Text Generation
Transformers
PyTorch
Safetensors
Russian
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use igorktech/rut5-small-chit-chat-intelligent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use igorktech/rut5-small-chit-chat-intelligent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="igorktech/rut5-small-chit-chat-intelligent")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("igorktech/rut5-small-chit-chat-intelligent") model = AutoModelForSeq2SeqLM.from_pretrained("igorktech/rut5-small-chit-chat-intelligent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use igorktech/rut5-small-chit-chat-intelligent with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "igorktech/rut5-small-chit-chat-intelligent" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "igorktech/rut5-small-chit-chat-intelligent", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/igorktech/rut5-small-chit-chat-intelligent
- SGLang
How to use igorktech/rut5-small-chit-chat-intelligent 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 "igorktech/rut5-small-chit-chat-intelligent" \ --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": "igorktech/rut5-small-chit-chat-intelligent", "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 "igorktech/rut5-small-chit-chat-intelligent" \ --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": "igorktech/rut5-small-chit-chat-intelligent", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use igorktech/rut5-small-chit-chat-intelligent with Docker Model Runner:
docker model run hf.co/igorktech/rut5-small-chit-chat-intelligent
Download pytorch_model.bin from igorktech/rut5-small-chit-chat-intelligent: direct link, hf CLI and curl.
- Browser
- Download file 259 MB
-
https://huggingface.co/igorktech/rut5-small-chit-chat-intelligent/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://igorktech/rut5-small-chit-chat-intelligent/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/igorktech/rut5-small-chit-chat-intelligent/resolve/main/pytorch_model.bin
259 MB
- Xet hash:
- 915f4a47078d7e3e3855ef10d12fcd4d6ad1eaa48f81ed39bd3e799fa880396e
- Size of remote file:
- 259 MB
- SHA256:
- b001b8502642ae7a5f8acc97f47ddf83b4f2ee0dc2388acd7371f054f4a5afb0
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