anamikac2708/Gemma-7b-finetuned-investopedia-Lora-Adapters

Uploaded model

• Developed by: anamikac2708
• License: cc-by-nc-4.0
• Finetuned from model : unsloth/gemma-7b-bnb-4bit

This are lora adapeters that are trained on top of Gemma-7B model using 2x faster Unsloth and Huggingface's TRL library using open-sourced finance dataset https://huggingface.co/datasets/FinLang/investopedia-instruction-tuning-dataset developed for finance application by FinLang Team

This project is for research purposes only. Third-party datasets may be subject to additional terms and conditions under their associated licenses.

How to Get Started with the Model

You can infer the adapters directly using Peft/Unsloth library or you can merge the adapter with the base model and can use it. Please find an example below using Unsloth:

import torch
from unsloth import FastLanguageModel
from transformers import AutoTokenizer, pipeline
max_seq_length=2048
model, tokenizer = FastLanguageModel.from_pretrained(
        model_name = "anamikac2708/Gemma-7b-finetuned-investopedia-Lora-Adapters", # YOUR MODEL YOU USED FOR TRAINING
        max_seq_length = max_seq_length,
        dtype = torch.bfloat16,
        load_in_4bit = False #Make it True if you want to load with bitsandbytes 4bit
    )
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
example = [{'content': 'You are a financial expert and you can answer any questions related to finance. You will be given a context and a question. Understand the given context and\n        try to answer. Users will ask you questions in English and you will generate answer based on the provided CONTEXT.\n        CONTEXT:\n        D. in Forced Migration from the University of the Witwatersrand (Wits) in Johannesburg, South Africa; A postgraduate diploma in Folklore & Cultural Studies at Indira Gandhi National Open University (IGNOU) in New Delhi, India; A Masters of International Affairs at Columbia University; A BA from Barnard College at Columbia University\n', 'role': 'system'}, {'content': ' In which universities did the individual obtain their academic qualifications?\n', 'role': 'user'}, {'content': ' University of the Witwatersrand (Wits) in Johannesburg, South Africa; Indira Gandhi National Open University (IGNOU) in New Delhi, India; Columbia University; Barnard College at Columbia University.', 'role': 'assistant'}]
prompt = pipe.tokenizer.apply_chat_template(example[:2], tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.1, top_k=50, top_p=0.1, eos_token_id=pipe.tokenizer.eos_token_id, pad_token_id=pipe.tokenizer.pad_token_id)
print(f"Query:\n{example[1]['content']}")
print(f"Context:\n{example[0]['content']}")
print(f"Original Answer:\n{example[2]['content']}")
print(f"Generated Answer:\n{outputs[0]['generated_text'][len(prompt):].strip()}")

License

Since non-commercial datasets are used for fine-tuning, we release this model as cc-by-nc-4.0.