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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use unsloth/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 unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL

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 unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL

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 unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL

LM Studio
Jan
Ollama
How to use unsloth/DeepSeek-V4-Flash-GGUF with Ollama:
```
ollama run hf.co/unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL
```
Unsloth Desktop

How to use unsloth/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 unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use unsloth/DeepSeek-V4-Flash-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.DeepSeek-V4-Flash-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use unsloth/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 unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL

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 unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use unsloth/DeepSeek-V4-Flash-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Please wait for official announcement before using!

by danielhanchen - opened Jul 7

Discussion

danielhanchen

Unsloth AI org Jul 7

Hey guys, as said previously, the GGUFs are not finalized until we announce them, there is more issues with the the inference implementation than we originally anticipated. We are trying to see if can push PRs to llama.cpp to fix the issues.

Deenn

Jul 7

•

edited Jul 7

It would be great if in the future you could create your own repository with an updating branch for testing.

danielhanchen

Unsloth AI org Jul 7

It would be great if in the future you could create your own repository with an updating branch for testing.

We can't as Hugging Face as upload limits for private storage. Next time we might use another account. Nevertheless the issue was not the GGUFs, but rather the backend implementation itself.

Deenn

Jul 7

•

edited Jul 7

Thanks for the answer, by repository I specified about GitHub with actual branch. Now we need to compile several alternative patches for running with 8-bit KV settings, MOE offloading on multiple GPUs, etc. for each DS4 tests.
Regards.

shimmyshimmer

Unsloth AI org Jul 7

•

edited Jul 8

We found DeepSeek-V4 issues in llama.cpp that caused gibberish after the 2nd turn. The cause was broken prompt caching. To run correctly, please use the latest llama.cpp version.
We also improved the DeepSeek-V4 chat jinja template, and tested over 4000 conversations to be equivalent with the official baseline.

Guide: https://unsloth.ai/docs/models/deepseek-v4
GGUF: https://huggingface.co/unsloth/DeepSeek-V4-Flash-GGUF

You can run DeepSeek-V4-Flash with all our fixes and Thinking toggles via Unsloth Studio:
deepseek-v4-flash in unsloth studio

llama.cpp added DeepSeek V4 support in #24162 - we noticed that when using any GGUF from any provider, multi turn conversations would not function well when compared to DS4's Hugging Face baseline. llama.cpp uses --ctx-checkpoints N which allowed it to do prefix caching to save inference costs. Instead of re-processing every prompt again on the 2nd, nth ask, we can use KV caching. However we found DS4 needed --ctx-checkpoints 0 or else you will get gibberish. Please use the latest version of llama.cpp to get fixes.

Engine	Score	Calculation	Tool selection	Parallel Tools	Multi Turn tools	Nested tools
Official code	15/15	3	3	3	3	3
Any provider	4/15	1	2	0	0	1
After our fix	15/15	3	2	3	3	3

Thanks guys and feel free to support our Tweet, Linkedin post or Reddit post

CC: @LaikaFramework @bacchio @klwjack @s3nh @ParadigmComplex @nv-rush @AlanSilvaTech @CHHORVORN

shimmyshimmer changed discussion status to closed Jul 8

maglat

Jul 8

Hey hey,

I am a bit curious and need advice. (My setup is 8 RTX3090s)
Before official llama.cpp received the fixes I ran your Q8 GGUF with following llama.cpp fork https://github.com/fairydreaming/llama.cpp.git

With that fork, I am able to let it run Q8 GGUF at 350k context with -b 2048 -ub256
Now I tried to run the same Q8 GGUF with the latest llama.cpp official release I get OOM with the same startup command. I have to lower the context to 100k to get it running.

This is my startup command I use with llama fork is as follows.

./build/bin/llama-server
-m /mnt/extra/models/deepseek4flash/DeepSeek-V4-Flash-UD-Q8_K_XL-00001-of-00005.gguf
--host 0.0.0.0
--port 8788
--alias MainLLM
-ngl 99
-fa on
--no-mmap
--jinja
--ctx-checkpoints 0
-c 250000
-b 2048
-ub 256
--cache-type-k q8_0
--cache-type-v q8_0
--parallel 1

Deenn

Jul 8

maglat

Try just applying the latest commit "pull/25402/head:deepseek-v4-checkpointing-fix" from llama.cpp as a patch to https://github.com/fairydreaming/llama.cpp/tree/dsv4 to work with checkpoints.
There is no actual branch yet exist.

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