How to use from
SGLangUse 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 "debugdll/Blind" \
--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": "debugdll/Blind",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
Blind — Russian-language LLMs, trained from scratch
A series of models from 50M to 750M parameters, created entirely from scratch and trained on Russian-language data.
A project for enthusiasts, students, and developers who want to experiment with LLMs without a huge investment.
I will continue training for 2 languages RU and EN and also increase the model if there are 100 downloads
Models
| File | Size | Parameters |
|---|---|---|
blind-0-ultrasmall.pt |
173 MB | ~50M |
blind-0-small.pt |
356 MB | ~85M |
blind-0-medium.pt |
407 MB | ~100M |
blind-0-large.pt |
445 MB | ~110M |
blind-1.pt |
1 GB | ~740M |
blind-1.1.pt |
1 GB | ~750M |
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "debugdll/Blind" \ --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": "debugdll/Blind", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'