Instructions to use IlyaGusev/saiga_nemo_12b_gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IlyaGusev/saiga_nemo_12b_gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="IlyaGusev/saiga_nemo_12b_gguf",
	filename="saiga_nemo_12b.BF16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use IlyaGusev/saiga_nemo_12b_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/saiga_nemo_12b_gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf IlyaGusev/saiga_nemo_12b_gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf IlyaGusev/saiga_nemo_12b_gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf IlyaGusev/saiga_nemo_12b_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 IlyaGusev/saiga_nemo_12b_gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf IlyaGusev/saiga_nemo_12b_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 IlyaGusev/saiga_nemo_12b_gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IlyaGusev/saiga_nemo_12b_gguf:Q4_K_M

Use Docker

docker model run hf.co/IlyaGusev/saiga_nemo_12b_gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use IlyaGusev/saiga_nemo_12b_gguf with Ollama:
```
ollama run hf.co/IlyaGusev/saiga_nemo_12b_gguf:Q4_K_M
```

Unsloth Studio

How to use IlyaGusev/saiga_nemo_12b_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/saiga_nemo_12b_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/saiga_nemo_12b_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/saiga_nemo_12b_gguf to start chatting

Docker Model Runner
How to use IlyaGusev/saiga_nemo_12b_gguf with Docker Model Runner:
```
docker model run hf.co/IlyaGusev/saiga_nemo_12b_gguf:Q4_K_M
```

Lemonade

How to use IlyaGusev/saiga_nemo_12b_gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IlyaGusev/saiga_nemo_12b_gguf:Q4_K_M

Run and chat with the model

lemonade run user.saiga_nemo_12b_gguf-Q4_K_M

List all available models

lemonade list

Llama.cpp compatible versions of an original 12B model.

Download one of the versions, for example saiga_nemo_12b.Q4_K_M.gguf.

wget https://huggingface.co/IlyaGusev/saiga_nemo_12b_gguf/resolve/main/saiga_nemo_12b.Q4_K_M.gguf

Download interact_llama3_llamacpp.py

wget https://raw.githubusercontent.com/IlyaGusev/rulm/master/self_instruct/src/interact_llama3_llamacpp.py

How to run:

pip install llama-cpp-python fire

python3 interact_llama3_llamacpp.py saiga_nemo_12b.Q4_K_M.gguf

System requirements:

15GB RAM for q8_0 and less for smaller quantizations

Downloads last month: 2,634

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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Datasets used to train IlyaGusev/saiga_nemo_12b_gguf

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Collection including IlyaGusev/saiga_nemo_12b_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