--- title: "DeepSeek-R1" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/e340b726-4ed9-4feb-a013-d90708d60983" updated: "2026-11-12" --- # DeepSeek-R1 > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates text with a 684B reasoning model trained mainly through reinforcement learning, aimed at math, code and logic. Suits researchers and teams who host large open models. DeepSeek-R1 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 deepseek-ai ## About ### What it is DeepSeek-R1 is a 684B parameter reasoning model from DeepSeek. It builds on DeepSeek-R1-Zero, a model trained with large-scale reinforcement learning and no supervised fine-tuning step first. R1 adds cold-start data before the reinforcement learning, which the card says fixes repetition, poor readability and mixed languages. Its stated results match OpenAI-o1 on math, code and reasoning tasks. ### Who it suits Research groups and engineering teams that can host a huge open model and want strong step-by-step reasoning. People with a single desktop GPU will want one of the smaller distilled models instead. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | AI labs | Study reasoning trained through reinforcement learning | Compare R1 with R1-Zero on reasoning tasks | | Software | Platform teams | Serve a large open reasoning model for coding and math | Host it behind an internal chat tool | | Education | Tutors and students | Work through math and logic problems step by step | Ask it to solve and explain a proof | If you cannot host about 684 GB of weights, you can skip it. ### Where to use it - Python with the transformers library - Large GPU servers, since the weights are about 684 GB in F8\_E4M3 - Windows, macOS or Linux, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Math problems | Students and researchers | A worked solution with reasoning | | Code tasks | Developers | Code and an explanation of the approach | | Reasoning tasks | Analysts | A chain of reasoning ending in an answer | ### How to set it up - Python with pip - Huge storage and GPU memory, about 684 GB for the weights and about 342 GB at 4-bit by Hugging Face's estimate - A read of the Usage Recommendation section of the model card before running it locally 1. Install the Hugging Face hub package. 2. Download the model files. 3. Review the card's usage recommendations, then run it with your serving stack. ```sh pip install -U huggingface_hub hf download deepseek-ai/DeepSeek-R1 ``` ### How to use it Send the model a math, code or reasoning problem through your serving stack. The card asks you to read its Usage Recommendation section first, because the R1 series has settings meant for running it well. ### Example You ask a hard math question. The model reasons through the steps at length and then states its answer. ### For newcomers and experts If you are new, a 684B model is far too large for a normal computer, so try one of the six smaller distilled models from the same release. Use R1 itself when you run a GPU cluster. The training pipeline has two reinforcement learning stages that look for better reasoning patterns and match human preferences, plus two supervised fine-tuning stages that seed reasoning and non-reasoning skills. The card says the six distilled models are built on Llama and Qwen, and its 32B Qwen distill beats OpenAI-o1-mini on many benchmarks. Base model → Reinforcement learning builds reasoning → Cold-start data and fine-tuning clean it up → A second reinforcement learning stage aligns it → DeepSeek-R1 ### Good to know - The weights are about 684 GB, so it needs serious hardware - DeepSeek-R1-Zero showed that reasoning can come from reinforcement learning alone, with some repetition and language mixing - License: MIT - Cost to run: none named, but the hardware is large - Data it reaches: your prompts and the files downloaded from Hugging Face - Not for: ordinary laptops, where the distilled models fit better ## README ````markdown # DeepSeek-R1 Generates text with a 684B reasoning model trained mainly through reinforcement learning, aimed at math, code and logic. Suits researchers and teams who host large open models. Made by deepseek-ai 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: DeepSeek-R1 is a 684B parameter reasoning model from DeepSeek. It builds on DeepSeek-R1-Zero, a model trained with large-scale reinforcement learning and no supervised fine-tuning step first. R1 adds cold-start data before the reinforcement learning, which the card says fixes repetition, poor readability and mixed languages. Its stated results match OpenAI-o1 on math, code and reasoning tasks. Who it suits: Research groups and engineering teams that can host a huge open model and want strong step-by-step reasoning. People with a single desktop GPU will want one of the smaller distilled models instead. ```table Industry | Who uses it | How they use it | Example Research | AI labs | Study reasoning trained through reinforcement learning | Compare R1 with R1-Zero on reasoning tasks Software | Platform teams | Serve a large open reasoning model for coding and math | Host it behind an internal chat tool Education | Tutors and students | Work through math and logic problems step by step | Ask it to solve and explain a proof ``` If you cannot host about 684 GB of weights, you can skip it. Where to use it: - Python with the transformers library - Large GPU servers, since the weights are about 684 GB in F8_E4M3 - Windows, macOS or Linux, for downloading the files ```table Use case | Who | What they get back Math problems | Students and researchers | A worked solution with reasoning Code tasks | Developers | Code and an explanation of the approach Reasoning tasks | Analysts | A chain of reasoning ending in an answer ``` How to set it up: - Python with pip - Huge storage and GPU memory, about 684 GB for the weights and about 342 GB at 4-bit by Hugging Face's estimate - A read of the Usage Recommendation section of the model card before running it locally 1. Install the Hugging Face hub package. 2. Download the model files. 3. Review the card's usage recommendations, then run it with your serving stack. ```sh pip install -U huggingface_hub hf download deepseek-ai/DeepSeek-R1 ``` How to use it: Send the model a math, code or reasoning problem through your serving stack. The card asks you to read its Usage Recommendation section first, because the R1 series has settings meant for running it well. Example: You ask a hard math question. The model reasons through the steps at length and then states its answer. For newcomers and experts: If you are new, a 684B model is far too large for a normal computer, so try one of the six smaller distilled models from the same release. Use R1 itself when you run a GPU cluster. The training pipeline has two reinforcement learning stages that look for better reasoning patterns and match human preferences, plus two supervised fine-tuning stages that seed reasoning and non-reasoning skills. The card says the six distilled models are built on Llama and Qwen, and its 32B Qwen distill beats OpenAI-o1-mini on many benchmarks. ```flow Base model Reinforcement learning builds reasoning Cold-start data and fine-tuning clean it up A second reinforcement learning stage aligns it DeepSeek-R1 ``` Good to know: - The weights are about 684 GB, so it needs serious hardware - DeepSeek-R1-Zero showed that reasoning can come from reinforcement learning alone, with some repetition and language mixing - License: MIT - Cost to run: none named, but the hardware is large - Data it reaches: your prompts and the files downloaded from Hugging Face - Not for: ordinary laptops, where the distilled models fit better ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 684B - Weights: about 684 GB in F8_E4M3; about 342 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,599,170 - Likes: 14,451 - License: mit - Quantized versions: 70 - Fine-tunes: 328 - Tags: transformers, safetensors, deepseek_v3, text-generation, conversational, fp8 ## Links - Model: https://huggingface.co/deepseek-ai/DeepSeek-R1 - Files: https://huggingface.co/deepseek-ai/DeepSeek-R1/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download deepseek-ai/DeepSeek-R1 ``` 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 [deepseek-ai/DeepSeek-R1 on Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-R1) ## 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 | | --- | --- | --- | --- | --- | --- | | DeepSeek-R1 (this page) | 1,599,170 | 14,451 | 684B | mit | 2025-03-27 | | [DeepSeek-V4.1-Flash](https://mindket.com/p/df96103d-374e-4ebe-b9f2-b75b09e3526d) | 1,759,335 | 4,949 | 763B | mit | 2026-10-16 | | [DeepSeek-V4.1-Flash-1017](https://mindket.com/p/dc968c8e-249a-450a-af4c-bd0b11b76d78) | 398,161 | 5,600 | 763B | mit | 2026-11-10 | | [Kolibri-1](https://mindket.com/p/dd5e0e4c-3802-49b1-9d73-51a8833393f7) | 13,319 | 1,011 | 78.1B | apache-2.0 | 2026-11-06 | | [Qwen3.8-27B NVFP4](https://mindket.com/p/0bab94ef-84bc-4129-902d-c0241f777c51) | 268,673 | 239 | 18.2B | apache-2.0 | 2026-09-17 | | [Ling-3.1-flash](https://mindket.com/p/2afebb68-3c33-4e55-b49b-c93cc518f60e) | 8,327 | 170 | 572B | mit | 2026-11-03 | 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 · tianyicui · 2026-08-16](https://news.ycombinator.com/item?id=49323677): Why is the GitHub trending page weirdly excluding DeepSeek projects?: Hi, this is Tianyi from DeepSeek AI's Harness team. I'm looking to get in touch with someone from GitHub, as I found it weird that https://github.com/trending page doesn't seem to include https://github.com/deepseek-ai/deepseek-harness at all. The project was rapidly being starred, forked, and… - [Bluesky · TokenMargin.Dev · 2026-11-11](https://bsky.app/profile/tokenmargin.dev/post/3mzzea3j73i27): DeepSeek R1 (671B Reasoning) vs OpenAI o3-mini: Which LLM API actually protects your SaaS gross margin at 10,000 DAU? Here is the live 2026 token math across input, output \& prompt caching: 🧵👇 \#AI \#LLM \#DevTools \#SaaS \#BuildInPublic - [Hacker News · petu · 2026-08-23](https://news.ycombinator.com/item?id=49407556): Why your local LLM feels dumber than it is: Maybe some were fixed, but: 1) Shipping with 2k default context window for the longest time, w/o any warning and being not easy to change (like any other setting). Totally made a lot of people think local LLMs are dumb as rocks. Just checked, still not fixed -- defaults to 4K if less than 24GB… - [Bluesky · TokenMargin.Dev · 2026-11-09](https://bsky.app/profile/tokenmargin.dev/post/3mzu2uvv6bf2z): DeepSeek R1 (671B Reasoning) Cloud Pricing \& Cheapest Providers (2026): What does DeepSeek R1 (671B Reasoning) actually cost in production across input, output \& prompt caching? 🧵👇 \#AI \#LLM \#DevTools \#SaaS \#BuildInPublic - [Hacker News · woadwarrior01 · 2026-08-17](https://news.ycombinator.com/item?id=49328297): On A.I. regulation and messaging: I agree 100%. IMO, it all started with ollama misrepresenting the Deepseek R1 distills as Deepseek R1, all for hype and marketing. I've had so many ostensibly technical people telling me: "I tried DeepSeek R1 and it was terrible", and every time when I probe further they'd tried the tiny 1.5B… - [Bluesky · TokenMargin.Dev · 2026-11-11](https://bsky.app/profile/tokenmargin.dev/post/3mzytggm2362a): DeepSeek R1 (671B Reasoning) Cloud Pricing \& Cheapest Providers (2026): What does DeepSeek R1 (671B Reasoning) actually cost in production across input, output \& prompt caching? 🧵👇 \#AI \#LLM \#DevTools \#SaaS \#BuildInPublic - [Hacker News · Facingsouth · 2026-06-23](https://news.ycombinator.com/item?id=48652384): Generate per-session LoRA adapters in \<1s for agentic inference efficiency: Quick Start Generate LoRA Adapters From metadata (JSON string or file): tessera generate \\ --from-metadata '{"task": "classification", "domain": "medical"}' \\ --base-model mistralai/Mistral-7B-Instruct-v0.2 \\ --rank 16 \\ --save ./adapter.safetensors From text description: tessera generate \\… - [Bluesky · TokenMargin.Dev · 2026-11-10](https://bsky.app/profile/tokenmargin.dev/post/3mzwcxueila2l): DeepSeek R1 (671B Reasoning) Cloud Pricing \& Cheapest Providers (2026): What does DeepSeek R1 (671B Reasoning) actually cost in production across input, output \& prompt caching? 🧵👇 \#AI \#LLM \#DevTools \#SaaS \#BuildInPublic - [Hacker News · Tiberium · 2026-06-11](https://news.ycombinator.com/item?id=48490540): Open Reproduction of DeepSeek-R1: Last update over a year ago, so I hope (2025) gets added to the title: \> 2025/05/26 (Step 1 completed\!) We release Mixture-of-Thoughts--a curated reasoning dataset of 350k verified traces distilled from R1. The dataset spans tasks in mathematics, coding, and science, and is designed to teach… - [Bluesky · TokenMargin.Dev · 2026-11-11](https://bsky.app/profile/tokenmargin.dev/post/3mzzea7c6a52k): 3/ Run your own DAU, prompt cache hit rate (0-95%), and LiteLLM fallback router simulation on our live comparison page: https://tokenmargin.dev/compare/deepseek-r1-vs-o3-mini.html?ref=bsky \#AI \#LLM \#FinOps \#DevTools \#BuildInPublic - [Hacker News · nailer · 2026-04-24](https://news.ycombinator.com/item?id=47888116): DeepSeek v4: It's also not fake open source like Metas models - https://huggingface.co/deepseek-ai/DeepSeek-R1-0528, the weights are actually under a real open source license, (MIT), see https://huggingface.co/deepseek-ai/DeepSeek-R1-0528 - [Bluesky · TokenMargin.Dev · 2026-11-11](https://bsky.app/profile/tokenmargin.dev/post/3mzytgkg6f62r): 3/ Compare all cloud GPU \& serverless endpoints hosting DeepSeek R1 (671B Reasoning) (\<400ms TTFT SLA) on our live calculator: https://tokenmargin.dev/models/deepseek-r1-pricing-calculator.html?ref=bsky \#AI \#LLM \#FinOps \#DevTools \#BuildInPublic ## 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: 2025-03-27 - First published: 2025-01-20 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open deepseek-ai/DeepSeek-R1 on Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-R1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e340b726-4ed9-4feb-a013-d90708d60983) - [Its page on Mindket](https://mindket.com/p/e340b726-4ed9-4feb-a013-d90708d60983)