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DeepSeek-V4.1-Flash-1017-GGUF

430k289

unsloth · 748B parameters · 4.1 TB · MIT

Address
unsloth/deepseek-v4.1-flash-1017-gguf
Held on
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Status
Verified
Base model
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Lineage
…
Unchanged from base
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Trending
#37
Downloads, 30 days
430k
Weights
4.1 TB
Revision

Quantization analysis, KLD plots and other analysis is here. To swap the engram (MXFP8 to Q6K / Q5K / IQ4NL, see this guide)

At a glance

Task
Vision language
Input
image, text
Output
text
Parameters
748B
Architecture
DeepSeek 41
Context
1M tokens
Format
GGUF
License
MITCommercial use
Base model
Quantized from deepseek-ai/DeepSeek-V4.1-Flash-1017
Released
Oct 2026
Updated
Oct 2026
Likes
243
Downloads, all time
351,192

Family

Models built on DeepSeek-V4.1-Flash-1017-GGUF.

Quantized10

Run it

Pinned to the indexed revision.

llama-server -hf unsloth/DeepSeek-V4.1-Flash-1017-GGUF

Through the hub: the same tools, each file from a source that is up (Hugging Face, ModelScope, IPFS), at this revision. The second line checks every file against its address.

export HF_ENDPOINT=https://gethologram.ai
cd "$(hf download unsloth/DeepSeek-V4.1-Flash-1017-GGUF --quiet)" && curl -s $HF_ENDPOINT/unsloth/DeepSeek-V4.1-Flash-1017-GGUF/resolve/main/SHA256SUMS | sha256sum -c --quiet

Spaces

Used in 2 Spaces.

BIGJUTT/model-pulsefastgpu/vram-and-gpu-cost-calculator
Read the full model card

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/raw/main/images/unsloth%20new%20logo.png)](https://github.com/unslothai/unsloth/)
[![](https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png)](https://discord.gg/unsloth)
[![](https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png)](https://unsloth.ai/docs/models/deepseek-v4.1-flash)



  ![deepseek-v4.1-flash unsloth desktop](https://cdn-uploads.huggingface.co/production/uploads/62ecdc18b72a69615d6bd857/NL1yOq6MuJeGmrEZckR_h.png)
Run on a 128GB+ RAM device for the smallest 1-bit, 3-bit on 256GB or full precision on 320GB. [Read Guide](https://unsloth.ai/docs/models/deepseek-v4.1-flash)
To run, please use our llama.cpp PR or the [Unsloth Desktop](https://unsloth.ai/docs/desktop) app.
Get up to 3x faster inference with Unsloth vs other ngram/CPU/VRAM repos./

DeepSeek-V4.1

Homepage Chat

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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.

Benchmark DS-V4.1-Flash-1017 DS-V4.1-Flash K3 GLM-5.3 GPT-5.6 Sol Opus 5
HLE w/o tools 42.6 (44.0†) 36.8 (39.1†) 43.5 42.0† 44.5 56.3
Terminal-Bench 4.0 42.3 31.2 12.6 37.9 39.9 51.8
DeepSWE v1.1 77.0 74.2 67.5 66.9 73.0 74.0
ProgramBench 29.1 20.3 17.5 19.0 23.0 37.0
NL2Repo-Bench 68.2 65.4 58.0 58.0 56.8 75.3
Automation-Bench v1.0.6 60.5 54.8 46.7 48.8 45.8 50.3
Agents’ Last Exam 35.4 31.8 27.6 28.5 26.7 28.6
Apex Bench v1.1 72.1 64.6 47.8 56.6 54.3 65.8
Chartography (w/tools) 83.0 78.9 68.1 — 79.9 84.0
BabyVision (w/tools) 92.5 89.6 85.7 — 88.9 94.1
ZeroBench-main (w/tools) 51.0 49.0 41.0 — 53.0 52.0

† 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:

Parameter Value
temperature 1.0
top_p 1.0
context_window 1M tokens
max_tokens ≥ 256K

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.

Derived on Oct 31, 2026 from Hugging Face at revision 8a6ec9cf, README.md .