You can deploy this model and start earning money today!

msaad02/
llama2_7b_brockportgpt

Name: llama2_7b_brockportgpt
Availability: InStock

msaad02/formatted-ss-cleaned-brockport-qa

autotrain_compatible

text-generation-inference

endpoints_compatible

Views

No views yet

BrockportGPT LLaMA2 Finetune

This model is part of my thesis to make a question-answering bot for SUNY Brockport. The data used for finetuning is a subset of the question database I generated, and is also available on my huggingface account.

For more information, see my github repo for the project.

Examples

Are SAT scores required?

SAT scores are not required for admission, but test scores are used as part of Brockport's holistic review process to make admissions decisions.

Who can I contact for help with financial aid?

You can contact the financial aid office at SUNY Brockport for assistance with financial aid. They are available to answer any questions and help you navigate the financial aid process.

Prompting

This prompting style is what was used for finetuning:

Below is an inquiry related to SUNY Brockport - from academics, admissions, and faculty support to student life. Prioritize accuracy and brevity.

### Instruction:
{question}

### Response:
{response}

Usage

To run this model, I suggest using the following code to load the model in 4-bit and build a pipeline, which implements the correct prompt formatting

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import textwrap

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

model = AutoModelForCausalLM.from_pretrained(
    "msaad02/llama2_7b_brockportgpt",
    quantization_config=bnb_config,
    device_map={"": 0},
    trust_remote_code=True
)

tokenizer = AutoTokenizer.from_pretrained("msaad02/llama2_7b_brockportgpt")

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline(
    task="text-generation", 
    model=model, 
    tokenizer=tokenizer
)

def qa(text: str, full = False):
    # textwrap.dedent gets rid of indenting at the start of each newline
    text = textwrap.dedent(f"""\
        Below is an inquiry related to SUNY Brockport - from academics, admissions, and faculty support to student life. Prioritize accuracy and brevity.

        ### Instruction:
        {text}

        ### Response:
        """)
    
    response = pipe(text, max_length=100, do_sample=True, top_k=50, top_p=0.95, temperature=1.0)
    response = response[0]['generated_text']
    response = response.split("### Response:\n")[1] if not full else response

    return response

print(qa("How do I apply?", full = True))