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Update README.md
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README.md
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---
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license: other
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---
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---
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datasets:
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- tatsu-lab/alpaca
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language:
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- en
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pipeline_tag: text2text-generation
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library_name: transformers
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license: other
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---
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# Model Details
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- **Model name:** Flan-UL2-Alpaca-LORA
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- **Model type:** - Text2Text Generation
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- **Parent Model:** [google/flan-ul2](https://huggingface.co/google/flan-ul2)
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- **Training dataset:** [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca)
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- **Language:** English
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- **Framework:** PyTorch
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- **Model version:** 1.0
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We take the instruction-tuned Flan models (trained on Academic datasets) and perform style transfer using the Alpaca dataset.
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We fine-tuned the google/flan-ul2 model on the Alpaca datset using [PEFT-LORA](https://huggingface.co/docs/diffusers/main/en/training/lora).
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# License
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- Parent model ([google/flan-ul2](https://huggingface.co/google/flan-ul2)): Apache 2.0
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- Dataset ([Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca)) : cc-by-4.0
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- Text-Davinci-3 (Used to generate Alpaca): [OpenAI License](https://openai.com/policies/terms-of-use)
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# How to Use
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```
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import torch
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from transformers import pipeline
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# Chose the model inference precision
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dtype = torch.float16 # options are torch.bfloat16, torch.float32
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model = pipeline(model="VMware/flan-ul2-alpaca-lora",device_map = 'auto',torch_dtype=dtype )
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prompt = "YOUR PROMPT HERE"
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output = model(prompt_template.format(instruction= prompt), max_length=2048, do_sample=True)
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```
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Using Alpaca prompt template might generate better outputs for certain prompts as the model was trained using the template.
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```
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# Chose the model inference precision
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import torch
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from transformers import pipeline
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dtype = torch.float16 # options are torch.bfloat16, torch.float32
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model = pipeline(model="VMware/flan-ul2-alpaca-lora",device_map = 'auto',torch_dtype=dtype )
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prompt_template = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
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prompt = "YOUR PROMPT HERE"
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output = model(prompt_template.format(instruction= prompt), max_length=2048, do_sample=True)
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```
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# Training Details
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The model was trained on 3xV100 GPUs using PEFT-LORA and Deepspeed
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* Hyperparameters:
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* learning_rate = 3e-4
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* per_device_batch_size = 2
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* gradient_accumulation_steps = 21
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* epochs = 3
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```
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# Limitations and Bias
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The model is based on a large and diverse dataset, but it may still have limitations and biases in certain areas. Some limitations include:
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- Language: The model is designed to work with English text only and may not perform as well in other languages.
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In addition, the model may have some bias in terms of the data it was trained on. The dataset includes questions from a variety of sources, but it may not be representative of all populations or perspectives. As a result, the model may perform better or worse for certain types of questions or on certain types of texts.
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# Contribution
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