Text Generation
Transformers
Safetensors
Russian
llama
russian
small-language-model
sovereign-ai
instruct
text-generation-inference
Instructions to use longtimedevs/Ru-Small-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use longtimedevs/Ru-Small-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="longtimedevs/Ru-Small-Instruct")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("longtimedevs/Ru-Small-Instruct") model = AutoModelForCausalLM.from_pretrained("longtimedevs/Ru-Small-Instruct", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use longtimedevs/Ru-Small-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "longtimedevs/Ru-Small-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "longtimedevs/Ru-Small-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/longtimedevs/Ru-Small-Instruct
- SGLang
How to use longtimedevs/Ru-Small-Instruct 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 "longtimedevs/Ru-Small-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "longtimedevs/Ru-Small-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "longtimedevs/Ru-Small-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "longtimedevs/Ru-Small-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use longtimedevs/Ru-Small-Instruct with Docker Model Runner:
docker model run hf.co/longtimedevs/Ru-Small-Instruct
Project & Team Information
This model (Ru-Small-Instruct) and its associated ecosystem are developed and maintained by the independent developer team LongTime Devs.
Development
- Developed by: LongTime Devs
- Official Website: https://long-time.ru
- Project Status: Independent Open-Source Project
Contact & Support
- Telegram: @longtime_support
(Partnerships, technical feedback, and bug reports)
Services & Ecosystem
- Yello Messenger: https://yello.long-time.ru
(Proprietary real-time communication platform built by LongTime Devs)
About Ru-Small-Instruct
- Hugging Face Repository: longtimedevs/Ru-Small-Instruct
- License: MIT License
- Scope: Lightweight, sovereign ~0.1B parameter Russian language model optimized for local inference, micro-deployments, and open-source research.
2026 LongTime Devs. All rights reserved by the individual authors.