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

Libraries

How to use evilfreelancer/ruGPT3XL with Transformers:

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

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

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("evilfreelancer/ruGPT3XL", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use evilfreelancer/ruGPT3XL with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/evilfreelancer/ruGPT3XL

SGLang

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

Docker Model Runner
How to use evilfreelancer/ruGPT3XL with Docker Model Runner:
```
docker model run hf.co/evilfreelancer/ruGPT3XL
```

evilfreelancer commited on Mar 30

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 ## Links
 - [A family of pretrained transformer language models for Russian](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=yPayeJIAAAAJ&citation_for_view=yPayeJIAAAAJ:Se3iqnhoufwC) - paper on Google Scholar
-- [Generating Long Sequences with Sparse Transformers](https://arxiv.org/abs/1904.10509) - sparse attention paper (Child et al., 2019)
 - [ai-forever/rugpt3xl](https://huggingface.co/ai-forever/rugpt3xl) - original model
 - [ai-forever/ru-gpts](https://github.com/ai-forever/ru-gpts) - original training codebase
 - [DeepSpeed Sparse Attention](https://www.deepspeed.ai/tutorials/sparse-attention/) - original sparse attention implementation

 ## Links
 - [A family of pretrained transformer language models for Russian](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=yPayeJIAAAAJ&citation_for_view=yPayeJIAAAAJ:Se3iqnhoufwC) - paper on Google Scholar
 - [ai-forever/rugpt3xl](https://huggingface.co/ai-forever/rugpt3xl) - original model
 - [ai-forever/ru-gpts](https://github.com/ai-forever/ru-gpts) - original training codebase
 - [DeepSpeed Sparse Attention](https://www.deepspeed.ai/tutorials/sparse-attention/) - original sparse attention implementation