Instructions to use hrezaei/T5Laa-Large-WeightedLoss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hrezaei/T5Laa-Large-WeightedLoss with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="hrezaei/T5Laa-Large-WeightedLoss")

# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("hrezaei/T5Laa-Large-WeightedLoss", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hrezaei/T5Laa-Large-WeightedLoss with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hrezaei/T5Laa-Large-WeightedLoss"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hrezaei/T5Laa-Large-WeightedLoss",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hrezaei/T5Laa-Large-WeightedLoss

SGLang

How to use hrezaei/T5Laa-Large-WeightedLoss 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 "hrezaei/T5Laa-Large-WeightedLoss" \
    --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": "hrezaei/T5Laa-Large-WeightedLoss",
		"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 "hrezaei/T5Laa-Large-WeightedLoss" \
        --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": "hrezaei/T5Laa-Large-WeightedLoss",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hrezaei/T5Laa-Large-WeightedLoss with Docker Model Runner:
```
docker model run hf.co/hrezaei/T5Laa-Large-WeightedLoss
```

T5Laa-Large-WeightedLoss / emissions.csv

hrezaei

Model save

21bb5a7 12 months ago

Raw History Blame Contribute Delete

839 Bytes

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	2025-10-05T10:13:05,codecarbon,5b6a147f-b32d-48ae-88fa-9082a50addc4,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,78452.60989674926,0.46708179118496806,5.953680722664167e-06,179.07819168933295,305.84559295361987,50.0,3.9265552426702945,6.4621924389165315,1.0888734312026163,11.477621112789484,Sweden,SWE,dalarna county,,,Linux-4.18.0-553.56.1.el8_10.x86_64-x86_64-with-glibc2.28,3.10.18,3.0.4,32,AMD EPYC 7413 24-Core Processor,1,1 x NVIDIA H100 PCIe,15.6326,60.6043,171.68,machine,N,1.0