Overview

Sulphur 2 Base GGUF provides developers with a quantized, hardware-efficient implementation of the Sulphur 2 architecture, specifically optimized for text-to-video generation. By utilizing the GGUF format, this model lowers the VRAM barrier for local deployment, allowing for seamless integration into C++ based environments or llama.cpp-compatible pipelines. Unlike full-precision weights, this version enables rapid prototyping and inference on consumer-grade GPUs or Apple Silicon without sacrificing significant temporal consistency. It is particularly suited for developers building automated video content pipelines, AI-driven storytelling tools, or custom video synthesis plugins where low-latency local execution is prioritized over cloud-based API dependency.

Highlights

GGUF quantization for reduced VRAM and local deployment
Optimized for high-efficiency text-to-video synthesis
Apache-2.0 license allows flexible commercial integration
Compatible with llama.cpp and local inference engines
Lower hardware requirements for rapid video prototyping

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("Abiray/Sulphur-2-base-GGUF")
tokenizer = AutoTokenizer.from_pretrained("Abiray/Sulphur-2-base-GGUF")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download Abiray/Sulphur-2-base-GGUF

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download Abiray/Sulphur-2-base-GGUF config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('Abiray/Sulphur-2-base-GGUF')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/Abiray/Sulphur-2-base-GGUF

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Abiray/Sulphur-2-base-GGUF

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('Abiray/Sulphur-2-base-GGUF')
tokenizer = AutoTokenizer.from_pretrained('Abiray/Sulphur-2-base-GGUF')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance

pip install modelscope

CLI Download

Download the full repository

modelscope download --model Abiray/Sulphur-2-base-GGUF

Download a single file to a local folder (e.g. README.md into ./dir)

modelscope download --model Abiray/Sulphur-2-base-GGUF README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('Abiray/Sulphur-2-base-GGUF')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://www.modelscope.cn/Abiray/Sulphur-2-base-GGUF.git

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/Abiray/Sulphur-2-base-GGUF.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'Abiray/Sulphur-2-base-GGUF')

Full Documentation

来源: HuggingFace

---
base_model: SulphurAI/Sulphur-2-base
library_name: gguf
pipeline_tag: text-to-video
tags:

gguf

quantized

---

Sulphur-2-Base (Dev) - GGUF

This repository contains GGUF format model files for SulphurAI's Sulphur-2-base.

Model Details

Original Model: SulphurAI/Sulphur-2-base

Format: GGUF

Architecture: ltxv

Model Size: 21B parameters

Available Quantizations

The following quantization tiers are provided to accommodate different hardware capabilities and VRAM constraints.

| Filename | Quantization Type | Size | Recommended Use |
|:---|:---|:---|:---|
| sulphur_dev_bf16.gguf | BF16 (16-bit) | 42.0 GB | Unquantized baseline. Maximum quality and accuracy. Requires massive VRAM. |
| sulphur_dev-Q8_0.gguf | Q8_0 (8-bit) | 22.8 GB | Extremely high quality, near unquantized performance. |
| sulphur_dev-Q6_K.gguf | Q6_K (6-bit) | 17.8 GB | Very high quality, minimal precision loss. |
| sulphur_dev-Q5_K_M.gguf | Q5_K_M (5-bit) | 16.1 GB | Excellent balance of quality and performance. |
| sulphur_dev-Q5_K_S.gguf | Q5_K_S (5-bit) | 15.0 GB | Slightly smaller 5-bit variant for strict memory limits. |
| sulphur_dev-Q4_K_M.gguf | Q4_K_M (4-bit) | 14.3 GB | Recommended standard. Fast inference with very low quality degradation. |
| sulphur_dev-Q4_K_S.gguf | Q4_K_S (4-bit) | 13.2 GB | Smaller 4-bit variant, slightly lower quality than K_M. |
| sulphur_dev-Q4_0.gguf | Q4_0 (4-bit) | 13.0 GB | Legacy 4-bit quant. Very fast inference but higher perplexity than K-quants. |
| sulphur_dev-Q3_K_M.gguf | Q3_K_M (3-bit) | 11.1 GB | High compression. Best for constrained environments with limited RAM/VRAM. |
| sulphur_dev-Q3_K_S.gguf | Q3_K_S (3-bit) | 10.3 GB | Maximum compression. Lowest footprint but highest quality loss. |

Sulphur 2 base GGUF

Overview

Highlights

Usage

Hugging Face Download

CLI Download

SDK Download

Git Download

PyTorch / Transformers Usage

Model Download

CLI Download

SDK Download

Git Download

Notebook Quickstart

Full Documentation

Sulphur-2-Base (Dev) - GGUF

Model Details

Available Quantizations