Instructions to use hrezaei/T5Lae-Large-WeightedLoss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hrezaei/T5Lae-Large-WeightedLoss with Transformers:

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

pipe = pipeline("text-generation", model="hrezaei/T5Lae-Large-WeightedLoss")

# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("hrezaei/T5Lae-Large-WeightedLoss", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hrezaei/T5Lae-Large-WeightedLoss with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hrezaei/T5Lae-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/T5Lae-Large-WeightedLoss",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hrezaei/T5Lae-Large-WeightedLoss

SGLang

How to use hrezaei/T5Lae-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/T5Lae-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/T5Lae-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/T5Lae-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/T5Lae-Large-WeightedLoss",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hrezaei/T5Lae-Large-WeightedLoss with Docker Model Runner:
```
docker model run hf.co/hrezaei/T5Lae-Large-WeightedLoss
```

T5Lae-Large-WeightedLoss / emissions.csv

hrezaei

Model save

512cfc3 8 months ago

raw

history blame contribute delete

836 Bytes

	timestamp,project_name,run_id,experiment_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,on_cloud,pue
	2025-10-02T18:30:09,codecarbon,5fb2357d-826c-45b1-9359-5fce9fa00685,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,23975.849363714457,0.14043782923998818,5.8574704532691e-06,180.3366875035157,304.96945793913596,50.0,1.226391726447315,1.8918470779209429,0.3327460645342553,3.4509848689025233,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