--- title: "Mistral-7B-v0.1" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/462400f3-8981-464a-82c5-3c31376c9d1d" updated: "2026-11-13" --- # Mistral-7B-v0.1 > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates and continues text as a 7B pretrained base model, for researchers and developers who want a starting point to test, tune or build on. Mistral-7B-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 Mistral-7B-v0.1 is a pretrained text generation model with 7 billion parameters, released by Mistral AI. It is a base model, so it continues text you give it and was not tuned for chat. The card says it beat Llama 2 13B on every benchmark the team tried. ### Who it suits It suits researchers and developers who want an open English base model to test, fine-tune or build products on. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Language model researchers | Use it as a base to compare against or to study | A researcher runs the same prompts on this model and a larger one | | Software | Developers | Fine-tune it for their own task | A developer tunes it on support tickets to draft replies | | Education | Students | Learn how a pretrained model behaves before any tuning | A student gives it the start of a story and reads how it continues | If you want a ready chat assistant with moderation, you can skip it. ### Where to use it - Python with the transformers library - Windows, macOS or Linux, after downloading the files with Python | Use case | Who | What they get back | | --- | --- | --- | | Continue a piece of text | Anyone testing it | Generated text that follows the prompt | | Base for fine-tuning | Developers | A starting model to train on their own data | | Baseline for comparison | Researchers | Outputs to measure other models against | ### How to set it up - Python on your computer - The transformers library, version 4.34.0 or newer - Disk space for about 14.5 GB in BF16, or about 3.6 GB at 4-bit by Hugging Face's estimate 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load them with transformers. The card shows no code sample, so follow the transformers documentation for loading a causal language model. ```sh pip install -U huggingface_hub hf download mistralai/Mistral-7B-v0.1 ``` ### How to use it Give the model the start of a text and let it continue. It has no chat format and no moderation layer, so treat the output as raw completion. The only commands the source shows are the download steps. ```sh hf download mistralai/Mistral-7B-v0.1 ``` | Tool | What it does | | --- | --- | | hf download | Fetches the model files to your computer | | pip install -U huggingface\_hub | Installs the package that provides the hf command | ### Example You give it the opening line of a short story, such as a lighthouse keeper finding a letter. The model writes what might come next, sentence by sentence, until it reaches your length limit. ### For newcomers and experts A base model only predicts the next words. It does not answer questions the way a chat assistant does, so ask it to complete a sentence rather than to follow an order. A first small thing to try is a one-line story opening. For experts, it is a transformer with grouped-query attention, sliding-window attention and a byte-fallback BPE tokenizer. As a pretrained base, it has no instruction tuning or moderation built in. That gives you freedom to fine-tune, and puts safety filtering on you. Mistral AI pretrains the 7B base model → You download the files with hf → transformers loads the weights → You send the start of a text → The model continues it word by word ### Good to know - Errors: a KeyError for mistral, or a meta tensor error, means your Transformers is too old, so use 4.34.0 or newer - Language: English - License: Apache 2.0 - Cost to run: none named, apart from your own hardware - Data it reaches: downloading uses the web and writes files to your disk, and the source names no other access - Not for: moderated chat, since the card says the base model has no moderation mechanisms ## README ````markdown # Mistral-7B-v0.1 Generates and continues text as a 7B pretrained base model, for researchers and developers who want a starting point to test, tune or build on. 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-v0.1 is a pretrained text generation model with 7 billion parameters, released by Mistral AI. It is a base model, so it continues text you give it and was not tuned for chat. The card says it beat Llama 2 13B on every benchmark the team tried. Who it suits: It suits researchers and developers who want an open English base model to test, fine-tune or build products on. ```table Industry | Who uses it | How they use it | Example Research | Language model researchers | Use it as a base to compare against or to study | A researcher runs the same prompts on this model and a larger one Software | Developers | Fine-tune it for their own task | A developer tunes it on support tickets to draft replies Education | Students | Learn how a pretrained model behaves before any tuning | A student gives it the start of a story and reads how it continues ``` If you want a ready chat assistant with moderation, you can skip it. Where to use it: - Python with the transformers library - Windows, macOS or Linux, after downloading the files with Python ```table Use case | Who | What they get back Continue a piece of text | Anyone testing it | Generated text that follows the prompt Base for fine-tuning | Developers | A starting model to train on their own data Baseline for comparison | Researchers | Outputs to measure other models against ``` How to set it up: - Python on your computer - The transformers library, version 4.34.0 or newer - Disk space for about 14.5 GB in BF16, or about 3.6 GB at 4-bit by Hugging Face's estimate 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load them with transformers. The card shows no code sample, so follow the transformers documentation for loading a causal language model. ```sh pip install -U huggingface_hub hf download mistralai/Mistral-7B-v0.1 ``` How to use it: Give the model the start of a text and let it continue. It has no chat format and no moderation layer, so treat the output as raw completion. The only commands the source shows are the download steps. ```sh hf download mistralai/Mistral-7B-v0.1 ``` ```table Tool | What it does hf download | Fetches the model files to your computer pip install -U huggingface_hub | Installs the package that provides the hf command ``` Example: You give it the opening line of a short story, such as a lighthouse keeper finding a letter. The model writes what might come next, sentence by sentence, until it reaches your length limit. For newcomers and experts: A base model only predicts the next words. It does not answer questions the way a chat assistant does, so ask it to complete a sentence rather than to follow an order. A first small thing to try is a one-line story opening. For experts, it is a transformer with grouped-query attention, sliding-window attention and a byte-fallback BPE tokenizer. As a pretrained base, it has no instruction tuning or moderation built in. That gives you freedom to fine-tune, and puts safety filtering on you. ```flow Mistral AI pretrains the 7B base model You download the files with hf transformers loads the weights You send the start of a text The model continues it word by word ``` Good to know: - Errors: a KeyError for mistral, or a meta tensor error, means your Transformers is too old, so use 4.34.0 or newer - Language: English - License: Apache 2.0 - Cost to run: none named, apart from your own hardware - Data it reaches: downloading uses the web and writes files to your disk, and the source names no other access - Not for: moderated chat, since the card says the base model has no moderation mechanisms ## 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: 441,952 - Likes: 4,231 - License: apache-2.0 - Languages: en - Quantized versions: 196 - Fine-tunes: 952 - Tags: transformers, pytorch, safetensors, mistral, text-generation, pretrained, mistral-common ## Links - Model: https://huggingface.co/mistralai/Mistral-7B-v0.1 - Files: https://huggingface.co/mistralai/Mistral-7B-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-v0.1 ``` 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 [mistralai/Mistral-7B-v0.1 on Hugging Face](https://huggingface.co/mistralai/Mistral-7B-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-v0.1 (this page) | 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 | | [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 | | [ARLoop-0.6B](https://mindket.com/p/457fa82c-e2a1-4587-93ed-5435177b083f) | 2,164 | 5 | 583M | apache-2.0 | 2026-11-05 | | [KLM 1.5](https://mindket.com/p/bffc34a5-c64b-418e-a26e-f869a269c8c1) | 1,603 | 31 | 99M | apache-2.0 | 2026-11-01 | | [KLM 1.6](https://mindket.com/p/8b218890-16d1-4ff9-b98e-3eea5e8533e8) | 909 | 60 | 99M | apache-2.0 | 2026-11-09 | 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 · TheBlapse · 2023-09-27](https://news.ycombinator.com/item?id=37670946): Mistral AI “drops” mistral-7B-v0.1 - [Bluesky · @crovia.bsky.social · 2026-11-09](https://bsky.app/profile/crovia.bsky.social/post/3mzug22desf2g): Silence Report 2026-W45: 6,324 AI models observed hour by hour; 168 hourly epochs anchored in Bitcoin; 19 signed proofs that nothing was disclosed. Longest verifiable silence: 48.37 days (mistralai/Mistral-7B-v0.1). Every number links to a signed file you can verify in your browser. - [Hacker News · pizza · 2024-11-02](https://news.ycombinator.com/item?id=42025488): SmolLM2: In shell 1: $ docker run --runtime nvidia --gpus all \\ -v \~/.cache/huggingface:/root/.cache/huggingface \\ --env "HUGGING\_FACE\_HUB\_TOKEN=\" \\ -p 8000:8000 \\ --ipc=host \\ vllm/vllm-openai:latest \\ --model mistralai/Mistral-7B-v0.1 In shell 2: $ curl http://localhost:8000/v1/completions \\ -H… - [Bluesky · @crovia.bsky.social · 2026-09-28](https://bsky.app/profile/crovia.bsky.social/post/3mwkshf3ow42h): Silence Report 2026-W39: 6,324 AI models observed hour by hour; 168 hourly epochs anchored in Bitcoin; 19 signed proofs that nothing was disclosed. Longest verifiable silence: 7.29 days (mistralai/Mistral-7B-v0.1). Every number links to a signed file you can verify in your browser. - [Hacker News · rgbrgb · 2023-12-11](https://news.ycombinator.com/item?id=38602226): Mistral: Our first AI endpoints are available in early access: \> Mistral-tiny. Our most cost-effective endpoint currently serves Mistral 7B Instruct v0.2, a new minor release of Mistral 7B Instruct. Mistral-tiny only works in English. It obtains 7.6 on MT-Bench. The instructed model can be downloaded here. "download here" link is to v0.1 0. Oversight or are… - [Bluesky · @crovia.bsky.social · 2026-11-02](https://bsky.app/profile/crovia.bsky.social/post/3mzcsrmd4xt2f): Silence Report 2026-W44: 6,322 AI models observed hour by hour; 168 hourly epochs anchored in Bitcoin; 19 signed proofs that nothing was disclosed. Longest verifiable silence: 41.41 days (mistralai/Mistral-7B-v0.1). Every number links to a signed file you can verify in your browser. - [Hacker News · lhl · 2023-12-01](https://news.ycombinator.com/item?id=38483611): Are Open-Source Large Language Models Catching Up?: A couple big/strong open models that have just been released the past few days: \* Qwen 72B (and 1.8B) - 32K context, trained on 3T tokens, \<100M MAU commercial license, strong benchmark performance: https://twitter.com/huybery/status/1730127387109781932 \* DeepSeek LLM 67B - 4K context, 2T tokens,… - [Bluesky · @crovia.bsky.social · 2026-10-26](https://bsky.app/profile/crovia.bsky.social/post/3myr7j62wby2q): Silence Report 2026-W43: 6,326 AI models observed hour by hour; 168 hourly epochs anchored in Bitcoin; 19 signed proofs that nothing was disclosed. Longest verifiable silence: 34.41 days (mistralai/Mistral-7B-v0.1). Every number links to a signed file you can verify in your browser. - [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" - [Bluesky · @crovia.bsky.social · 2026-10-19](https://bsky.app/profile/crovia.bsky.social/post/3my7mapahve2u): Silence Report 2026-W42: 6,328 AI models observed hour by hour; 168 hourly epochs anchored in Bitcoin; 19 signed proofs that nothing was disclosed. Longest verifiable silence: 27.37 days (mistralai/Mistral-7B-v0.1). Every number links to a signed file you can verify in your browser. - [Hacker News · brucethemoose2 · 2023-10-15](https://news.ycombinator.com/item?id=37892107): Zephyr 7B – Mistral Finetune that responds like ChatGPT: IMO the title is a bit misleading: \> Zephyr-7B-α has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size… - [Bluesky · @crovia.bsky.social · 2026-10-12](https://bsky.app/profile/crovia.bsky.social/post/3mxnyyb3giy26): Silence Report 2026-W41: 6,328 AI models observed hour by hour; 168 hourly epochs anchored in Bitcoin; 19 signed proofs that nothing was disclosed. Longest verifiable silence: 20.41 days (mistralai/Mistral-7B-v0.1). Every number links to a signed file you can verify in your browser. ## 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: 2023-09-20 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open mistralai/Mistral-7B-v0.1 on Hugging Face](https://huggingface.co/mistralai/Mistral-7B-v0.1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/462400f3-8981-464a-82c5-3c31376c9d1d) - [Its page on Mindket](https://mindket.com/p/462400f3-8981-464a-82c5-3c31376c9d1d)