DeepSeek-V4.1-Flash-1017-GGUF

Read our How to Run DeepSeek-V4.1 Flash Guide!#

See Unsloth Dynamic 3.0 GGUFs for our quantization benchmarks.

https://github.com/unslothai/unsloth/ https://discord.gg/unsloth https://unsloth.ai/docs/models/deepseek-v4.1-flash

Image: deepseek-v4.1-flash unsloth desktop Run on a 128GB+ RAM device for the smallest 1-bit, 3-bit on 256GB or full precision on 320GB. Read Guide

To run, please use our llama.cpp PR or the Unsloth Desktop app.

Get up to 3x faster inference with Unsloth vs other ngram/CPU/VRAM repos./

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Quantization analysis, KLD plots and other analysis is here.

To swap the engram (MXFP8 to Q6_K / Q5_K / IQ4_NL, see this guide)

DeepSeek-V4.1-Flash-1017#

Image: DeepSeek-V4.1

Homepage Chat

Hugging Face Twitter Follow

License

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},
}

Contact#

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