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 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

My output is garbled

by pliskin123 - opened 28 days ago

Discussion

pliskin123

28 days ago

I'm using the latest llama.cpp (b9893), but the output is garbled. Why is this happening? Is it a chat template issue? The startup parameters I'm using are as follows:
"%EXE_PATH%" ^
-m "%MODEL_PATH%" ^
--host 0.0.0.0 ^
--port %user_port% ^
--keep -1 ^
-np 1 ^
--mlock ^
-fa on ^
--cache-type-k %k_type% ^
--cache-type-v %v_type% ^
--temp %temperature% ^
--fit-ctx %ctx_val% --fit on -v

lsm03624

28 days ago

me too

danielhanchen

Unsloth AI org 28 days ago

as we said 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 issue

danielhanchen

Unsloth AI org 27 days ago

Hey I think we fixed it see https://unsloth.ai/docs/models/deepseek-v4#llama.cpp-deepseek-v4-implementation-fixes

shimmyshimmer

Unsloth AI org 27 days ago

•

edited 27 days ago

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: @pliskin123 @lsm03624

shimmyshimmer changed discussion status to closed 27 days ago

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