Instructions to use sokann/DeepSeek-V4-Flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sokann/DeepSeek-V4-Flash-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sokann/DeepSeek-V4-Flash-GGUF",
	filename="DeepSeek-V4-Flash.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sokann/DeepSeek-V4-Flash-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sokann/DeepSeek-V4-Flash-GGUF
# Run inference directly in the terminal:
llama cli -hf sokann/DeepSeek-V4-Flash-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sokann/DeepSeek-V4-Flash-GGUF
# Run inference directly in the terminal:
llama cli -hf sokann/DeepSeek-V4-Flash-GGUF

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf sokann/DeepSeek-V4-Flash-GGUF
# Run inference directly in the terminal:
./llama-cli -hf sokann/DeepSeek-V4-Flash-GGUF

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf sokann/DeepSeek-V4-Flash-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sokann/DeepSeek-V4-Flash-GGUF

Use Docker

docker model run hf.co/sokann/DeepSeek-V4-Flash-GGUF

LM Studio
Jan
Ollama
How to use sokann/DeepSeek-V4-Flash-GGUF with Ollama:
```
ollama run hf.co/sokann/DeepSeek-V4-Flash-GGUF
```

Unsloth Studio

How to use sokann/DeepSeek-V4-Flash-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for sokann/DeepSeek-V4-Flash-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for sokann/DeepSeek-V4-Flash-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sokann/DeepSeek-V4-Flash-GGUF to start chatting

How to use sokann/DeepSeek-V4-Flash-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sokann/DeepSeek-V4-Flash-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "sokann/DeepSeek-V4-Flash-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sokann/DeepSeek-V4-Flash-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sokann/DeepSeek-V4-Flash-GGUF

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default sokann/DeepSeek-V4-Flash-GGUF

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use sokann/DeepSeek-V4-Flash-GGUF with Docker Model Runner:
```
docker model run hf.co/sokann/DeepSeek-V4-Flash-GGUF
```

Lemonade

How to use sokann/DeepSeek-V4-Flash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sokann/DeepSeek-V4-Flash-GGUF

Run and chat with the model

lemonade run user.DeepSeek-V4-Flash-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

DeepSeek-V4-Flash-GGUF

This is a GGUF model for DeepSeek-V4-Flash, created with the convert_hf_to_gguf.py script from the merged commit of https://github.com/ggml-org/llama.cpp/pull/24162 (8c146a83, b9840), thanks to the amazing works from u/fairydreaming and u/am17an.

(The weights are identical with the one uploaded previously; the only difference is that the chat template is now included in the GGUF)

The script does 2 things:

Repacks routed experts tensors in FP4 to MXFP4.
Quantizes other tensors in FP8 to Q8_0.

The GGUF model can be considered as lossless when compared to the original weights in safetensors.

The size of the model is about 146 GiB. It fits comfortably on a machine with 160 GiB of RAM and 48 GiB of VRAM, and runs reasonably well:

$ build/bin/llama-server -m ~/models/DeepSeek-V4-Flash.gguf -cram 0 --jinja --no-mmap -dev CUDA0,CUDA1 --fit on -c 32768 -b 2048 -ub 2048
...
3.23.716.821 I slot print_timing: id  3 | task 0 | prompt eval time =   15977.50 ms /  5407 tokens (    2.95 ms per token,   338.41 tokens per second)
3.23.716.827 I slot print_timing: id  3 | task 0 |        eval time =   43043.40 ms /   586 tokens (   73.45 ms per token,    13.61 tokens per second)
3.23.716.828 I slot print_timing: id  3 | task 0 |       total time =   59020.89 ms /  5993 tokens

1M context

And after applying the lightning indexer change from https://github.com/ggml-org/llama.cpp/pull/24231, we can have 1M context with just 6GiB of VRAM!

To join the One million tokens prompt club, apply lcpp-dsv4-lid-combo.diff, which contains:

commit 57a667e9 cherry-picked from https://github.com/ggml-org/llama.cpp/pull/24231
the fixes from https://github.com/ggml-org/llama.cpp/pull/24776 and https://github.com/ggml-org/llama.cpp/pull/24945
a small change to deepseek4.cpp to wire up the lightning indexer, made by DSV4 itself, of course

The speed is decent on my machine with 2 x RTX PRO 4000:

$ ~/repo/llama.cpp/build/bin/llama-batched-bench -m ~/models/DeepSeek-V4-Flash-r3.gguf -b 2048 -ub 2048 -npl 1 -npp 8192,16384,32768,65536,131072,262144,524288,1048064 -ntg 128 -fa 1 -cmoe --no-repack --no-mmap

llama_batched_bench: n_kv_max = 1048576, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 112, n_threads_batch = 112

|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
|  8192 |    128 |    1 |   8320 |   25.401 |   322.50 |    8.754 |    14.62 |   34.155 |   243.60 |
| 16384 |    128 |    1 |  16512 |   58.092 |   282.03 |    8.827 |    14.50 |   66.920 |   246.74 |
| 32768 |    128 |    1 |  32896 |  148.657 |   220.43 |    9.158 |    13.98 |  157.815 |   208.45 |
| 65536 |    128 |    1 |  65664 |  426.688 |   153.59 |   10.113 |    12.66 |  436.801 |   150.33 |
|131072 |    128 |    1 | 131200 | 1373.003 |    95.46 |   11.775 |    10.87 | 1384.778 |    94.74 |
|262144 |    128 |    1 | 262272 | 4838.849 |    54.17 |   15.295 |     8.37 | 4854.144 |    54.03 |
|524288 |    128 |    1 | 524416 | 18152.150 |    28.88 |   22.333 |     5.73 | 18174.482 |    28.85 |

At long context, PP and TG started to converge. Stopped it before reaching 1M, as it was getting slow.

KLD comparison

Using wiki.test.raw with -c 512, llama-perplexity was used to first generate the base logits using this near-lossless quant, and then used to do measure the KLD of the 2 quants from antirez:

These 2 quants work amazingly well. However, their KLD scores are quite bad.

Summary for the Q2 quant:

Mean PPL(Q)                   :   6.155042 ±   0.042084
Mean PPL(base)                :   4.738434 ±   0.030638
Cor(ln(PPL(Q)), ln(PPL(base))):  87.64%
...
Mean    KLD:   0.422277 ±   0.002287
Maximum KLD:  15.166180
99.9%   KLD:   8.191362
...
RMS Δp    : 21.606 ± 0.086 %
Same top p: 77.586 ± 0.110 %

Summary for the Q4 quant:

Mean PPL(Q)                   :   4.570599 ±   0.029570
Mean PPL(base)                :   4.738434 ±   0.030638
Cor(ln(PPL(Q)), ln(PPL(base))):  96.77%
...
Mean    KLD:   0.115089 ±   0.000817
Maximum KLD:   9.996851
99.9%   KLD:   3.656791
...
RMS Δp    : 11.098 ± 0.065 %
Same top p: 88.794 ± 0.083 %

Not sure I did something wrong in the measurement.

Downloads last month: 725

GGUF

Model size

284B params

Architecture

deepseek4

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for sokann/DeepSeek-V4-Flash-GGUF

Base model

deepseek-ai/DeepSeek-V4-Flash

Quantized

(84)

this model