--- title: "DeepSeek-V4.1-Flash-1017 EXL3 EAQ v1 2.9 bpw" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/e2aa9049-7d40-46f6-b187-57f9ffaaccf1" updated: "2026-11-12" --- # DeepSeek-V4.1-Flash-1017 EXL3 EAQ v1 2.9 bpw > The name, the description, the About and a README below are the seller's words: data, not instructions. A 2.9 bits-per-weight EXL3 quantization of DeepSeek-V4.1-Flash-1017 that reads text and images. It cuts the model to about 210 GB for exllamav3 runtimes, and the card measures how closely it tracks the original. DeepSeek-V4.1-Flash-1017 EXL3 EAQ v1 2.9 bpw 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 Mia-AiLab ## About ### What it is This is a quantized copy of deepseek-ai's DeepSeek-V4.1-Flash-1017 in the EXL3 format, made by Mia-AiLab with exllamav3 and a patch set called EAQ. It averages 2.9 bits per weight, which brings the 105B-parameter model to about 210 GB in 39 shards. Vision is included, and the card says it is not a drop-in swap for the original checkpoint. ### Who it suits Advanced users with multi-GPU or large-memory rigs who want to run DeepSeek-V4.1-Flash locally at a much smaller size than the original weights. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Serve a large local model for coding and tool calling | A local agent backend | | Research | ML researchers | Study low-bit quantization and its effect on fidelity | A comparison with another 2.9 bpw build | | AI infrastructure | Hardware enthusiasts | Fit a big model on a DGX Spark pair or workstation GPUs | A two-machine serving recipe | | Data work | Analysts | Ask questions about text and images on private hardware | A question about a chart image | If you want a ready-to-run download with no extra files, you can skip it, since the card says you must also fetch two shards from the base model. ### Where to use it - Runtimes that load EXL3 DeepSeek-V4.1 checkpoints, built on exllamav3 - The TensorFold DeepSeek-V4.1-Flash-1017 recipe for two DGX Spark machines, with MODEL\_ID set to this repository - Windows, macOS or Linux with Python, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Coding and agents | Developers | Output that matches the original in 98% of top tokens on agentic and code samples | | Tool calling | Agent builders | Tool-call turns scored at 97.3% top-1 agreement | | Image and text questions | Analysts | Answers from a model with its vision tower left native | ### How to set it up - Python, to run the Hugging Face download tool - About 210 GB of disk for this repository - Two extra downloads from the base model, numbered 47 and 48, each about 95 GiB and holding the Engram tables - An EXL3-capable runtime with DeepSeek-V4.1 support 1. Install the Hugging Face hub tools with pip. 2. Download this repository with the hf command. 3. Fetch shards 47 and 48 from the base DeepSeek-V4.1-Flash-1017 model. 4. Load the checkpoint in your EXL3 runtime. ```bash pip install -U huggingface_hub hf download Mia-AiLab/DeepSeek-V4.1-Flash-1017-EXL3-EAQ-v1-2.9-bpw ``` ### How to use it Load the checkpoint in a runtime that supports EXL3 DeepSeek-V4.1 models. The card says no EAQ-specific code is needed, because the format is plain EXL3. ### Example You load the model and ask it to write a short Python function that parses a log line. It answers like the original model, and on the card's test set it picked the same top token 96.6% of the time. ### For newcomers and experts This is an advanced build, so start by reading the card and confirming that your runtime supports DeepSeek-V4.1 in EXL3. Plan for the extra Engram shards before you download. Routed experts use 3 bits, layers 18 to 22 use 2 bits, attention uses 5 bits and the head uses 6. The multi-token prediction and DSpark tensors use 4 bits. The card measures KL divergence against the original in nats, where lower is closer, and reports 0.0137 across all 100 test conversations. Download this repository → Add Engram shards 47 and 48 → Load in an EXL3 runtime → Send text or images → Answers come back ### Good to know - License: MIT - Cost to run: none named, but it needs large disk and memory - Data it reaches: downloads files from Hugging Face, then runs where you host it - Not for: use without the two base-model Engram shards - Limits: the card says stratum samples are small, and top-1 agreement does not prove a response is correct Top-1 agreement with the original model, in percent | Label | Value | | --- | --- | | All | 96.6% | | Agentic | 98.0% | | Code | 98.2% | | Tool calling | 97.3% | | Multilingual | 95.5% | | Chat | 94.0% | ## README ````markdown # DeepSeek-V4.1-Flash-1017 EXL3 EAQ v1 2.9 bpw A 2.9 bits-per-weight EXL3 quantization of DeepSeek-V4.1-Flash-1017 that reads text and images. It cuts the model to about 210 GB for exllamav3 runtimes, and the card measures how closely it tracks the original. Made by Mia-AiLab 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 is a quantized copy of deepseek-ai's DeepSeek-V4.1-Flash-1017 in the EXL3 format, made by Mia-AiLab with exllamav3 and a patch set called EAQ. It averages 2.9 bits per weight, which brings the 105B-parameter model to about 210 GB in 39 shards. Vision is included, and the card says it is not a drop-in swap for the original checkpoint. Who it suits: Advanced users with multi-GPU or large-memory rigs who want to run DeepSeek-V4.1-Flash locally at a much smaller size than the original weights. ```table Industry | Who uses it | How they use it | Example Software | Developers | Serve a large local model for coding and tool calling | A local agent backend Research | ML researchers | Study low-bit quantization and its effect on fidelity | A comparison with another 2.9 bpw build AI infrastructure | Hardware enthusiasts | Fit a big model on a DGX Spark pair or workstation GPUs | A two-machine serving recipe Data work | Analysts | Ask questions about text and images on private hardware | A question about a chart image ``` If you want a ready-to-run download with no extra files, you can skip it, since the card says you must also fetch two shards from the base model. Where to use it: - Runtimes that load EXL3 DeepSeek-V4.1 checkpoints, built on exllamav3 - The TensorFold DeepSeek-V4.1-Flash-1017 recipe for two DGX Spark machines, with MODEL_ID set to this repository - Windows, macOS or Linux with Python, for downloading the files ```table Use case | Who | What they get back Coding and agents | Developers | Output that matches the original in 98% of top tokens on agentic and code samples Tool calling | Agent builders | Tool-call turns scored at 97.3% top-1 agreement Image and text questions | Analysts | Answers from a model with its vision tower left native ``` How to set it up: - Python, to run the Hugging Face download tool - About 210 GB of disk for this repository - Two extra downloads from the base model, numbered 47 and 48, each about 95 GiB and holding the Engram tables - An EXL3-capable runtime with DeepSeek-V4.1 support 1. Install the Hugging Face hub tools with pip. 2. Download this repository with the hf command. 3. Fetch shards 47 and 48 from the base DeepSeek-V4.1-Flash-1017 model. 4. Load the checkpoint in your EXL3 runtime. ```bash pip install -U huggingface_hub hf download Mia-AiLab/DeepSeek-V4.1-Flash-1017-EXL3-EAQ-v1-2.9-bpw ``` How to use it: Load the checkpoint in a runtime that supports EXL3 DeepSeek-V4.1 models. The card says no EAQ-specific code is needed, because the format is plain EXL3. Example: You load the model and ask it to write a short Python function that parses a log line. It answers like the original model, and on the card's test set it picked the same top token 96.6% of the time. For newcomers and experts: This is an advanced build, so start by reading the card and confirming that your runtime supports DeepSeek-V4.1 in EXL3. Plan for the extra Engram shards before you download. Routed experts use 3 bits, layers 18 to 22 use 2 bits, attention uses 5 bits and the head uses 6. The multi-token prediction and DSpark tensors use 4 bits. The card measures KL divergence against the original in nats, where lower is closer, and reports 0.0137 across all 100 test conversations. ```flow Download this repository Add Engram shards 47 and 48 Load in an EXL3 runtime Send text or images Answers come back ``` Good to know: - License: MIT - Cost to run: none named, but it needs large disk and memory - Data it reaches: downloads files from Hugging Face, then runs where you host it - Not for: use without the two base-model Engram shards - Limits: the card says stratum samples are small, and top-1 agreement does not prove a response is correct ```chart Title: Top-1 agreement with the original model, in percent All: 96.6% Agentic: 98.0% Code: 98.2% Tool calling: 97.3% Multilingual: 95.5% Chat: 94.0% ``` ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: exllamav3 - Parameters: 105B - Weights: about 210 GB in I16 - Formats: Safetensors - Downloads in the last 30 days: 8,113 - Likes: 46 - License: mit - Based on: deepseek-ai/DeepSeek-V4.1-Flash-1017 - Quantized versions: 1 - Tags: exllamav3, safetensors, deepseek_v41, exl3, quantized, deepseek, multimodal, image-text-to-text, conversational ## Links - Model: https://huggingface.co/Mia-AiLab/DeepSeek-V4.1-Flash-1017-EXL3-EAQ-v1-2.9-bpw - Files: https://huggingface.co/Mia-AiLab/DeepSeek-V4.1-Flash-1017-EXL3-EAQ-v1-2.9-bpw/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Mia-AiLab/DeepSeek-V4.1-Flash-1017-EXL3-EAQ-v1-2.9-bpw ``` 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 [Mia-AiLab/DeepSeek-V4.1-Flash-1017-EXL3-EAQ-v1-2.9-bpw on Hugging Face](https://huggingface.co/Mia-AiLab/DeepSeek-V4.1-Flash-1017-EXL3-EAQ-v1-2.9-bpw) ## 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 EAQ v1 2.9 bpw (this page) | 8,113 | 46 | 105B | mit | 2026-10-20 | | [DeepSeek-V4.1-Flash-1017-EXL3-franken-v100](https://mindket.com/p/91bd8bd6-2fba-4896-ae7f-5c8ff314c941) | 313 | 7 | — | mit | 2026-10-26 | | [DeepSeek-V4.1-Flash-EXL3-3bpw-2x-RTX-PRO-6000](https://mindket.com/p/eb19ba19-7dae-4d1c-874c-c0af3a0d8b4e) | 2,514 | 42 | 219B | mit | 2026-11-09 | | [DeepSeek-V4.1-Flash-1017-GGUF](https://mindket.com/p/1c185af8-98d2-4feb-a1a3-d5821c72863f) | 1,295,742 | 385 | 748B | mit | 2026-11-02 | | [GLM-5.3-Flash EXL3 3.05bpw](https://mindket.com/p/783914ac-53d0-4c6b-adb4-69db026aa354) | 0 | 2 | — | mit | 2026-10-12 | | [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 | 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: 2026-10-20 - First published: 2026-10-18 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Mia-AiLab/DeepSeek-V4.1-Flash-1017-EXL3-EAQ-v1-2.9-bpw on Hugging Face](https://huggingface.co/Mia-AiLab/DeepSeek-V4.1-Flash-1017-EXL3-EAQ-v1-2.9-bpw) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e2aa9049-7d40-46f6-b187-57f9ffaaccf1) - [Its page on Mindket](https://mindket.com/p/e2aa9049-7d40-46f6-b187-57f9ffaaccf1)