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

Developed by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
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]

Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

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

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

Framework versions

PEFT 0.12.0