shravankotagi

SmartVest

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README

License: apache-2.0

SmartVest Interactive Interface

SmartVest is deployed as a full-stack financial assistant with an integrated safety verification engine:

Voice & Text Input: Users can type or use voice queries (speech-to-text) to ask market questions.
Trust Guard Panel: Features real-time LLM response validation, evaluating outputs against safety checkpoints (e.g., SEBI disclaimers, hallucination checks) to assign a verification score before rendering to the user.
Conversational Search History: Tracks query logs and verification statuses (PASS) in real time.

Sample Generation Outputs

Example 1: Live Market & Entity Query

User: Bajaj Housing Finance ka aaj ka bhav kya hai?
SmartVest: Bajaj Housing Finance (BAJAJHFL) ka aaj ka live NSE price hai Rs 86.52 (change neeche -1.27% aaj). Invest karne se pehle apne broker app pe confirm karein. Yeh sirf informational data hai. SEBI registered advisor se zaroor milein.

Example 2: Portfolio Advice & Risk Query

User: loss ho raha hai portfolio mein kya karein?
SmartVest: Bear market mein ghabrana zaroori nahi. Historically markets hamesha recover karte hain - long term investors ke liye yeh buying opportunity hoti hai. SIP jaari rakho, quality stocks hold karo, panic mein mat becho. Yeh sirf educational info hai. SEBI registered advisor se zaroor milein.

Example 3: Financial Product Comparison

User: FD vs mutual fund konsa better hai long term ke liye?
SmartVest: FD (Fixed Deposit) mein guaranteed returns milte hain aur risk bahut low hota hai, jabki Mutual Funds market-linked hote hain aur unme high returns ka potential hota hai. Long term wealth creation ke liye Mutual Funds behtar hain, par short term safety ke liye FD secure hai. Apne goal ke hisab se allocate karein. Yeh sirf educational info hai. SEBI registered advisor se zaroor milein.

Model Details

Developed by: Shravan Kotagi
Application Name: SmartVest
Model type: Causal LM - Sarvam-1 + LoRA adapter (PEFT)
Languages: Hinglish (hi-Latn), English
License: Apache 2.0
Base model: sarvamai/sarvam-1
Task: Conversational SFT for Indian finance, tax rules (LTCG/STCG), stock tickers, and general market literacy.

What It Does

SmartVest acts as a friendly, concise AI assistant for Indian stock market queries:

Entity Resolution: Handles Indian tickers (e.g., BAJAJHFL, RELIANCE, INFY) and Hinglish financial slang (bhav, mandi, munafa, profit book).
Taxation & Market Literacy: Explains capital gains tax, brokerage charges, and trading losses clearly.
Safety Disclaimers & Trust Guard: Automatically includes SEBI/educational disclaimers and verifies responses for investor safety.

Training Details

Table with columns: Parameter, Value
Parameter	Value
Base Model	`sarvamai/sarvam-1`
Method	QLoRA (4-bit quantization via `bitsandbytes`)
Dataset	`shravankotagi/hinglish-finance-sft-v5-fixed`
Hardware	Kaggle T4 GPU (15.6 GB VRAM)
LoRA Rank / Alpha	`r=16` / ()

Dataset Overview

The model was fine-tuned on shravankotagi/hinglish-finance-sft-v5-fixed, an instruction dataset consisting of 5,521 instruction-response pairs covering:

Financial QA & Literacy: Mutual funds, ETFs, portfolio rebalancing, and risk metrics.
Indian Taxation: Capital gains (LTCG/STCG), ITR forms, and tax loss harvesting.
Market Sentiment & News: Analysis of market trends, corporate actions, and regulatory updates in conversational Hinglish.

Training Loss Curve

Table with columns: Step, Training Loss, Validation Loss
Step	Training Loss	Validation Loss
150	0.7084	0.7169
300	0.6150	0.6206
450	0.5472	0.5872
600	0.5373	0.5665
750	0.4852	0.5523
900	0.5248	0.5426

How to Use

To run inference, load sarvamai/sarvam-1 in 4-bit mode, resize its embedding layer to match the expanded tokenizer, and apply the PEFT adapter:

python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

MODEL_ID = "shravankotagi/SmartVest"

# 1. Configure 4-bit quantization
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
)

# 2. Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "sarvamai/sarvam-1",
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,
)

# 3. Load tokenizer and resize embeddings
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
tokenizer.pad_token = tokenizer.eos_token
base_model.resize_token_embeddings(len(tokenizer))

# 4. Load SmartVest LoRA adapter
model = PeftModel.from_pretrained(
    base_model,
    MODEL_ID,
    ignore_mismatched_sizes=True
)
model.eval()

# 5. Generate
SYSTEM = """You are SmartVest, an AI assistant for Indian retail investors.
You understand Hinglish and answer finance questions clearly and factually."""

user_query = "FD vs mutual fund konsa better hai long term ke liye?"
prompt = f"<s>[INST] {SYSTEM}\n\nUser query: {user_query} [/INST]"

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=120,
        temperature=0.5,
        do_sample=True,
        repetition_penalty=1.1
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response.split("[/INST]")[-1].strip())

Interface Preview

Limitations & Disclaimers

Educational Purpose Only: SmartVest is an experimental AI assistant and does not provide certified financial, legal, or tax advice.
Dynamic Data: Stock prices, tax slabs (LTCG/STCG), and exchange fees change frequently. Always verify live rates via official SEBI/NSE/BSE sources or consult a SEBI-registered financial advisor.
Scope: Optimized specifically for Indian retail market contexts and Hinglish terminology; may underperform on non-Indian or purely technical corporate finance queries.

Contact

Developer: Shravan Kotagi
Hugging Face: @shravankotagi

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Model Details

Model Provider

shravankotagi

Model Tree

Base

sarvamai/sarvam-1

Adapter

this model

Input Modalities

Text

Output Modalities

Text

Supported Functionality

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README

License: apache-2.0

SmartVest Interactive Interface

SmartVest is deployed as a full-stack financial assistant with an integrated safety verification engine:

Voice & Text Input: Users can type or use voice queries (speech-to-text) to ask market questions.
Trust Guard Panel: Features real-time LLM response validation, evaluating outputs against safety checkpoints (e.g., SEBI disclaimers, hallucination checks) to assign a verification score before rendering to the user.
Conversational Search History: Tracks query logs and verification statuses (PASS) in real time.

Sample Generation Outputs

Example 1: Live Market & Entity Query

Example 2: Portfolio Advice & Risk Query

Example 3: Financial Product Comparison

Model Details

Developed by: Shravan Kotagi
Application Name: SmartVest
Model type: Causal LM - Sarvam-1 + LoRA adapter (PEFT)
Languages: Hinglish (hi-Latn), English
License: Apache 2.0
Base model: sarvamai/sarvam-1
Task: Conversational SFT for Indian finance, tax rules (LTCG/STCG), stock tickers, and general market literacy.

What It Does

SmartVest acts as a friendly, concise AI assistant for Indian stock market queries:

Entity Resolution: Handles Indian tickers (e.g., BAJAJHFL, RELIANCE, INFY) and Hinglish financial slang (bhav, mandi, munafa, profit book).
Taxation & Market Literacy: Explains capital gains tax, brokerage charges, and trading losses clearly.
Safety Disclaimers & Trust Guard: Automatically includes SEBI/educational disclaimers and verifies responses for investor safety.

Training Details

Table with columns: Parameter, Value
Parameter	Value
Base Model	`sarvamai/sarvam-1`
Method	QLoRA (4-bit quantization via `bitsandbytes`)
Dataset	`shravankotagi/hinglish-finance-sft-v5-fixed`
Hardware	Kaggle T4 GPU (15.6 GB VRAM)
LoRA Rank / Alpha	`r=16` / ()

Dataset Overview

The model was fine-tuned on shravankotagi/hinglish-finance-sft-v5-fixed, an instruction dataset consisting of 5,521 instruction-response pairs covering:

Financial QA & Literacy: Mutual funds, ETFs, portfolio rebalancing, and risk metrics.
Indian Taxation: Capital gains (LTCG/STCG), ITR forms, and tax loss harvesting.
Market Sentiment & News: Analysis of market trends, corporate actions, and regulatory updates in conversational Hinglish.

Training Loss Curve

Table with columns: Step, Training Loss, Validation Loss
Step	Training Loss	Validation Loss
150	0.7084	0.7169
300	0.6150	0.6206
450	0.5472	0.5872
600	0.5373	0.5665
750	0.4852	0.5523
900	0.5248	0.5426

How to Use

To run inference, load sarvamai/sarvam-1 in 4-bit mode, resize its embedding layer to match the expanded tokenizer, and apply the PEFT adapter:

python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

MODEL_ID = "shravankotagi/SmartVest"

# 1. Configure 4-bit quantization
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
)

# 2. Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "sarvamai/sarvam-1",
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,
)

# 3. Load tokenizer and resize embeddings
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
tokenizer.pad_token = tokenizer.eos_token
base_model.resize_token_embeddings(len(tokenizer))

# 4. Load SmartVest LoRA adapter
model = PeftModel.from_pretrained(
    base_model,
    MODEL_ID,
    ignore_mismatched_sizes=True
)
model.eval()

# 5. Generate
SYSTEM = """You are SmartVest, an AI assistant for Indian retail investors.
You understand Hinglish and answer finance questions clearly and factually."""

user_query = "FD vs mutual fund konsa better hai long term ke liye?"
prompt = f"<s>[INST] {SYSTEM}\n\nUser query: {user_query} [/INST]"

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=120,
        temperature=0.5,
        do_sample=True,
        repetition_penalty=1.1
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response.split("[/INST]")[-1].strip())

Interface Preview

Limitations & Disclaimers

Educational Purpose Only: SmartVest is an experimental AI assistant and does not provide certified financial, legal, or tax advice.
Dynamic Data: Stock prices, tax slabs (LTCG/STCG), and exchange fees change frequently. Always verify live rates via official SEBI/NSE/BSE sources or consult a SEBI-registered financial advisor.
Scope: Optimized specifically for Indian retail market contexts and Hinglish terminology; may underperform on non-Indian or purely technical corporate finance queries.

Contact

Developer: Shravan Kotagi
Hugging Face: @shravankotagi