Instructions to use YuvanKumar/SyntheticTabularDataGenration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use YuvanKumar/SyntheticTabularDataGenration with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/phi-3-mini-4k-instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "YuvanKumar/SyntheticTabularDataGenration") - Notebooks
- Google Colab
- Kaggle
| base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit | |
| library_name: peft | |
| # Model Card for Model ID | |
| to genrate tabluar data give instruction like | |
| FastLanguageModel.for_inference(model) # Enable native 2x faster inference | |
| inputs = tokenizer( | |
| [ | |
| alpaca_prompt.format( | |
| "understand the pattern and functional dependencies in the table given in json format in Input and generate similar table with 5 rows.", # instruction | |
| """{("category":"A","item_id":"A1","location":"loc-001","price":100,"available":true),("category":"A","item_id":"A2","location":"loc-002","price":150,"available":false")},{("category":"B","item_id":"B1","location":"loc-001","price":100,"available":true),("category":"B","item_id":"B2","location":"loc-002","price":150,"available":false")},{("category":"C","item_id":"C1","location":"loc-001","price":100,"available":true),("category":"B","item_id":"B3","location":"loc-002","price":150,"available":false")}""", # input | |
| "", # output - leave this blank for generation! | |
| ) | |
| ], return_tensors = "pt").to("cuda") | |
| where | |
| alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides data mentioned in instruction. Write a response and explanation that appropriately completes the request. In the Input section a table is given in form of json format. ( (col1: 1,col2: 2), (col1: 3, col2: 4)) here (col1: 1,col2: 2) is row 1 and (col1: 3, col2: 4)) is row 2 in row 1 col 1 has value 1 and col 2 has value 2. | |
| ### Instruction: | |
| {} | |
| ### Input: | |
| {} | |
| ### Output: | |
| {}""" | |
| ## Model Details | |
| ### Model Description | |
| <!-- Provide a longer summary of what this model is. --> | |
| - **Developed by:** [More Information Needed] | |
| - **Funded by [optional]:** [More Information Needed] | |
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| - **Model type:** [More Information Needed] | |
| - **Language(s) (NLP):** [More Information Needed] | |
| - **License:** [More Information Needed] | |
| - **Finetuned from model [optional]:** [More Information Needed] | |
| ### Model Sources [optional] | |
| <!-- Provide the basic links for the model. --> | |
| - **Repository:** [More Information Needed] | |
| - **Paper [optional]:** [More Information Needed] | |
| - **Demo [optional]:** [More Information Needed] | |
| ## Uses | |
| <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> | |
| ### Direct Use | |
| <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> | |
| [More Information Needed] | |
| ### Downstream Use [optional] | |
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| ### Out-of-Scope Use | |
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| ## Bias, Risks, and Limitations | |
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| ### Recommendations | |
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| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. | |
| ## How to Get Started with the Model | |
| Use the code below to get started with the model. | |
| [More Information Needed] | |
| ## Training Details | |
| ### Training Data | |
| <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> | |
| [More Information Needed] | |
| ### Training Procedure | |
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| #### Preprocessing [optional] | |
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| #### Training Hyperparameters | |
| - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> | |
| #### Speeds, Sizes, Times [optional] | |
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| ## Evaluation | |
| <!-- This section describes the evaluation protocols and provides the results. --> | |
| ### Testing Data, Factors & Metrics | |
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| #### Factors | |
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| ### Results | |
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| #### Summary | |
| ## Model Examination [optional] | |
| <!-- Relevant interpretability work for the model goes here --> | |
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| ## Environmental Impact | |
| <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> | |
| Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). | |
| - **Hardware Type:** [More Information Needed] | |
| - **Hours used:** [More Information Needed] | |
| - **Cloud Provider:** [More Information Needed] | |
| - **Compute Region:** [More Information Needed] | |
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| ## Technical Specifications [optional] | |
| ### Model Architecture and Objective | |
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| ### Compute Infrastructure | |
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| #### Hardware | |
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| #### Software | |
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| ## Citation [optional] | |
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| **BibTeX:** | |
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| **APA:** | |
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| ## Glossary [optional] | |
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| ## More Information [optional] | |
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| ## Model Card Authors [optional] | |
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| ## Model Card Contact | |
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| ### Framework versions | |
| - PEFT 0.12.0 |