Text Generation
Transformers
PyTorch
Safetensors
English
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use kargaranamir/T5R-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kargaranamir/T5R-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kargaranamir/T5R-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("kargaranamir/T5R-base") model = AutoModelForSeq2SeqLM.from_pretrained("kargaranamir/T5R-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use kargaranamir/T5R-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kargaranamir/T5R-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kargaranamir/T5R-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/kargaranamir/T5R-base
- SGLang
How to use kargaranamir/T5R-base 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 "kargaranamir/T5R-base" \ --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": "kargaranamir/T5R-base", "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 "kargaranamir/T5R-base" \ --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": "kargaranamir/T5R-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use kargaranamir/T5R-base with Docker Model Runner:
docker model run hf.co/kargaranamir/T5R-base
Commit History
-
Update README.md
4269d7e
-
Update README.md
3d114da -
Update README.md
3e48fc2 -
Update README.md
96c2387 -
Adding `safetensors` variant of this model (#2)
- Adding `safetensors` variant of this model (1dd01fb0357422860568335f89678dd7990493a7) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>5ee1c5f
-
add missing ```
3fec762 -
add example of readme
f10f95e -
Edit Examples
dc68044 -
Update config.json
737e47b -
add text2text-generation
0c96eab -
add base part
d2fa755 -
add base part
f50a554 -
add T5R
Amir Kargaran5ef3c94
-
Update README.md
37bc035
-
add checkpoint of 11702
c42fef0 -
End of training
db8ad7a -
update model card README.md
38ca3df -
End of training
c92f5da -
initial commit
28e83d9