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

Libraries

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

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

pipe = pipeline("text-generation", model="hrezaei/T5Laa-Large-WeightedLoss-InstructMixed")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hrezaei/T5Laa-Large-WeightedLoss-InstructMixed 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-InstructMixed"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hrezaei/T5Laa-Large-WeightedLoss-InstructMixed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use hrezaei/T5Laa-Large-WeightedLoss-InstructMixed 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-InstructMixed" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hrezaei/T5Laa-Large-WeightedLoss-InstructMixed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-InstructMixed" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hrezaei/T5Laa-Large-WeightedLoss-InstructMixed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

hrezaei commited on Oct 29, 2025

Commit

0a2e66f

verified ·

1 Parent(s): e168488

Training in progress, epoch 0

Browse files

Files changed (9) hide show

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README.md CHANGED Viewed

@@ -27,7 +27,7 @@ print(output["generated_text"])
 ## Training procedure
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/uoy/InstructionTuning/runs/qk5wu3l8)
 This model was trained with SFT.

 ## Training procedure
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/uoy/InstructionTuning/runs/1f7n3ta3)
 This model was trained with SFT.

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