transformers safetensors llama text-generation autotrain BPLLM PEFTint4 conversational license:other autotrain_compatible endpoints_compatible text-generation-inference region:us
---
tags:
- autotrain
- text-generation
- BPLLM
- PEFTint4
widget:
- text: "I love AutoTrain because "
license: other
---

# Fine-tuned Llama 2 13B PEFT int4 for Food Delivery and Reimbursement

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 2 13B model fine-tuned (PEFT with quantization int4) to operate within the context of the Food Delivery and Reimbursement process models (different 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](https://doi.org/10.21203/rs.3.rs-4125790/v1)".

# Model Trained Using AutoTrain

This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/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)
```