Instructions to use IlyaGusev/saiga2_7b_gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IlyaGusev/saiga2_7b_gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="IlyaGusev/saiga2_7b_gguf",
	filename="model-q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use IlyaGusev/saiga2_7b_gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf IlyaGusev/saiga2_7b_gguf:Q2_K
# Run inference directly in the terminal:
llama-cli -hf IlyaGusev/saiga2_7b_gguf:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf IlyaGusev/saiga2_7b_gguf:Q2_K
# Run inference directly in the terminal:
llama-cli -hf IlyaGusev/saiga2_7b_gguf:Q2_K

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 IlyaGusev/saiga2_7b_gguf:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf IlyaGusev/saiga2_7b_gguf:Q2_K

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 IlyaGusev/saiga2_7b_gguf:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IlyaGusev/saiga2_7b_gguf:Q2_K

Use Docker

docker model run hf.co/IlyaGusev/saiga2_7b_gguf:Q2_K

LM Studio
Jan

vLLM

How to use IlyaGusev/saiga2_7b_gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IlyaGusev/saiga2_7b_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": "IlyaGusev/saiga2_7b_gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IlyaGusev/saiga2_7b_gguf:Q2_K

Ollama
How to use IlyaGusev/saiga2_7b_gguf with Ollama:
```
ollama run hf.co/IlyaGusev/saiga2_7b_gguf:Q2_K
```

Unsloth Studio new

How to use IlyaGusev/saiga2_7b_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 IlyaGusev/saiga2_7b_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 IlyaGusev/saiga2_7b_gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for IlyaGusev/saiga2_7b_gguf to start chatting

Docker Model Runner
How to use IlyaGusev/saiga2_7b_gguf with Docker Model Runner:
```
docker model run hf.co/IlyaGusev/saiga2_7b_gguf:Q2_K
```

Lemonade

How to use IlyaGusev/saiga2_7b_gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IlyaGusev/saiga2_7b_gguf:Q2_K

Run and chat with the model

lemonade run user.saiga2_7b_gguf-Q2_K

List all available models

lemonade list

YAML Metadata Warning:The pipeline tag "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Llama.cpp compatible versions of an original 7B model.

Download one of the versions, for example model-q4_K.gguf.

wget https://huggingface.co/IlyaGusev/saiga2_7b_gguf/resolve/main/model-q4_K.gguf

Download interact_llamacpp.py

wget https://raw.githubusercontent.com/IlyaGusev/rulm/master/self_instruct/src/interact_llamacpp.py

How to run:

pip install llama-cpp-python fire

python3 interact_llamacpp.py model-q4_K.gguf

System requirements:

10GB RAM for q8_0 and less for smaller quantizations

Downloads last month: 274

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

8-bit

Datasets used to train IlyaGusev/saiga2_7b_gguf

Spaces using IlyaGusev/saiga2_7b_gguf 9

Collection including IlyaGusev/saiga2_7b_gguf

Saiga GGUF

Collection

Russian fine-tunes of different base LLMs in the GGUF format compatible with llama.cpp • 8 items • Updated Apr 27, 2025 • 39