bartowski/DeepSeek-V4-Flash-0731-GGUF overview

---

quantized_by: bartowski

pipeline_tag: text-generation

license: mit

base_model_relation: quantized

base_model: deepseek-ai/DeepSeek-V4-Flash-0731

---

Llamacpp Quantizations of DeepSeek-V4-Flash-0731 by deepseek-ai

Using <a href="https://github.com/ggml-org/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggml-org/llama.cpp/releases/tag/b10173">b10173</a> for quantization.

Original model: https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731

This model is in MXFP4 and as such has only been provided in MXFP4 format!

Other sizes may be provided after some investigation.

Model details:

• Parameter count: 284B
• Input support: text
• MTP: yes - details
• imatrix: no (for now)

How to run

Prompt format

No chat template specified so default is used. This may be incorrect, check original model card for details.

{system_prompt}<｜User｜>{prompt}<｜Assistant｜></think>

Download the MXFP4 files:

| Filename | Quant type | File Size | Split | Description |

| -------- | ---------- | --------- | ----- | ----------- |

| DeepSeek-V4-Flash-0731-MXFP4.gguf | MXFP4 | 156.38GB | true | Original quality. |

Downloading using the Hugging Face CLI

<details>

<summary>Click to view download instructions</summary>

First, make sure you have the Hugging Face CLI installed:

pip install -U "huggingface_hub[cli]"

The files marked true in the Split column above are stored as multiple parts in a folder. To download all the parts to a local folder, run:

hf download bartowski/DeepSeek-V4-Flash-0731-GGUF --include "DeepSeek-V4-Flash-0731-MXFP4/*" --local-dir ./

You can either specify a new local-dir (DeepSeek-V4-Flash-0731-MXFP4) or download them all in place (./)

</details>

How to run

These quants run with llama.cpp - installable in one line via llama.app:

curl -LsSf https://llama.app/install.sh | sh
llama-server -hf bartowski/DeepSeek-V4-Flash-0731:MXFP4

llama-server includes a built-in chat web UI, served at http://localhost:8080 by default.

These quants were made with llama.cpp release b10173 - if this model's architecture is newly supported, you'll need that release or newer to run them.

They also work in: LM Studio · koboldcpp · ramalama · Jan AI · Text Generation Web UI · LoLLMs · Atomic Chat

MTP (DSpark)

This model has MTP (Multi-Token Prediction) weights in the form of a DSpark draft model. Unlike other MTP implementations, these are not included in the quants themselves - they are provided as a separate file in this repo: dspark-DeepSeek-V4-Flash-0731-MXFP4.gguf

DSpark acts as a draft model, letting llama.cpp run speculative decoding for faster generation. To use it, add the following flag to your llama.cpp command:

--spec-type draft-dspark

When running with -hf as shown above, llama.cpp downloads the DSpark file automatically alongside the model. If you're downloading files manually instead, also grab the dspark file and pass it with -md /path/to/dspark-DeepSeek-V4-Flash-0731-MXFP4.gguf.

Note: the DSpark file keeps the original model's MXFP4 expert weights unchanged (remaining tensors at Q8_0 or higher).

Credits

Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.

Thank you ZeroWw for the inspiration to experiment with embed/output.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski