Instructions to use Dmitriy-Zemskov/RuGPT-5-small-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dmitriy-Zemskov/RuGPT-5-small-v1 with Transformers:

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

pipe = pipeline("text-generation", model="Dmitriy-Zemskov/RuGPT-5-small-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Dmitriy-Zemskov/RuGPT-5-small-v1")
model = AutoModelForCausalLM.from_pretrained("Dmitriy-Zemskov/RuGPT-5-small-v1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Dmitriy-Zemskov/RuGPT-5-small-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dmitriy-Zemskov/RuGPT-5-small-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dmitriy-Zemskov/RuGPT-5-small-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Dmitriy-Zemskov/RuGPT-5-small-v1

SGLang

How to use Dmitriy-Zemskov/RuGPT-5-small-v1 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 "Dmitriy-Zemskov/RuGPT-5-small-v1" \
    --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": "Dmitriy-Zemskov/RuGPT-5-small-v1",
		"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 "Dmitriy-Zemskov/RuGPT-5-small-v1" \
        --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": "Dmitriy-Zemskov/RuGPT-5-small-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Dmitriy-Zemskov/RuGPT-5-small-v1 with Docker Model Runner:
```
docker model run hf.co/Dmitriy-Zemskov/RuGPT-5-small-v1
```

🌌 RuGPT-5-small (v1.0)

!!!АХТУНГ!!! Данная модель не создана компанией "СБЕР" это кастомный LM.

Подробнее:

⚙️ Детали модели

Архитектура: используется GPT + DeepSeek, но модель своя.
Параметры: 320M
Язык: Русский, только русский.
Лицения: Apache 2.0

🏋️ Детали Тренировки

Датасет: ``
Железо: ОДНА NVIDIA GEFORCE RTX 5060 TI (16GB VRAM)
Эпохи: ...
Шагов: - 115 тысяч
СРЕДНИЙ LOSS: 3.501953
Оптимизатор: lr = 2e-4
Контекст: 2048 токенов

Downloads last month: 20

Safetensors

Model size

0.3B params

Tensor type

BF16

Duplicated from ViorikaAI-org/RuGPT-5-small-v1

Dmitriy-Zemskov
/

RuGPT-5-small-v1

🌌 RuGPT-5-small (v1.0)

Подробнее:

⚙️ Детали модели

🏋️ Детали Тренировки

Datasets used to train Dmitriy-Zemskov/RuGPT-5-small-v1