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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="unsloth/DeepSeek-V4-Flash-GGUF",
	filename="UD-IQ1_M/DeepSeek-V4-Flash-UD-IQ1_M-00001-of-00003.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 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 Studio

How to use unsloth/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 unsloth/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 unsloth/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 unsloth/DeepSeek-V4-Flash-GGUF to start chatting

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 @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": "unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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 new

OpenClaw new

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"

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

Finally! 🎉

by agikeen - opened 7 days ago

Discussion

agikeen

7 days ago

Finally got the Unsloth-quantized DeepSeek-V4-Flash! Huge thanks to the team — amazing work as always. 🦥🚀

danielhanchen

Unsloth AI org 7 days ago

Not finalized yet, please wait for our announcement :)

danielhanchen

Unsloth AI org 5 days ago

•

edited 5 days ago

Hey guys it's finally here and ready to run!! 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: @Roland26 @adeebaldkheel @scorpionshoes @Maternion @Linukso1D @ParadigmComplex @agikeen @elpirater312 @maglat @JermemyHaschal @QyrouNnet-AI @Nooalt @agikeen

maglat

5 days ago

•

edited 5 days ago

Hey hey @danielhanchen ,

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

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