Instructions to use Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-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 Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF:Q4_K_M

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 Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF:Q4_K_M

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 Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-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": "Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF:Q4_K_M

SGLang

How to use Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF with Ollama:
```
ollama run hf.co/Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF:Q4_K_M
```

Lemonade

How to use Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Cygnis-Alpha-2-8B-v0.3-i1-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Cygnis Alpha 2 i1

Imatrix Optimized v0.3 Reasoning

Enhanced Importance Matrix (imatrix) quantizations for Cygnis Alpha 2.
Higher intelligence floor for low-bit inference, preserving complex CoT patterns.

Open in Ollama


        ollama run Simonc-44/Cygnis-Alpha-2-8B-v0.3-i1-GGUF

Available Imatrix Quants

Quantization	Size	Intelligence Level	Download
Q6_K (i1)	6.7 GB	REFERENCE GRADE	Link
Q4_K_M (i1)	5.0 GB	RECOMMENDED	Link
IQ4_XS (i1)	4.5 GB	HIGH LOGIC	Link
IQ3_M (i1)	3.9 GB	EFFICIENT	Link
IQ2_M (i1)	3.0 GB	EXPERIMENTAL	Link

Why Imatrix?

Standard quantization treats all weights equally. Imatrix (Importance Matrix) uses a calibration dataset to identify which neurons are vital for the model's logic. By protecting these neurons, we achieve 3-bit or 4-bit quants that often match the performance of standard 6-bit files.

Perplexity vs Bitrate (Efficiency)

Visual representation of why Imatrix (i1) quants outperform standard static quants at lower bitrates.

Ollama Config

FROM ./Cygnis-Alpha-2-7B-v0.3.i1-Q4_K_M.gguf

TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
<|im_thought|>
"""
PARAMETER stop "<|im_end|>"

Quantized with ❤️ by Simonc-44 | Powered by Llama.cpp Imatrix