Instructions to use orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-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 orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-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 orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF:MXFP4
# Run inference directly in the terminal:
llama cli -hf orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF:MXFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF:MXFP4
# Run inference directly in the terminal:
llama cli -hf orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF:MXFP4

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 orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF:MXFP4
# Run inference directly in the terminal:
./llama-cli -hf orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF:MXFP4

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 orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF:MXFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF:MXFP4

Use Docker

docker model run hf.co/orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF:MXFP4

LM Studio
Jan

vLLM

How to use orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-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": "orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF:MXFP4

Ollama
How to use orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF with Ollama:
```
ollama run hf.co/orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF:MXFP4
```
Unsloth Desktop

How to use orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF:MXFP4

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": "orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF:MXFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF:MXFP4
```

Lemonade

How to use orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF:MXFP4

Run and chat with the model

lemonade run user.DeepSeek-V4-Flash-Vision-Uncensored-GGUF-MXFP4

List all available models

lemonade list

Hermes Agent

How to use orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-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 orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF:MXFP4

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 orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF:MXFP4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF:MXFP4

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 "orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF:MXFP4" \
  --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"

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DeepSeek-V4-Flash-Vision-Uncensored-GGUF

GGUF conversion of the abliterated (refusal-removed) DeepSeek-V4-Flash-Vision-Exp — native MXFP4 experts, for llama.cpp

One Gateway. Every Model. — Route Smarter · Ship Safer · Spend Less.

Website · Model Catalog · GitHub · Discord · X

GGUF conversion of orcarouter/DeepSeek-V4-Flash-Vision-Uncensored — the abliterated (refusal-removed) build of DeepSeek's DeepSeek-V4-Flash-Vision-Exp, a ~305B / ~18B-active Mixture-of-Experts model with DeepSeek sparse attention, 4-wide Manifold-Constrained Hyper-Connections, a DSpark / MTP speculative head and a 1M-token context.

This is a format conversion, not a quantization. The 11,776 routed-expert matrices keep the base checkpoint's own FP4 codes — DeepSeek's per-32 ue8m0-scaled e2m1 layout is exactly GGUF's MXFP4 block format, so they are repacked bit-for-bit, not requantized. There is no Q2_K…Q6_K ladder here and no quality/size trade-off to pick from: one file set, the same weights the parent repo ships.

⚠️ Disclaimer — read before use

This model has had its safety alignment substantially removed via abliteration (orthogonalizing the refusal direction out of the residual stream). It will comply with harmful, unethical, or illegal requests that the original DeepSeek-V4-Flash-Vision-Exp would refuse. Released strictly for legitimate research — interpretability, AI-safety / refusal-mechanism study, red-teaming, and robustness evaluation. You assume full responsibility for how you use it and everything it generates; add your own safety and moderation layers before any deployment. Use must comply with the MIT License inherited from the base model and all applicable law. The authors accept no liability for misuse.

⚠️ Text only — vision is not included

The base checkpoint is a vision-language model. These GGUFs are text-only. llama.cpp has no graph implementation for DeepSeek-V4's vision tower — the only DeepSeek mmproj converters in the tree target DeepSeek-OCR, which expects a vision_config block and a SAM+CLIP tower that this checkpoint does not have. Rather than ship a broken projector, the converter drops the vision path entirely:

Dropped	Count	Why
`vision.*` (the ViT)	259	no llama.cpp graph for this tower
`aligner.*`	4	vision→language projector
`image_start` / `image_end` / `image_newline` / `image_pad`	4	learned image-marker vectors
`ffn.gate.bias_vl`	43	router bias applied only to image tokens
`mtp.*` (DSpark draft head)	4,708	no MTP inference path for this arch yet

llama-cli reports modalities : text on load. Vision will be added here once llama.cpp supports the tower upstream — the vision residual writers are abliterated in the parent weights, so nothing needs re-baking, only re-exporting. For image input today, use the parent repo with DeepSeek's own reference implementation.

Requirements — build a recent llama.cpp

The deepseek4 architecture (DeepSeek sparse attention with the top-512 indexer, 4-wide Manifold-Constrained Hyper-Connections, hash-routed early layers) was merged into mainline llama.cpp on 2026-06-29 (PR #24162, DeepSeek V4). No PR branch or patch is needed to run these files — any llama.cpp built from master at or after that date loads them. Older builds, and bundled runtimes that pin an earlier llama.cpp (LM Studio, Ollama, and similar), fail with unknown architecture 'deepseek4' until they bump it.

git clone https://github.com/ggml-org/llama.cpp/
cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --clean-first \
  --target llama-cli llama-server llama-gguf-split

(Drop -DGGML_CUDA=ON for a CPU-only build; use -DGGML_METAL=ON on macOS.)

Files

Split with llama-gguf-split into 4 parts. Download the whole set and point llama.cpp at the …-00001-of-00004.gguf part; it loads the rest automatically.

File	Size	Tensors
`DeepSeek-V4-Flash-Vision-Uncensored-MXFP4-00001-of-00004.gguf`	~44.5 GB	42
`DeepSeek-V4-Flash-Vision-Uncensored-MXFP4-00002-of-00004.gguf`	~44.5 GB	39
`DeepSeek-V4-Flash-Vision-Uncensored-MXFP4-00003-of-00004.gguf`	~44.5 GB	39
`DeepSeek-V4-Flash-Vision-Uncensored-MXFP4-00004-of-00004.gguf`	~22.9 GB	1,208
Total	~156 GB (145.6 GiB)	1,328

Precision map: routed experts MXFP4 (bit-exact repack of the source FP4); attention wo_b, shared-expert w2 and the other block-FP8 matrices dequantized to BF16; Hyper-Connection mixers F32; hash-routing tables I32.

Usage

Download

hf download orcarouter/DeepSeek-V4-Flash-Vision-Uncensored-GGUF \
  --local-dir ./dsv4-uncensored-gguf

Chat

./llama-cli -m DeepSeek-V4-Flash-Vision-Uncensored-MXFP4-00001-of-00004.gguf \
  -ngl 99 -c 8192 --temp 0.6 --top-p 0.95 -p "Hello!"

OpenAI-compatible server

./llama-server -m DeepSeek-V4-Flash-Vision-Uncensored-MXFP4-00001-of-00004.gguf \
  --host 0.0.0.0 --port 8000 -ngl 99 -c 8192

Prompting. The base tokenizer ships no chat template, so --jinja has nothing to apply and llama.cpp falls back to a generic one. For faithful prompting use the encoding/encode_messages reference from the parent repo and feed the rendered string directly. The model opens a <think> block by default; give max_tokens room (>= 2048) so the answer is not truncated inside it.

Evaluation

The abliteration was measured on the parent checkpoint, not separately re-measured on these GGUFs — see the parent model card for the harmful-refusal, over-refusal and capability tables. This conversion is a deterministic derivation of those weights: the expert matrices are bit-identical and the block-FP8 matrices are dequantized to BF16 without loss, so the behaviour carries over. Text generation was verified working on this build (llama-cli, 8×H100, ~57 tok/s).

Hardware

Runs on CPU, CUDA, Metal or ROCm via llama.cpp. Only ~6 of 256 routed experts are active per token, so decode is far faster than 305B suggests, but the full weights must fit in RAM/VRAM (or be memory-mapped): budget ~146 GiB + KV cache. Multi-GPU (-ngl split) and CPU+GPU offload both work.

License

MIT, inherited from deepseek-ai/DeepSeek-V4-Flash-Vision-Exp. Abliteration and format conversion do not change the underlying license obligations.