language:
- en
license: cc-by-nc-4.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- gguf
base_model: unsloth/gemma-7b-bnb-4bit
Uploaded model
- Developed by: anamikac2708
- License: cc-by-nc-4.0
- Finetuned from model : unsloth/gemma-7b-bnb-4bit
This Gemma model was trained 2x faster with 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
Model then converted Q5_K_M gguf using llama.cpp https://github.com/ggerganov/llama.cpp/. 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
! CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
from transformers import AutoTokenizer
from llama_cpp import Llama
tokenizer = AutoTokenizer.from_pretrained('google/gemma-7b')
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 = tokenizer.apply_chat_template(example[:2], tokenize=False, add_generation_prompt=True)
llm = Llama.from_pretrained(
repo_id="anamikac2708/Gemma-7b-finetuned-investopedia-q5_k_m_gguf",
filename="*Q5_K_M.gguf",
verbose=False
)
output = llm(
prompt,
max_tokens=256, # Generate up to 256 tokens
stop=["<|im_end|>"],
echo=True, # Whether to echo the prompt
)
print(output['choices'][0]['text'])
Evaluation
Coming soon!
Bias, Risks, and Limitations
This model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking into ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
License
Since non-commercial datasets are used for fine-tuning, we release this model as cc-by-nc-4.0.