⚡ Each donation funds the next large quant.

I host free GGUF or MoE quants as independent research.
Local hardware: Mechrevo Kuangshi GM7AG0M — RTX 3060 Laptop 6GB GDDR6, 64GB DDR5, i7-12700H (14C/20T, 4.7GHz), Windows 11, Samsung 990 Pro.
Good for imatrix and 0.6–35B-class work in RAM. 9B+ and searches need rented H200/Blackwell, typically $100 per quant.

🎉 Boosty🦄  |  ☕ Buy Me a Coffee🦄  |  ⭐ DonationAlerts🦄

💚 Thanks to Hugging Face for extra storage.🦄

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NOESIS / AMAImedia

Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform (framework: DHCF-FNO — Deterministic Hybrid Control Framework for Frozen Neural Operators)

• Founder: Ilia Bolotnikov
• Organization: AMAImedia.com
• X (Twitter): @AMAImediacom
• LinkedIn: Ilia Bolotnikov
• Telegram: @djbionicl
• Release date: 2026-10-17

AMAImedia

• Original repository: deepseek-ai/DeepSeek-V4.1-Flash-1017

• Released GGUF repository: mrfakename/DeepSeek-V4.1-Flash-1017-GGUF

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DeepSeek-V4.1-Flash-1017

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Technical Report 👁️

Introduction

DeepSeek-V4.1-Flash-1017 is a minor update to DeepSeek-V4.1-Flash, delivering substantial improvements across reasoning, coding, agentic tasks, and visual benchmarks. Alongside these gains, the model reduces task execution time and output length, enabling users to complete tasks faster and at lower cost and advancing the performance–cost Pareto frontier across multiple agent harnesses.

† denotes the text-only subset of HLE. We use the Claude Code harness for Terminal-Bench 4.0 and the Mini-SWE-V2 harness for DeepSWE v1.1.

Prompt Encoding

This release does not include a Jinja-format chat template. The encoding folder contains a self-contained Python reference implementation (encoding.py) with test cases for multi-turn conversations, tool calling, thinking mode, numeric reasoning effort, mid-conversation system messages, and interleaved image content.

For production use, we additionally release deepseek-recipe, a set of Rust libraries with Python bindings that provides the same prompt format as a maintained, protocol-aware toolkit. It converts Messages, Chat Completions, and Responses API requests into the Conversation format, encodes them into DeepSeek V4 and V4.1 prompts or token IDs, and parses model output back into complete or streamed responses — covering thinking, tool calls, images, and generation settings. Model inference, tool execution, and HTTP transport are left to the caller.

Minimal Inference

Please refer to the inference folder for instructions on weight conversion and running inference locally.

Recommended sampling parameters:

License

This repository and the model weights are licensed under the MIT License.

Citation

@misc{deepseekai2026deepseekv41flash,
      title={DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression},
      author={DeepSeek-AI},
      year={2026},
}

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DeepSeek-V4.1-Flash-1017 GGUF

GGUF quants of deepseek-ai/DeepSeek-V4.1-Flash-1017.

These are plain quants with no imatrix. Each folder holds a split GGUF. Point llama.cpp at the -00001-of- file and it loads the rest.

Running

Mainline llama.cpp doesn't support DeepSeek V4.1 yet. These files were made with the branch from ggml-org/llama.cpp#28696 at commit 2076412, and they need that branch or a build that includes it.

git clone https://github.com/vcruz305/llama.cpp && cd llama.cpp
git checkout 2076412598e99127ea2554467b2b7530856895d3
cmake -B build && cmake --build build -j

The two engram tables are large and read a few rows per token. Keeping them on the CPU works well, for example -ot "engram_embd.weight=CPU". The PR has a tested command line.

Text only for now. The llama.cpp branch can't convert the vision tower yet.

Acknowledgements

Huge thanks to Hugging Face for generously sponsoring the compute used for this project 🤗

x.com/realmrfakename

Contact

If you have any questions, please raise an issue or contact us at service@deepseek.com.