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import torch
import spaces
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "VanguardAI/BhashiniLLaMa3-8B_LoRA_Adapters",
max_seq_length = 2048,
dtype = None,
load_in_4bit = True,)
FastLanguageModel.for_inference(model)
condition= '''
ALWAYS provide output in a JSON format.
'''
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{}
### Input:
{}
### Response:
{}"""
@spaces.GPU(duration=300)
def chunk_it(inventory_list,user_input_text):
inputs = tokenizer(
[
alpaca_prompt.format(
'''
You will receive text input that you need to analyze to perform the following tasks:
transaction: Record the details of an item transaction.
last n days transactions: Retrieve transaction records for a specified time period.
view risk inventory: View inventory items based on a risk category.
view inventory: View inventory details.
new items: Add new items to the inventory.
report generation: Generate various inventory reports.
delete item: Delete an existing Item.
Required Parameters:
Each task requires specific parameters to execute correctly:
transaction:
ItemName (string)
ItemQt (quantity - integer)
Type (string: "sale" or "purchase" or "return")
ReorderPoint (integer)
last n days transactions:
ItemName (string)
Duration (integer: number of days, if user input is in weeks, months or years then convert to days)
view risk inventory:
RiskType (string: "overstock", "understock", or "Null" for all risk types)
view inventory:
ItemName (string)
new items:
ItemName (string)
SellingPrice (number)
CostPrice (number)
report generation:
ItemName (string)
Duration (integer: number of days, if user input is in weeks, months or years then convert to days )
ReportType (string: "profit", "revenue", "inventory", or "Null" for all reports)
The ItemName must always be matched from the below list of names, EXCEPT for when the Function is "new items".
'''+ inventory_list +
'''
ALWAYS provide output in a JSON format.
''', # instruction
user_input_text, # input
"", # output - leave this blank for generation!
)
], return_tensors = "pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens = 216, use_cache = True)
content= tokenizer.batch_decode(outputs)
return content
iface=gr.Interface(fn=chunk_it,
inputs="text",
outputs="text",
title="Bhashini_LLaMa_LoRA",
)
iface = gr.Interface(
fn=chunk_it,
inputs=[
gr.Textbox(label="user_input_text", lines=3),
gr.Textbox(label="inventory_list", lines=3)
],
outputs="text",
title="SomeModel",
)
iface.launch(inline=False) |