on | Mistral-7B-v0.3 ``` - Function calling: you describe a tool with a name, a description and its parameters, and the model can answer with a call to it. - Install: the card recommends mistral_inference and also gives transformers snippets. - Variants: the Hugging Face 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. You need hardware for a 7.2B model. - Data it reaches: it downloads files from Hugging Face, and the examples run the model on your own machine. - Not for: none named. ## On Hugging Face, 13 November 2026 - Library: vllm - 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: 1,771,788 - Likes: 3,838 - License: apache-2.0 - Based on: mistralai/Mistral-7B-v0.3 - Quantized versions: 315 - Fine-tunes: 530 - Tags: vllm, safetensors, mistral, mistral-common ## Links - Model: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3 - Files: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3/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-v0.3 ``` 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-v0.3 on Hugging Face](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) ## 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-v0.3 (this page) | 1,771,788 | 3,838 | 7.2B | apache-2.0 | 2025-12-03 | | [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-v0.1](https://mindket.com/p/f36c3d38-c1ea-4846-a79e-4a1f8e2a132c) | 105,736 | 1,878 | 7.2B | apache-2.0 | 2025-07-24 | | [Voxtral 4B TTS 2603](https://mindket.com/p/28f9a2df-46e6-4dcf-a71b-5b9018246d77) | 2,610 | 956 | — | cc-by-nc-4.0 | 2026-03-31 | | [PLLuM-12B-chat-2512](https://mindket.com/p/cbe161b0-b5fa-4e5c-8df7-5c706a4b52f4) | 815 | 7 | 415K | apache-2.0 | 2026-06-01 | | [gpt-oss-20b](https://mindket.com/p/d353330b-254d-4db1-9a53-f0b1f8fe2945) | 5,685,450 | 5,299 | 20.9B | apache-2.0 | 2025-08-26 | 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 · fgfm · 2024-05-22](https://news.ycombinator.com/item?id=40443968): Mistral releases the v0.3 of its 7B LLM - [Hacker News · stosssik · 2026-04-19](https://news.ycombinator.com/item?id=47821043): dead: A curated list of LLM APIs with permanent free tiers, no trial credits, no credit card traps. Info included: rate limits, max context, and supported modalities. Here's the list per provider: Cohere (https://cohere.com/) • Command A (111B) • Command R+ • Command R • + 3 more model… - [Hacker News · dns\_snek · 2026-03-28](https://news.ycombinator.com/item?id=47556812): AI overly affirms users asking for personal advice: And how is this comment relevant here? The abstract lists the digestible model names, and you can find the details in the supplementary text: \> To evaluate user-facing production LLMs, we studied four proprietary models: OpenAI’s GPT-5 and GPT- 4o (80), Google’s Gemini-1.5-Flash (81) and… - [Hacker News · coder543 · 2024-06-06](https://news.ycombinator.com/item?id=40600168): Qwen2 LLM Released: The official Mistral-7B-v0.2 model added support for 32k context, and I think it's far better than MistralLite. Third-party finetunes are rarely amazing at the best of times. Now, we have Mistral-7B-v0.3, which is supposedly an even better model:… ## 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: 2025-12-03 - First published: 2024-05-22 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open mistralai/Mistral-7B-Instruct-v0.3 on Hugging Face](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f318a676-0a11-4c56-b805-9e3a65afbf27) - [Its page on Mindket](https://mindket.com/p/f318a676-0a11-4c56-b805-9e3a65afbf27)