--- title: "DeepSeek-V4.1-Flash-1017-exl3" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/93187f0c-ef38-4d6a-b054-9370ad7daaed" updated: "2026-11-13" --- # DeepSeek-V4.1-Flash-1017-exl3 > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs a compressed DeepSeek-V4.1-Flash-1017 in ExLlamaV3 at 1.68, 2.25 or 2.68 bits per weight. For people who run large models on their own hardware and want less memory use. DeepSeek-V4.1-Flash-1017-exl3 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 turboderp ## About ### What it is DeepSeek-V4.1-Flash-1017-exl3 holds compressed copies of the DeepSeek-V4.1-Flash-1017 model in the EXL3 format. Each copy stores the weights with fewer bits, so the model needs less memory. The card lists three sizes, at 1.68, 2.25 and 2.68 bits per weight. ### Who it suits People who run large language models on their own hardware with ExLlamaV3 and want a smaller copy of this DeepSeek model. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Local model users | Run a DeepSeek model in less memory | Pick the 1.68 bits per weight copy to save space | | AI infrastructure | Engineers | Self-host the model with ExLlamaV3 | Serve the 2.25 bits per weight copy | | Research | Quantization researchers | Compare the three sizes on benchmark scores | Read the AIME-25 and GPQA-D results for each | If you do not use ExLlamaV3, you can skip it, since the card names no other runtime. ### Where to use it - ExLlamaV3 version 1.6.6, or its dev branch - Your own computer with Python, on Windows, macOS or Linux, for the download | Use case | Who | What they get back | | --- | --- | --- | | Math problems | Students and researchers | Answers from a model the card scores on AIME-25 | | Science questions | Researchers | Answers from a model the card scores on GPQA-D | | Memory-limited setups | Local model users | A smaller copy of the base model | ### How to set it up - Python with pip - ExLlamaV3 version 1.6.6, or its dev branch - Enough memory for the copy you pick, since the card gives bits per weight but no file sizes 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Load them in ExLlamaV3 and choose one of the three sizes. ```sh pip install -U huggingface_hub hf download turboderp/DeepSeek-V4.1-Flash-1017-exl3 ``` ### How to use it Load the copy you chose in ExLlamaV3, then prompt it as you would the original DeepSeek model. The card shows no command, so follow ExLlamaV3's own steps for loading an EXL3 model. ### Example You load the 2.25 bits per weight copy in ExLlamaV3 and send it a competition math question, since the card scores that copy on AIME-25. This only illustrates the use, because the card shows no sample reply. ### For newcomers and experts If you are new, a quant is a shrunk copy of a model. Fewer bits per weight means less memory, and the card's scores show what each size keeps. Start with the 2.68 bits per weight copy, which has the most bits of the three. For experts, the card calls these SC quants and plots KL divergence and perplexity against VRAM in two images. It says more quants and more benchmarks will follow. A fixed baseline score on AIME-25 was assumed, because the author found no source. The DeepSeek-V4.1-Flash-1017 base model → EXL3 compresses its weights to fewer bits → You pick the 1.68, 2.25 or 2.68 size → ExLlamaV3 loads it for chat ### Good to know The card scores each copy on AIME-25 and GPQA-D. The 1.68 copy gets 96.7% on AIME-25, and the other two get 100%. On GPQA-D the copies range from 89.9% to 90.4%. The baseline GPQA-D score of 90.2% comes from OpenRouter, and the AIME-25 baseline of 100% is an assumption. GPQA-D score by size | Label | Value | | --- | --- | | 1.68 bits per weight | 90.4% | | 2.25 bits per weight | 90.4% | | 2.68 bits per weight | 89.9% | | Baseline | 90.2% | If you see corrupted output on short prompts, the card says it is likely a failure mode of the model itself. - License: MIT, as the Hugging Face record lists it. - Cost to run: no API key or account is named. The card gives no hardware needs. - Data it reaches: downloading reaches Hugging Face. The card does not describe the model reaching files or the web. - Not for: older ExLlamaV3 versions, since the card says it requires version 1.6.6 or the dev branch. ## README ````markdown # DeepSeek-V4.1-Flash-1017-exl3 Runs a compressed DeepSeek-V4.1-Flash-1017 in ExLlamaV3 at 1.68, 2.25 or 2.68 bits per weight. For people who run large models on their own hardware and want less memory use. Made by turboderp 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-V4.1-Flash-1017-exl3 holds compressed copies of the DeepSeek-V4.1-Flash-1017 model in the EXL3 format. Each copy stores the weights with fewer bits, so the model needs less memory. The card lists three sizes, at 1.68, 2.25 and 2.68 bits per weight. Who it suits: People who run large language models on their own hardware with ExLlamaV3 and want a smaller copy of this DeepSeek model. ```table Industry | Who uses it | How they use it | Example Software | Local model users | Run a DeepSeek model in less memory | Pick the 1.68 bits per weight copy to save space AI infrastructure | Engineers | Self-host the model with ExLlamaV3 | Serve the 2.25 bits per weight copy Research | Quantization researchers | Compare the three sizes on benchmark scores | Read the AIME-25 and GPQA-D results for each ``` If you do not use ExLlamaV3, you can skip it, since the card names no other runtime. Where to use it: - ExLlamaV3 version 1.6.6, or its dev branch - Your own computer with Python, on Windows, macOS or Linux, for the download ```table Use case | Who | What they get back Math problems | Students and researchers | Answers from a model the card scores on AIME-25 Science questions | Researchers | Answers from a model the card scores on GPQA-D Memory-limited setups | Local model users | A smaller copy of the base model ``` How to set it up: - Python with pip - ExLlamaV3 version 1.6.6, or its dev branch - Enough memory for the copy you pick, since the card gives bits per weight but no file sizes 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Load them in ExLlamaV3 and choose one of the three sizes. ```sh pip install -U huggingface_hub hf download turboderp/DeepSeek-V4.1-Flash-1017-exl3 ``` How to use it: Load the copy you chose in ExLlamaV3, then prompt it as you would the original DeepSeek model. The card shows no command, so follow ExLlamaV3's own steps for loading an EXL3 model. Example: You load the 2.25 bits per weight copy in ExLlamaV3 and send it a competition math question, since the card scores that copy on AIME-25. This only illustrates the use, because the card shows no sample reply. For newcomers and experts: If you are new, a quant is a shrunk copy of a model. Fewer bits per weight means less memory, and the card's scores show what each size keeps. Start with the 2.68 bits per weight copy, which has the most bits of the three. For experts, the card calls these SC quants and plots KL divergence and perplexity against VRAM in two images. It says more quants and more benchmarks will follow. A fixed baseline score on AIME-25 was assumed, because the author found no source. ```flow The DeepSeek-V4.1-Flash-1017 base model EXL3 compresses its weights to fewer bits You pick the 1.68, 2.25 or 2.68 size ExLlamaV3 loads it for chat ``` Good to know: The card scores each copy on AIME-25 and GPQA-D. The 1.68 copy gets 96.7% on AIME-25, and the other two get 100%. On GPQA-D the copies range from 89.9% to 90.4%. The baseline GPQA-D score of 90.2% comes from OpenRouter, and the AIME-25 baseline of 100% is an assumption. ```chart Title: GPQA-D score by size 1.68 bits per weight: 90.4% 2.25 bits per weight: 90.4% 2.68 bits per weight: 89.9% Baseline: 90.2% ``` If you see corrupted output on short prompts, the card says it is likely a failure mode of the model itself. - License: MIT, as the Hugging Face record lists it. - Cost to run: no API key or account is named. The card gives no hardware needs. - Data it reaches: downloading reaches Hugging Face. The card does not describe the model reaching files or the web. - Not for: older ExLlamaV3 versions, since the card says it requires version 1.6.6 or the dev branch. ## On Hugging Face, 13 November 2026 - Task: image text to text - Downloads in the last 30 days: 581 - Likes: 30 - License: mit - Based on: deepseek-ai/DeepSeek-V4.1-Flash-1017 - Tags: exl3, image-text-to-text ## Links - Model: https://huggingface.co/turboderp/DeepSeek-V4.1-Flash-1017-exl3 - Files: https://huggingface.co/turboderp/DeepSeek-V4.1-Flash-1017-exl3/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download turboderp/DeepSeek-V4.1-Flash-1017-exl3 ``` 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 [turboderp/DeepSeek-V4.1-Flash-1017-exl3 on Hugging Face](https://huggingface.co/turboderp/DeepSeek-V4.1-Flash-1017-exl3) ## 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-V4.1-Flash-1017-exl3 (this page) | 581 | 30 | — | mit | 2026-11-02 | | [DeepSeek-V4.1-Flash-1017 EXL3 EAQ v1 2.9 bpw](https://mindket.com/p/e2aa9049-7d40-46f6-b187-57f9ffaaccf1) | 8,113 | 46 | 105B | mit | 2026-10-20 | | [GLM-5.3-Flash EXL3 4bpw for TensorFold](https://mindket.com/p/dc92b67d-d498-4170-8472-86e468d8b950) | 4,746 | 73 | 87.8B | apache-2.0 | 2026-10-03 | | [GLM-5.3-Flash EXL3 Spark](https://mindket.com/p/ec88db01-e5e6-420e-9171-26462734e98f) | 399 | 11 | 48B | mit | 2026-09-18 | | [GLM-5.3-Flash EXL3 3.5bpw](https://mindket.com/p/aa1e775c-766f-4b32-94d9-711360cd2ea8) | 140 | 4 | 78.1B | apache-2.0 | 2026-10-30 | | [Qwen3.8-27B-exl3](https://mindket.com/p/d2e0df1d-c113-4645-8870-2ce9b0728428) | 5,032 | 203 | — | apache-2.0 | 2026-08-27 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2026-11-02 - First published: 2026-10-29 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open turboderp/DeepSeek-V4.1-Flash-1017-exl3 on Hugging Face](https://huggingface.co/turboderp/DeepSeek-V4.1-Flash-1017-exl3) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/93187f0c-ef38-4d6a-b054-9370ad7daaed) - [Its page on Mindket](https://mindket.com/p/93187f0c-ef38-4d6a-b054-9370ad7daaed)