DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF

Instructions to use AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF")
model = AutoModelForCausalLM.from_pretrained("AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-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 AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF
# Run inference directly in the terminal:
llama cli -hf AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF
# Run inference directly in the terminal:
llama cli -hf AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-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 AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF
# Run inference directly in the terminal:
./llama-cli -hf AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-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 AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF

Use Docker

docker model run hf.co/AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF

LM Studio
Jan

vLLM

How to use AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-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": "AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-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/AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF

SGLang

How to use AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-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 images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF with Ollama:
```
ollama run hf.co/AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF
```
Unsloth Desktop

How to use AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF

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": "AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF with Docker Model Runner:
```
docker model run hf.co/AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF
```

Lemonade

How to use AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF

Run and chat with the model

lemonade run user.DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-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 AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-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 AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF

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 "AMAImedia/DeepSeek-V4-Flash-Vision-Exp-FP8-GGUF" \
  --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-FP8-GGUF / encoding /README.md

AMAImedia

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

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DeepSeek-V4 text and vision encoding

encoding_dsv4.py is the standalone prompt-format reference. It supports multi-turn conversations, tool calls, thinking modes, and interleaved image content blocks without importing the inference implementation.

OpenAI-style messages

from encoding_dsv4 import encode_messages

messages = [{
    "role": "user",
    "content": [
        {"type": "text", "text": "第一张图"},
        {
            "type": "image_url",
            "image_url": {"url": "examples/images/carrots.jpeg"},
        },
        {"type": "text", "text": "有什么内容？"},
    ],
}]

# non-thinking
prompt, media = encode_messages(
    messages,
    thinking_mode="chat",
    return_multi_modal_data=True,
)
# prompt:
# '<｜begin▁of▁sentence｜><｜User｜>第一张图\n\n<｜deepseek_image｜>\n\n有什么内容？<｜Assistant｜></think>'


# # thinking with `max` reasoning_effort
# prompt, media = encode_messages(
#     messages,
#     thinking_mode="thinking",
#     reasoning_effort="max",
#     return_multi_modal_data=True,
# )
# prompt:
# <｜begin▁of▁sentence｜>Reasoning Effort: Beyond maximum — exhaustive, relentless, and uncompromising.\nYou MUST reason with the utmost depth and rigor, leaving absolutely nothing to chance: exhaustively decompose the problem into its most fundamental components, trace every causal chain to its root, and resolve the underlying cause rather than any surface symptom.\nDo not stop reasoning until you have independently verified the solution from multiple angles and are certain that no assumption remains unchecked and no error remains undiscovered.\n\n<｜User｜>第一张图\n\n<｜deepseek_image｜>\n\n有什么内容？<｜Assistant｜><think>

Images are represented in the prompt by <｜deepseek_image｜>. media["images"] contains the corresponding image records in exactly the same order. Pixel loading and expansion into model image tokens are handled by inference/image_processor.py.

Compact TXT notation

parse_tagged_text() converts a compact prompt such as

第一张图<image>examples/images/carrots.jpeg</image>有什么内容？

into the same standard content blocks. It is an input convenience layer, not a second encoding implementation.

Tests

From the repository root:

python -m pytest -q encoding/test_encoding_dsv4.py

The tests include a check that the TXT and JSON examples encode to the same prompt and preserve the same two-image ordering.