Text Generation
Transformers
PyTorch
English
t5
text2text-generation
Generated from Trainer
instruction fine-tuning
text-generation-inference
Instructions to use MBZUAI/LaMini-T5-61M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MBZUAI/LaMini-T5-61M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MBZUAI/LaMini-T5-61M")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("MBZUAI/LaMini-T5-61M") model = AutoModelForSeq2SeqLM.from_pretrained("MBZUAI/LaMini-T5-61M") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MBZUAI/LaMini-T5-61M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MBZUAI/LaMini-T5-61M" # 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-T5-61M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MBZUAI/LaMini-T5-61M
- SGLang
How to use MBZUAI/LaMini-T5-61M 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-T5-61M" \ --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-T5-61M", "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-T5-61M" \ --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-T5-61M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MBZUAI/LaMini-T5-61M with Docker Model Runner:
docker model run hf.co/MBZUAI/LaMini-T5-61M
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README.md
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This model is one of our LaMini-LM series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)". This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [LaMini dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/).
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You can view other LaMini-LM series as follow. Note that not all models are performing as well. Models with ✩ are those with the best overall performance given their size/architecture. More details can be seen in our paper.
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<table>
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<thead>
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<tr>
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<th>Base model</th>
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<th colspan="4">LaMini series (#parameters)</th>
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</tr>
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</thead>
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<tbody>
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<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a>
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</p>
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We initialize with [t5-small](https://huggingface.co/t5-small) and fine-tune it on our [LaMini dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 61M.
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### Training Hyperparameters
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This model is one of our LaMini-LM series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)". This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/).
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You can view other LaMini-LM series as follow. Note that not all models are performing as well. Models with ✩ are those with the best overall performance given their size/architecture. More details can be seen in our paper.
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<table>
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<thead>
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<tr>
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<th>Base model</th>
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<th colspan="4">LaMini-LM series (#parameters)</th>
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</tr>
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</thead>
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<tbody>
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<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a>
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</p>
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We initialize with [t5-small](https://huggingface.co/t5-small) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 61M.
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### Training Hyperparameters
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