Vishykm/adaption_indian_finance_dataset

basemodel: mistralai/Mixtral-8x7B-Instruct-v0.1 basemodelrelation: adapter libraryname: peft license: apache-2.0 tags:

• —lora
• —peft
• —sft
• —finance
• —indian-finance
• —mixtral pipeline_tag: text-generation ---

adaptionindianfinance_dataset

A LoRA adapter fine-tuned on top of mistralai/Mixtral-8x7B-Instruct-v0.1 for the Indian personal-finance / financial-inclusion domain.

It spans a wide range of topics: banking services, digital payments and UPI, savings and investment planning, mutual funds, stocks, fixed-income products, insurance, retirement planning, taxation and government benefit schemes, credit cards, personal and business loans, credit scores, fraud and scam awareness, cybersecurity in financial transactions, regulations, RBI and SEBI guidelines, consumer rights, and India's evolving digital infrastructure.

Model Details

• —Base model: mistralai/Mixtral-8x7B-Instruct-v0.1
• —Relation to base: LoRA adapter (PEFT)
• —Training method: Supervised fine-tuning (SFT)
• —Training type: LoRA
• —Data format: chat
• —Domain: Indian finance / financial inclusion
• —DataSet: https://huggingface.co/datasets/Vishykm/adaption-financial-inclusion-dataset-for-india

Training metrics

LoRA Configuration

Training Hyperparameters

How to Get Started

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "mistralai/Mixtral-8x7B-Instruct-v0.1"
adapter = "Vishykm/adaption_indian_finance_dataset"

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)

Job Metadata

• —finetune_job_id: d0d3b083-e1f6-4d87-8fe7-bb1934855615
• —training_experiment_id: cc84875e-5b62-4a18-b2e1-d0ba9f19922c
• —trained_model_name: adaptionindianfinance_dataset

Framework versions

• —PEFT 0.15.1 </content> </invoke>