Instructions to use MBZUAI/LaMini-Flan-T5-783M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MBZUAI/LaMini-Flan-T5-783M with Transformers:

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

pipe = pipeline("text-generation", model="MBZUAI/LaMini-Flan-T5-783M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("MBZUAI/LaMini-Flan-T5-783M")
model = AutoModelForSeq2SeqLM.from_pretrained("MBZUAI/LaMini-Flan-T5-783M")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MBZUAI/LaMini-Flan-T5-783M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MBZUAI/LaMini-Flan-T5-783M"
# 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-Flan-T5-783M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MBZUAI/LaMini-Flan-T5-783M

SGLang

How to use MBZUAI/LaMini-Flan-T5-783M 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-Flan-T5-783M" \
    --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-Flan-T5-783M",
		"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-Flan-T5-783M" \
        --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-Flan-T5-783M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MBZUAI/LaMini-Flan-T5-783M with Docker Model Runner:
```
docker model run hf.co/MBZUAI/LaMini-Flan-T5-783M
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

LaMini-Flan-T5-783M

This model is one of our LaMini-LM model series in paper "LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions". This model is a fine-tuned version of google/flan-t5-large on LaMini-instruction dataset that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our project repository.
You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper.

Base model	LaMini-LM series (#parameters)
T5	LaMini-T5-61M	LaMini-T5-223M	LaMini-T5-738M
Flan-T5	LaMini-Flan-T5-77M✩	LaMini-Flan-T5-248M✩	LaMini-Flan-T5-783M✩
Cerebras-GPT	LaMini-Cerebras-111M	LaMini-Cerebras-256M	LaMini-Cerebras-590M	LaMini-Cerebras-1.3B
GPT-2	LaMini-GPT-124M✩	LaMini-GPT-774M✩	LaMini-GPT-1.5B✩
GPT-Neo	LaMini-Neo-125M	LaMini-Neo-1.3B
GPT-J	coming soon
LLaMA	coming soon

Use

Intended use

We recommend using the model to response to human instructions written in natural language.

We now show you how to load and use our model using HuggingFace pipeline().

# pip install -q transformers
from transformers import pipeline

checkpoint = "{model_name}"

model = pipeline('text2text-generation', model = checkpoint)

input_prompt = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"'
generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text']

print("Response", generated_text)

Training Procedure

We initialize with google/flan-t5-large and fine-tune it on our LaMini-instruction dataset. Its total number of parameters is 783M.

Training Hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0005
train_batch_size: 128
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 512
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Evaluation

We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our paper.

Limitations

More information needed

Citation

@article{lamini-lm,
  author       = {Minghao Wu and
                  Abdul Waheed and
                  Chiyu Zhang and
                  Muhammad Abdul-Mageed and
                  Alham Fikri Aji
                  },
  title        = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions},
  journal      = {CoRR},
  volume       = {abs/2304.14402},
  year         = {2023},
  url          = {https://arxiv.org/abs/2304.14402},
  eprinttype   = {arXiv},
  eprint       = {2304.14402}
}

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Paper for MBZUAI/LaMini-Flan-T5-783M

LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions

Paper • 2304.14402 • Published Apr 27, 2023 • 3