---
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:
{}"""



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### Framework versions

- PEFT 0.12.0