Instructions to use SulphurAI/Sulphur-2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SulphurAI/Sulphur-2-base with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SulphurAI/Sulphur-2-base", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - llama-cpp-python
How to use SulphurAI/Sulphur-2-base with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="SulphurAI/Sulphur-2-base", filename="prompt_enhancer/mmproj-BF16.gguf", )
llm.create_chat_completion( messages = "\"A young man walking on the street\"" )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use SulphurAI/Sulphur-2-base with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf SulphurAI/Sulphur-2-base:BF16 # Run inference directly in the terminal: llama-cli -hf SulphurAI/Sulphur-2-base:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf SulphurAI/Sulphur-2-base:BF16 # Run inference directly in the terminal: llama-cli -hf SulphurAI/Sulphur-2-base:BF16
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 SulphurAI/Sulphur-2-base:BF16 # Run inference directly in the terminal: ./llama-cli -hf SulphurAI/Sulphur-2-base:BF16
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 SulphurAI/Sulphur-2-base:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf SulphurAI/Sulphur-2-base:BF16
Use Docker
docker model run hf.co/SulphurAI/Sulphur-2-base:BF16
- LM Studio
- Jan
- Ollama
How to use SulphurAI/Sulphur-2-base with Ollama:
ollama run hf.co/SulphurAI/Sulphur-2-base:BF16
- Unsloth Studio new
How to use SulphurAI/Sulphur-2-base 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 SulphurAI/Sulphur-2-base 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 SulphurAI/Sulphur-2-base to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for SulphurAI/Sulphur-2-base to start chatting
- Pi new
How to use SulphurAI/Sulphur-2-base with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf SulphurAI/Sulphur-2-base:BF16
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": "SulphurAI/Sulphur-2-base:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use SulphurAI/Sulphur-2-base with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf SulphurAI/Sulphur-2-base:BF16
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 SulphurAI/Sulphur-2-base:BF16
Run Hermes
hermes
- Docker Model Runner
How to use SulphurAI/Sulphur-2-base with Docker Model Runner:
docker model run hf.co/SulphurAI/Sulphur-2-base:BF16
- Lemonade
How to use SulphurAI/Sulphur-2-base with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull SulphurAI/Sulphur-2-base:BF16
Run and chat with the model
lemonade run user.Sulphur-2-base-BF16
List all available models
lemonade list
Pleaase upload nvfp4, fp8, gguf versions
Pleaase upload nvfp4, fp8, gguf versions
Fp8 version is critical. It's important to get the transformer model itself to fit into vram.
I just did 2 t2v videos with the sulphur lora with the distilled gguf (distill 1.1) and videos are coming out pretty dang impressive. Speed is about the same even though lora is 10gb.
fp8, gguf 😥
Can this be loaded in FP8? Or no, because it's a checkpoint? I have an RTX 5090 and cannot fit this into VRAM
Running the full model on my poor 5060ti 16gb vram and 32gb ram. It works just takes bit longer than with fp8 haha
I'll have them converted to fp8
ohh will you upload to your github?
On Huggingface - takes some time but I'll get it done today
ETA is in a few hours and I'll post the link here
ohh will you upload to your github?
Huggingface
I have done GGUF conversions but they are not Comfy compatible as it seems Sulphur have changed the model dimensions. I could patch Comfy and submit a PR, but do we know if that's intentional/already planned?
Just stick with the Q6_K/Q8_K 1.1 GGUF's and use the standalone 768 LoRA. You'll get the same results with less VRAM usage.