ryanfrancis/smart-catalog-bot

🤖 Smart Catalog Bot: E-Commerce Tagging Assistant

📌 Overview

This is a fine-tuned LoRA adapter for TinyLlama/TinyLlama-1.1B-Chat-v1.0. It has been specifically instruction-tuned to act as an automated, highly structured parser for e-commerce product catalogs.

Standard open-source Large Language Models often hallucinate conversational filler (e.g., "Here are your tags..."). This model was trained to abandon conversational outputs entirely and strictly generate API-ready metadata tags based on unstructured product descriptions.

🏗️ Model Details

• Developer: Ryan Francis
• Model Type: Causal Language Model (LoRA Fine-Tune)
• Base Model: TinyLlama 1.1B
• Language: English
• Primary Use Case: Automated e-commerce pipeline categorization

⚙️ Training Methodology

The model was fine-tuned on a synthetically generated dataset of 100 diverse, unstructured e-commerce product descriptions. It was trained to map these descriptions to a rigid output schema: [Category: X] [Target: Y] [Material/Style: Z].

• Training Regime: fp16 mixed precision natively on a T4 GPU
• Technique: Parameter-Efficient Fine-Tuning (PEFT) via LoRA
• Target Modules: q_proj, v_proj
• Optimizer: adamw_torch

📊 Evaluation and Validation

The model was evaluated using a Format Compliance Rate metric. On unseen validation prompts, the fine-tuned adapter achieved strict structural adherence, successfully outputting the exact requested bracketed format with zero conversational hallucinations.

💻 How to Use This Model

You can load this model and run inference using the peft and transformers libraries.

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

# 1. Load the base model
base_model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
model = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto", torch_dtype=torch.float16)

# 2. Load the fine-tuned adapter
repo_id = "ryanfrancis/smart-catalog-bot"
model = PeftModel.from_pretrained(model, repo_id)

# 3. Test the model
prompt = "Product Description: A low-profile velvet sofa in emerald green with tapered wooden legs, perfect for a mid-century modern living room.\nTags:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

# Generate with low temperature for strict formatting
outputs = model.generate(**inputs, max_new_tokens=40, temperature=0.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))