shubhamsaini7737/flan-t5-base-gk-finetuned

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Model Details

Fine-tuned Flan-T5 for General Knowledge QA

This is a google/flan-t5-base model that has been fine-tuned using the LoRA method on a small, curated set of general knowledge questions and answers.

Model Description

This model is designed to answer simple, factual questions based on the data it was trained on. It follows an instruction-based format.

Intended Uses & Limitations

• Intended Use: This model is intended for demonstration purposes and as a simple Q&A bot for the specific facts it was trained on.
• Limitations: The model's knowledge is strictly limited to the 20 facts provided during fine-tuning. It will likely fail to answer questions outside of this scope or provide incorrect information. It should not be used for any critical applications.

How to Use

Here is how to load and use the model for inference:

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from peft import PeftModel
import torch

base_model_name = "google/flan-t5-base"
adapter_path = "shubhamsaini7737/flan-t5-base-gk-finetuned" # <-- IMPORTANT: Change this

tokenizer = AutoTokenizer.from_pretrained(base_model_name)
base_model = AutoModelForSeq2SeqLM.from_pretrained(base_model_name)
model = PeftModel.from_pretrained(base_model, adapter_path)
model.eval()

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# Prepare the prompt
prompt_instruction = "Answer"
prompt_input = "Who discovered gravity?"
input_text = f"{prompt_instruction}\n{prompt_input}"
inputs = tokenizer(input_text, return_tensors="pt").to(device)

# Generate
outputs = model.generate(**inputs, max_new_tokens=20)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(answer)

Training Procedure

• Base Model: google/flan-t5-base
• Fine-tuning Method: LoRA
• Key Parameters: r=16, lora_alpha=32, learning_rate=3e-4
• Training Data: Fine-tuned on a list of 20 general knowledge questions.

Model Description

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• Language(s) (NLP): en
• License: apache-2.0
• Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

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Uses

Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

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Evaluation

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Results

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Model Details

This is a google/flan-t5-base model fine-tuned on a custom General Knowledge dataset.

Intended Use

This model is intended for question-answering on general knowledge topics.

Limitations

The model's knowledge is limited to the data it was trained on and may produce incorrect or biased information.