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import torch
from transformers import LlamaForCausalLM, LlamaTokenizer, LoraModel, LoraConfig, PeftModel
import gradio as gr
# Load tokenizer
tokenizer = LlamaTokenizer.from_pretrained("VanguardAI/BhashiniLLaMa3-8B_LoRA_Adapters")
# Load base model
base_model = LlamaForCausalLM.from_pretrained("unsloth/llama-3-8b-Instruct-bnb-4bit")
# Apply LoRA adapters
lora_config = LoraConfig(
r=16,
lora_alpha=16,
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj",],
lora_dropout=0,
bias="none",
task_type="CAUSAL_LM"
)
model = PeftModel.from_pretrained(base_model, "VanguardAI/BhashiniLLaMa3-8B_LoRA_Adapters", config=lora_config)
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, skip_special_tokens=True)
return content[0]
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=5)
],
outputs="text",
title="Formatter Pro",
)
iface.launch(inline=False)
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