Text Generation
Transformers
Safetensors
Russian
gpt2
gpt-5
russian
conversational
deepseek
text-generation-inference
Instructions to use ViorikaAI-org/RuGPT-5-small-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ViorikaAI-org/RuGPT-5-small-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ViorikaAI-org/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("ViorikaAI-org/RuGPT-5-small-v1") model = AutoModelForCausalLM.from_pretrained("ViorikaAI-org/RuGPT-5-small-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ViorikaAI-org/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 "ViorikaAI-org/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": "ViorikaAI-org/RuGPT-5-small-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ViorikaAI-org/RuGPT-5-small-v1
- SGLang
How to use ViorikaAI-org/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 "ViorikaAI-org/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": "ViorikaAI-org/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 "ViorikaAI-org/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": "ViorikaAI-org/RuGPT-5-small-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ViorikaAI-org/RuGPT-5-small-v1 with Docker Model Runner:
docker model run hf.co/ViorikaAI-org/RuGPT-5-small-v1
Download config.json from ViorikaAI-org/RuGPT-5-small-v1: direct link, hf CLI and curl.
- Browser
- Download file 509 Bytes
-
https://huggingface.co/ViorikaAI-org/RuGPT-5-small-v1/resolve/main/config.json
- Command line
-
hf download hf://ViorikaAI-org/RuGPT-5-small-v1/config.json
-
curl -L -o config.json https://huggingface.co/ViorikaAI-org/RuGPT-5-small-v1/resolve/main/config.json
509 Bytes
| { | |
| "architectures": ["GPT2LMHeadModel"], | |
| "model_type": "gpt2", | |
| "vocab_size": 50000, | |
| "n_positions": 2048, | |
| "n_ctx": 2048, | |
| "n_embd": 1024, | |
| "n_layer": 16, | |
| "n_head": 16, | |
| "intermediate_size": 3072, | |
| "activation_function": "gelu_new", | |
| "resid_pdrop": 0.1, | |
| "embd_pdrop": 0.1, | |
| "attn_pdrop": 0.1, | |
| "layer_norm_epsilon": 1e-5, | |
| "initializer_range": 0.02, | |
| "bos_token_id": 0, | |
| "eos_token_id": 2, | |
| "pad_token_id": 1, | |
| "gradient_checkpointing": false, | |
| "use_cache": true, | |
| "num_experts": 0 | |
| } | |