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MedAliFarhat/
Tunisia-Banking-Compliance-Gemma

Name: Tunisia-Banking-Compliance-Gemma
Availability: InStock

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Tunisia Banking Compliance – Gemma LoRA Adapter

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A LoRA adapter for the Gemma-2B model fine-tuned on Tunisian banking regulations and compliance tasks. This adapter allows the base google/gemma-2b model to answer legal questions, reference specific articles, and assist with compliance-related inquiries.

Model Details

Base model: google/gemma-2b
Fine-tuning method: LoRA (Low-Rank Adaptation)
Quantization: 4-bit NF4 using BitsAndBytes (bnb_4bit_compute_dtype=torch.bfloat16)
Training dataset: Tunisian banking compliance corpus (questions, answers, article numbers)
Trainer: TRL SFTTrainer

Key Features

Responds accurately to Tunisian banking law questions
References article numbers in answers
Lightweight LoRA adapter (~MB scale) for easy integration with Gemma-2B
Compatible with 4-bit quantized Gemma-2B for low GPU memory usage

Usage

python

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

# 4-bit quantization config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-2b",
    quantization_config=bnb_config,
    device_map="auto"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "path_to_outputs_compliance")
tokenizer = AutoTokenizer.from_pretrained("path_to_outputs_compliance")

# Generate predictions
input_text = "Quelle est la réglementation pour les prêts bancaires aux entreprises en Tunisie ?"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

MedAliFarhat/Tunisia-Banking-Compliance-Gemma

Tunisia Banking Compliance – Gemma LoRA Adapter

Model Details

Key Features

Usage

MedAliFarhat/
Tunisia-Banking-Compliance-Gemma