Instructions to use orzattyholdings/Sylor-S1-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use orzattyholdings/Sylor-S1-Flash with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="orzattyholdings/Sylor-S1-Flash",
	filename="sylor_s1_flash.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use orzattyholdings/Sylor-S1-Flash 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 orzattyholdings/Sylor-S1-Flash
# Run inference directly in the terminal:
llama cli -hf orzattyholdings/Sylor-S1-Flash

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf orzattyholdings/Sylor-S1-Flash
# Run inference directly in the terminal:
llama cli -hf orzattyholdings/Sylor-S1-Flash

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 orzattyholdings/Sylor-S1-Flash
# Run inference directly in the terminal:
./llama-cli -hf orzattyholdings/Sylor-S1-Flash

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 orzattyholdings/Sylor-S1-Flash
# Run inference directly in the terminal:
./build/bin/llama-cli -hf orzattyholdings/Sylor-S1-Flash

Use Docker

docker model run hf.co/orzattyholdings/Sylor-S1-Flash

LM Studio
Jan

vLLM

How to use orzattyholdings/Sylor-S1-Flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "orzattyholdings/Sylor-S1-Flash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "orzattyholdings/Sylor-S1-Flash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/orzattyholdings/Sylor-S1-Flash

Ollama
How to use orzattyholdings/Sylor-S1-Flash with Ollama:
```
ollama run hf.co/orzattyholdings/Sylor-S1-Flash
```

Unsloth Studio

How to use orzattyholdings/Sylor-S1-Flash 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 orzattyholdings/Sylor-S1-Flash 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 orzattyholdings/Sylor-S1-Flash to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for orzattyholdings/Sylor-S1-Flash to start chatting

Atomic Chat new
Docker Model Runner
How to use orzattyholdings/Sylor-S1-Flash with Docker Model Runner:
```
docker model run hf.co/orzattyholdings/Sylor-S1-Flash
```

Lemonade

How to use orzattyholdings/Sylor-S1-Flash with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull orzattyholdings/Sylor-S1-Flash

Run and chat with the model

lemonade run user.Sylor-S1-Flash-{{QUANT_TAG}}

List all available models

lemonade list

🚀 Sylor S1 Flash (20B Parameter - GGUF Exclusive Release)

Sylor S1 Flash is an efficiency-focused, 20 Billion parameter language model engineered by Orzatty S/W. This model is specifically optimized for low-latency text generation, local developer automation, and high-performance edge compute.

To protect our intellectual property (IP), the unquantized raw weights remain private. Sylor S1 Flash is distributed exclusively in .gguf format, allowing developers to run a highly capable 20B model locally on consumer hardware (GPUs/CPUs) without sacrificing performance.

🔑 Core Properties

Orzatty Native Identity: Fully aligned with custom system conditioning, custom behavioral filtering, and native personality integration for the Orzatty software ecosystem.
IP Protected Deployment: Distributed solely via quantized .gguf files to maintain backend architecture privacy while maximizing local accessibility.
Edge Compute Ready: Low-overhead processing designed to complement lightweight networking systems and local developer environments.
No Cloud Required: Built for complete privacy and offline operation on standard workstations.

📊 Performance & Benchmarks

Sylor S1 Flash inherits a highly optimized 20B parameter base architecture, delivering robust metrics across standard industry benchmarks for reasoning, coding, and structural understanding:

Benchmark	Score / Value	Description
MMLU (5-shot)	~68.4%	Academic task knowledge & general reasoning
HumanEval (Pass@1)	~52.1%	Python coding proficiency & logic synthesis
ARC-Challenge (25-shot)	~81.2%	Grade-school science hard questions
GSM8K (8-shot)	~76.5%	Multi-step mathematical reasoning

🛠️ Quick Start & Local Usage

Since this model is an exclusive .gguf release, you can run it instantly using llama.cpp or any compatible local inference backend.

Prerequisites

Download your preferred quantization file (e.g., sylor_s1_flash.gguf) from our files section.

Via llama.cpp CLI

# Run interactive chat mode via terminal
./llama-cli -m sylor_s1_flash.gguf -p "You are Sylor S1 Flash, an AI core by Orzatty S/W..." -cnv

⚠️ Intended Use & Limitations

Sylor S1 Flash is tuned to act as an assistant, code companion, and structural data processor.

Local Resource Management: Performance scales directly with your hardware's VRAM allocation. For optimal token-per-second velocity, offload as many layers to your GPU as your memory limits allow.
Alignment: The model contains custom safety and system filters designed to represent the corporate identity of Orzatty S/W.

Downloads last month: 16

GGUF

Model size

21B params

Architecture

gpt-oss

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Collection including orzattyholdings/Sylor-S1-Flash

Sylor S1

Collection

Familia de Modelos S1 • 2 items • Updated 2 days ago