Text Generation
Transformers
Safetensors
Russian
t5
text2text-generation
T5
russian
text-generation-inference
Instructions to use r1char9/T5_chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use r1char9/T5_chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="r1char9/T5_chat")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("r1char9/T5_chat") model = AutoModelForSeq2SeqLM.from_pretrained("r1char9/T5_chat") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use r1char9/T5_chat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "r1char9/T5_chat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "r1char9/T5_chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/r1char9/T5_chat
- SGLang
How to use r1char9/T5_chat 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 "r1char9/T5_chat" \ --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": "r1char9/T5_chat", "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 "r1char9/T5_chat" \ --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": "r1char9/T5_chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use r1char9/T5_chat with Docker Model Runner:
docker model run hf.co/r1char9/T5_chat
- Xet hash:
- ad4206d2d807df0186f397005e90c7a4850e8238d28d522ec61f7cea63b2c446
- Size of remote file:
- 1 MB
- SHA256:
- 7a4eb87011448a4564a3144979384da51eee1da95e554feb22ccc85529535dd5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.