This model was trained for the experiments carried out in the research paper "Conversing with business process-aware Large Language Models: the BPLLM framework".

It comprises a version of the Llama 3.1 8B model fine-tuned (PEFT with quantization int4) to operate within the context of the Food Delivery and E-commerce process models (similar in terms of activities and events) introduced in the article.

Further insights can be found in our paper "Conversing with business process-aware Large Language Models: the BPLLM framework".

Model Trained Using AutoTrain

This model was trained using AutoTrain. For more information, please visit AutoTrain.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "PATH_TO_THIS_REPO"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype='auto'
).eval()

# Prompt content: "hi"
messages = [
    {"role": "user", "content": "hi"}
]

input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

# Model response: "Hello! How can I assist you today?"
print(response)

angeloc1/llama3dot1SimilarProcesses4

Fine-tuned Llama 3.1 8B PEFT int4 for Food Delivery and E-commerce

Model Trained Using AutoTrain

Usage

angeloc1/
llama3dot1SimilarProcesses4