Instructions to use PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF with LTX.io:

# Install the LTX-2 pipelines
git clone https://github.com/Lightricks/LTX-2.git
cd LTX-2
uv sync --frozen

# Download the weights from this repo, plus the Gemma text encoder
hf download PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF --local-dir models/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF
hf download google/gemma-3-12b-it-qat-q4_0-unquantized --local-dir models/gemma-3-12b

# Fast pipeline (distilled model, no distilled LoRA needed)
uv run python -m ltx_pipelines.distilled \
    --distilled-checkpoint-path models/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF/<distilled-checkpoint>.safetensors \
    --spatial-upsampler-path models/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF/<spatial-upsampler>.safetensors \
    --gemma-root models/gemma-3-12b \
    --prompt "A beautiful sunset over the ocean" \
    --output-path output.mp4
# For image-to-video, add: --image path/to/image.jpg 0 0.8

# HQ pipeline (two-stage, higher quality)
uv run python -m ltx_pipelines.ti2vid_two_stages_hq \
    --checkpoint-path models/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF/<checkpoint>.safetensors \
    --distilled-lora models/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF/<distilled-lora>.safetensors 0.8 \
    --spatial-upsampler-path models/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF/<spatial-upsampler>.safetensors \
    --gemma-root models/gemma-3-12b \
    --prompt "A beautiful sunset over the ocean" \
    --output-path output.mp4
# For image-to-video, add: --image path/to/image.jpg 0 0.8

llama-cpp-python

How to use PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF",
	filename="mmproj-prompt_enhancer_uncensored.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use PeppX/Sulphur-2-Prompt-Enhancer-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 PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama cli -hf PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF:IQ4_NL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama cli -hf PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF:IQ4_NL

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 PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF:IQ4_NL
# Run inference directly in the terminal:
./llama-cli -hf PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF:IQ4_NL

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 PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF:IQ4_NL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF:IQ4_NL

Use Docker

docker model run hf.co/PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF:IQ4_NL

LM Studio
Jan

vLLM

How to use PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PeppX/Sulphur-2-Prompt-Enhancer-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": "PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF:IQ4_NL

Ollama
How to use PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF with Ollama:
```
ollama run hf.co/PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF:IQ4_NL
```

Unsloth Studio

How to use PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-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 PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-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 PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF to start chatting

How to use PeppX/Sulphur-2-Prompt-Enhancer-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 PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF:IQ4_NL

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": "PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF:IQ4_NL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use PeppX/Sulphur-2-Prompt-Enhancer-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 PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF:IQ4_NL

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 PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF:IQ4_NL

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use PeppX/Sulphur-2-Prompt-Enhancer-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 PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF:IQ4_NL

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 "PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF:IQ4_NL" \
  --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 PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF:IQ4_NL
```

Lemonade

How to use PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF:IQ4_NL

Run and chat with the model

lemonade run user.Sulphur-2-Prompt-Enhancer-Uncensored-GGUF-IQ4_NL

List all available models

lemonade list

Sulphur-2 Prompt Enhancer Uncensored - GGUF

Quantized GGUF files for the uncensored prompt enhancer from SulphurAI/Sulphur-2-base. Converts short prompts into detailed video generation prompts for LTX 2.3 / Sulphur-2.

Model Info

Field	Value
Base	Qwen3.5-9B (multimodal)
Architecture	qwen35
Context	262,144 tokens
Blocks	32 layers
Embedding	4096
Uncensoring	Rimuru A+(B-C) merge, B=Hauhau

Files

File	Size	BPW	Description
`prompt_enhancer_uncensored-Q4_K_M.gguf`	5.3 GB	5.02	Standard 4-bit k-quant (balanced)
`prompt_enhancer_uncensored-IQ4_NL.gguf`	5.1 GB	4.85	Importance-aware 4-bit (non-linear)
`mmproj-prompt_enhancer_uncensored.gguf`	880 MB	—	Vision projector (BF16)

Converted from BF16 safetensors → F16 GGUF → quantized with llama.cpp. No requantization — clean F16 source.

Usage

ComfyUI

Drop both the model GGUF and mmproj into ComfyUI/models/text_encoders/ or ComfyUI/models/LLM/GGUF/. Use with ComfyUI-Qwen3.5-Uncensored node.

LM Studio / llama.cpp

llama-server -m prompt_enhancer_uncensored-Q4_K_M.gguf \
  --mmproj mmproj-prompt_enhancer_uncensored.gguf \
  -ngl 99 --host 0.0.0.0

Source

Original model: SulphurAI/Sulphur-2-base
Quantized by PeppX

Downloads last month: 892

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for PeppX/Sulphur-2-Prompt-Enhancer-Uncensored-GGUF

Base model

Lightricks/LTX-2.3

Quantized

SulphurAI/Sulphur-2-base

Quantized

(14)

this model