Instructions to use evilfreelancer/ruGPT3XL-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use evilfreelancer/ruGPT3XL-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="evilfreelancer/ruGPT3XL-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("evilfreelancer/ruGPT3XL-GGUF", dtype="auto")

llama-cpp-python

How to use evilfreelancer/ruGPT3XL-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="evilfreelancer/ruGPT3XL-GGUF",
	filename="ruGPT3XL-f16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use evilfreelancer/ruGPT3XL-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 evilfreelancer/ruGPT3XL-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf evilfreelancer/ruGPT3XL-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 evilfreelancer/ruGPT3XL-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf evilfreelancer/ruGPT3XL-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 evilfreelancer/ruGPT3XL-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf evilfreelancer/ruGPT3XL-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 evilfreelancer/ruGPT3XL-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf evilfreelancer/ruGPT3XL-GGUF:Q4_K_M

Use Docker

docker model run hf.co/evilfreelancer/ruGPT3XL-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use evilfreelancer/ruGPT3XL-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "evilfreelancer/ruGPT3XL-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": "evilfreelancer/ruGPT3XL-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/evilfreelancer/ruGPT3XL-GGUF:Q4_K_M

SGLang

How to use evilfreelancer/ruGPT3XL-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 "evilfreelancer/ruGPT3XL-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": "evilfreelancer/ruGPT3XL-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 "evilfreelancer/ruGPT3XL-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": "evilfreelancer/ruGPT3XL-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use evilfreelancer/ruGPT3XL-GGUF with Ollama:
```
ollama run hf.co/evilfreelancer/ruGPT3XL-GGUF:Q4_K_M
```

Unsloth Studio

How to use evilfreelancer/ruGPT3XL-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 evilfreelancer/ruGPT3XL-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 evilfreelancer/ruGPT3XL-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for evilfreelancer/ruGPT3XL-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use evilfreelancer/ruGPT3XL-GGUF with Docker Model Runner:
```
docker model run hf.co/evilfreelancer/ruGPT3XL-GGUF:Q4_K_M
```

Lemonade

How to use evilfreelancer/ruGPT3XL-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull evilfreelancer/ruGPT3XL-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.ruGPT3XL-GGUF-Q4_K_M

List all available models

lemonade list

ruGPT-3 XL (HuggingFace format) GGUF

A 1.3B-parameter GPT-3-style language model for Russian, converted from the original ai-forever/rugpt3xl Megatron-LM checkpoint into a native HuggingFace transformers format.

This is a base (pretrained) model, not instruction-tuned. It performs text completion and can be fine-tuned for downstream tasks.

Details in "A family of pretrained transformer language models for Russian" paper.

Model Details

Parameter	Value
Parameters	1.3B
Architecture	GPT-3 (decoder-only transformer)
Hidden size	2048
Layers	24
Attention heads	16
FFN intermediate size	8192
Max sequence length	2048
Vocabulary	50,264 tokens (BPE)
Activation	GELU
Normalization	Pre-LayerNorm
Position encoding	Learned absolute
Precision	float16
Training data	80B tokens of Russian text (4 epochs)
Test perplexity	12.05

Quick Start

Example with Q4_K_M:

./llama.cpp/build/bin/llama-cli \
  -m ./ruGPT3XL-GGUF/ruGPT3XL-q4_k_m.gguf \
  -c 2048 \
  -p "Москва - столица" \
  -n 128 \
  --temp 0.7 \
  --top-p 0.9 \
  --repeat-penalty 1.2

Notes:

Use -c 2048 for the native context length.
Prefer ruGPT3XL-q4_k_m.gguf or ruGPT3XL-q8_0.gguf for CPU inference.
Use ruGPT3XL-f16.gguf mainly for GPU.

Start server:

./llama.cpp/build/bin/llama-server \
  -m ./ruGPT3XL-GGUF/ruGPT3XL-q4_k_m.gguf \
  -c 2048 \
  --host 127.0.0.1 \
  --port 8080

Example request:

curl http://127.0.0.1:8080/completion \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "Вопрос: Какая столица России?\n\nОтвет: ",
    "n_predict": 128,
    "temperature": 0.7,
    "top_p": 0.9,
    "repeat_penalty": 1.2
  }'

Limitations

This is a base model trained on Russian internet text. It may generate biased, factually incorrect, or offensive content.
The model was trained primarily on Russian text. It has limited capability in other languages.
Maximum context length is 2048 tokens. Inputs longer than this will be truncated.
The model is not instruction-tuned and works best for text completion rather than following specific instructions.

Citation

@misc{rugpt3xl-gguf,
  title={ruGPT3XL-GGUF},
  author={Pavel Rykov},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/evilfreelancer/ruGPT3XL-GGUF}
}

Model tree for evilfreelancer/ruGPT3XL-GGUF

Base model

ai-forever/rugpt3xl

Finetuned

evilfreelancer/ruGPT3XL

Quantized

(2)

this model

evilfreelancer
/

ruGPT3XL-GGUF