Instructions to use tarruda/DeepSeek-V4-Flash-Vision-Exp-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 tarruda/DeepSeek-V4-Flash-Vision-Exp-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 tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF:IQ3_XXS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF:IQ3_XXS

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 tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./llama-cli -hf tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF:IQ3_XXS

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 tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF:IQ3_XXS

Use Docker

docker model run hf.co/tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF:IQ3_XXS

LM Studio
Jan

vLLM

How to use tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF:IQ3_XXS

Ollama
How to use tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF with Ollama:
```
ollama run hf.co/tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF:IQ3_XXS
```
Unsloth Desktop

How to use tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF:IQ3_XXS

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": "tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF:IQ3_XXS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF with Docker Model Runner:
```
docker model run hf.co/tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF:IQ3_XXS
```

Lemonade

How to use tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF:IQ3_XXS

Run and chat with the model

lemonade run user.DeepSeek-V4-Flash-Vision-Exp-GGUF-IQ3_XXS

List all available models

lemonade list

Hermes Agent

How to use tarruda/DeepSeek-V4-Flash-Vision-Exp-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 tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF:IQ3_XXS

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 tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF:IQ3_XXS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF:IQ3_XXS

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 "tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF:IQ3_XXS" \
  --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"

DeepSeek V4 Flash Vision Exp GGUF

WARNING: This is not currently supported by llama.cpp master and requires my experimental branch to use https://github.com/tarruda/llama.cpp/tree/dsv4-vision-exp-support

GGUF quantizations for deepseek-ai/DeepSeek-V4-Flash-Vision-Exp.

DeepSeek published the original model weights in MXFP4, so the MXFP4 GGUFs in this repo are direct conversions of those original safetensors.

Quant Recipes

Recipe	Quant size	Default type	Expert tensor type
`Q2_K`	95464.96 MiB (2.82 BPW)	`Q8_0`	`Q2_K`
`IQ3_XXS`	109912.96 MiB (3.24 BPW)	`Q8_0`	`IQ3_XXS`
`Q3_K`	122296.96 MiB (3.61 BPW)	`Q8_0`	`Q3_K`

For every quant recipe, ffn_down_exps, ffn_gate_exps, and ffn_up_exps use the recipe's expert type. Compressor, indexer, token embedding, output, hyper-connection, router-bias, and token-to-expert mapping tensors retain the BF16, F32, or I32 types specified in scripts/quantize.sh. All three quants use the included 726-entry, 812-chunk importance matrix.

Usage

model="./IQ3_XXS/DeepSeek-V4-Flash-Vision-Exp-IQ3_XXS-00001-of-00004.gguf"
mmproj="./mmproj-DeepSeek-V4-Flash-Vision-Exp-BF16.gguf"

ctx=262144
parallel=1

llama-server --no-mmap --no-warmup \
  --model "$model" --mmproj "$mmproj" \
  ---ctx-size $ctx_size -np $parallel \
  --repeat-penalty 1.0  --presence-penalty 0.0 \
  --chat-template-kwargs '{"reasoning_effort": "max"}' \
  --reasoning-preserve \
  -b 2048 -ub 2048 \
  --cache-ram 1024 -ctxcp 12 \
  --temp 1.0 --top-p 1.0

Downloads last month: -

GGUF

Model size

284B params

Architecture

deepseek4

Hardware compatibility

2-bit

3-bit

4-bit

View +1 variant

Model tree for tarruda/DeepSeek-V4-Flash-Vision-Exp-GGUF

Base model

deepseek-ai/DeepSeek-V4-Flash-Vision-Exp

Quantized

(8)

this model