Text Generation
Transformers
PyTorch
English
t5
text2text-generation
Generated from Trainer
instruction fine-tuning
text-generation-inference
Instructions to use MBZUAI/LaMini-T5-223M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MBZUAI/LaMini-T5-223M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MBZUAI/LaMini-T5-223M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("MBZUAI/LaMini-T5-223M") model = AutoModelForSeq2SeqLM.from_pretrained("MBZUAI/LaMini-T5-223M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MBZUAI/LaMini-T5-223M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MBZUAI/LaMini-T5-223M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MBZUAI/LaMini-T5-223M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MBZUAI/LaMini-T5-223M
- SGLang
How to use MBZUAI/LaMini-T5-223M 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 "MBZUAI/LaMini-T5-223M" \ --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": "MBZUAI/LaMini-T5-223M", "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 "MBZUAI/LaMini-T5-223M" \ --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": "MBZUAI/LaMini-T5-223M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MBZUAI/LaMini-T5-223M with Docker Model Runner:
docker model run hf.co/MBZUAI/LaMini-T5-223M
Download training_args.bin from MBZUAI/LaMini-T5-223M: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/MBZUAI/LaMini-T5-223M/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://MBZUAI/LaMini-T5-223M@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/MBZUAI/LaMini-T5-223M/resolve/refs%2Fpr%2F1/training_args.bin
4.73 kB
- Xet hash:
- 80c8bdc9b2aeb48e9f2b400580ba8d4024c7a88d8e3b36949b8a2b22c2535660
- Size of remote file:
- 4.73 kB
- SHA256:
- fd54bd1e69aeb64624114c6f85218dd18a879931ca774c2de9f425340fea8fb9
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