Update README.md

README.md

@@ -4,4 +4,226 @@ language:
 - en
 base_model:
 - google/gemma-2-2b
----
+---
+
+# Model Summary
+
+**Model Name**: `sustainable-fashion-gemma2-2b-202501080`
+**Model Description**: This model is a **specialized** variant of the Gemma-2 2B, fine-tuned to consistently deliver **robust sustainable fashion advice**, practical **capsule wardrobe** guidance, and **timeless styling** recommendations. By integrating **LoRA** techniques, the model efficiently captures **nuanced, real-world** fashion insights without sacrificing inference performance.
+
+### Core Criteria
+
+1. **Conciseness & Directness**
+- Offers clear, actionable fashion tips without unnecessary complexity.
+
+2. **Personalization**
+- Tailors advice to individual budgets, lifestyles, and style preferences.
+
+3. **Integration of Concepts**
+- Connects sustainability principles, budget constraints, and style guidelines into a unified approach.
+
+4. **Tone & Accessibility**
+- Maintains a friendly, approachable voice—ideal for newcomers and seasoned eco-conscious dressers alike.
+
+5. **Strategic Focus**
+- Emphasizes long-term wardrobe value, cost-benefit analyses, and ecological impact in every recommendation.
+
+6. **Practical Reality**
+- Balances high-quality investments with realistic budgeting, mixing accessible pieces with sustainable choices.
+
+### Architecture & Training Highlights
+- **Base Model**: Gemma-2 2B parameters
+- **Fine-Tuning Method**: LoRA-based instruct tuning (`--task=instruct-lora`)
+- **Epochs**: 5
+- **Learning Rate**: 0.0002, using a cosine scheduler
+- **Optimizer**: `paged_adamw_32bit`
+- **Attention Implementation**: `flash_attention_2`, improving speed and memory usage
+- **Precision**: 4-bit quantization for memory efficiency (`--precision_mode=4bit`)
+- **Training Data**: Synthetic Q&A pairs on sustainable and timeless fashion (see [https://www.kaggle.com/datasets/tiyabk/sustainable-fashion/data](#data-overview))
+
+### Primary Use
+The model excels at answering sustainable and timelesse fashion questions on:
+
+**Eco-friendly fabrics**
+- Fabric Recommendations: Users inquire about the best sustainable materials (e.g., organic cotton, linen, hemp, Tencel) for different climates or occasions.
+- Material Care Guidance: Advice on proper care (washing, drying, storing) to extend the life of garments made with eco-friendly fabrics.
+
+**Capsule wardrobe construction**
+- Core Wardrobe Planning: Helps users identify essential clothing items that suit their lifestyle (e.g., a few versatile tops, bottom pieces, layering pieces).
+- Minimalist Shopping Lists: Suggests how many items to include in each category (tops, pants, outerwear, etc.) to maintain both variety and wearability.
+- Seasonal Transitions: Guides on switching out certain pieces for seasonal changes while keeping the majority of items year-round.
+
+**Climate-specific styling**
+- Weather-Appropriate Outfits: Users input local climate details (hot and humid vs. cold and dry), and the model advises on layering, fabric weights, and silhouettes.
+- Travel Packing Assistance: For trips to different climates, the model helps curate a light but versatile wardrobe that can handle temperature and weather variations.
+- Regional Seasonality: Accounts for monsoon seasons, harsh winters, or desert climates when recommending outfit structures, color choices, and fabric blends.
+
+**Timeless color palettes and silhouettes**
+- Color Palette Selection: Helps users identify a cohesive range of neutrals, accent colors, and core hues that - suit their skin tone and personal style.
+- Long-Lasting Trends: Suggests classic cuts (e.g., A-line skirts, tailored trousers, button-up shirts) that transcend seasonal fads.
+- Personalization: Balances timelessness with individual flair (e.g., a signature color or pattern) without sacrificing longevity.
+
+### Evaluation
+The model was evaluated on an internal validation set using built-in metrics (e.g., loss). The model has demonstrated robust understanding and consistent instruction-following behavior for fashion-related queries.
+
+---
+
+## Usage
+
+Below is a minimal code snippet showing how to load and use the model for inference. This example assumes the merged model is available in a storage bucket or local path.
+
+```bash
+!pip install transformers accelerate huggingface_hub
+```
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from huggingface_hub import notebook_login
+
+# If your model is private, uncomment:
+# notebook_login()
+
+# 1) Set up the device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# 2) Load tokenizer & model from Hugging Face
+model_path = "YourUsername/my-awesome-finetuned-model"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16)
+model.to(device)
+
+# 3) Define your generation functions
+def ask_fashion_model_short_response(question):
+inputs = tokenizer(question, return_tensors="pt").to(device)
+with torch.no_grad():
+outputs = model.generate(
+**inputs,
+max_new_tokens=200,
+do_sample=True,
+temperature=0.7,
+top_k=50,
+top_p=0.9,
+repetition_penalty=1.2,
+num_beams=1
+)
+return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+def ask_fashion_model_long_response(question):
+inputs = tokenizer(question, return_tensors="pt").to(device)
+with torch.no_grad():
+outputs = model.generate(
+**inputs,
+max_new_tokens=512,
+num_beams=2,
+length_penalty=1.8,
+temperature=1,
+top_k=50,
+top_p=1,
+do_sample=False
+)
+return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# 4) Example usage
+query = "What are some timeless ways to incorporate color into my wardrobe if I usually stick to neutral shades?"
+response = ask_fashion_model_long_response(query)
+print(response)```
+
+### Input / Output Shape
+- **Input**: A user prompt or query in natural language (e.g., “How do I choose the right colors for my skin tone?”).
+- **Output**: A single, text-based answer or instruction.
+
+### Known and Preventable Failures
+- **Ambiguous Requests**: Vague queries may yield broad responses. Encourage users to provide context.
+- **Non-Fashion Queries**: The model is specialized for sustainable fashion tasks and might produce less accurate or irrelevant answers for unrelated domains.
+
+---
+
+## System
+
+This model can be used as a **standalone** text-generation system or integrated into broader chat/assistant pipelines:
+- **Input Requirements**: A plain text prompt describing a fashion or styling question.
+- **Downstream Dependencies**: Any application expecting textual recommendations (e.g., e-commerce chatbots, personal stylist apps, content generation tools).
+
+The model’s text output could feed into:
+- **E-commerce**: Product recommendations for sustainability suggestions.
+- **Editorial/Content**: Generating blog articles or social media posts on sustainability fashion topics.
+
+---
+
+## Implementation Requirements
+
+- **Training Hardware**:
+- **Machine Type**: `a2-ultragpu-8g` with 8 NVIDIA A100 80GB GPUs
+- **Disk**: 500 GB SSD (`pd-ssd`)
+- **Replica Count**: 1 (single worker)
+
+- **Software**:
+- Container image: `us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/pytorch-peft-train:stable_20240909`
+- Key packages: PyTorch, Transformers, Deepspeed (Zero 2 config), LoRA libraries
+
+- **Compute Requirements**:
+Training with 8 GPUs over 5 epochs on a dataset of fashion Q&A pairs. Time to convergence depends on final dataset size and batch configuration (`per_device_train_batch_size=1` with gradient accumulation steps).
+
+- **Performance & Energy Consumption**:
+The usage of flash attention and 4-bit precision helps reduce memory usage and training costs. No exact energy consumption metrics are published, but overhead is significantly reduced compared to full FP16 or FP32 training.
+
+---
+
+# Model Characteristics
+
+## Model Initialization
+
+The model was **fine-tuned** from a pre-trained Gemma-2 2B parameter LLM. It was **not** trained from scratch.
+
+## Model Stats
+
+- **Parameter Count**: ~2B base parameters, plus LoRA adapters
+- **Model Size (disk)**: ~5GB
+- **Layers**: The base Gemma-2 architecture with multi-headed self-attention and transformer blocks.
+- **Latency**: Inference latency depends on GPU/CPU hardware. On a single GPU, the flash attention significantly speeds up token generation compared to naive implementations.
+
+## Other Details
+
+- **Pruning**: Not applied.
+- **Quantization**: 4-bit quantization used for training to reduce memory footprint.
+- **Differential Privacy**: No specialized techniques implemented; the dataset is synthetic with no direct PII.
+
+---
+
+# Data Overview
+
+## Training Data
+
+**Source**: A synthetic collection of 38K lines Q&A pairs about sustainable fashion, generated via advanced prompt engineering to cover:
+- **Sustainable practices** (e.g., recycled materials, secondhand shopping)
+- **Wardrobe fundamentals** (e.g., neutral color palettes, timeless silhouettes)
+- **Climate-specific styling**
+- **Budget constraints**
+...
+
+**Pre-processing**: Minimal text cleaning (e.g., removing extraneous symbols), focusing on clarity and consistency.
+
+## Demographic Groups
+
+The data does not explicitly classify demographics. Rather, it addresses general styling contexts (body changes, climate differences, budget considerations) that can be relevant across diverse populations.
+
+## Evaluation Data
+
+- **Train / Test / Dev Splits**: Partitioned from the same synthetic source:
+- **Train**: Main dataset (`train` split)
+- **Evaluation**: Sustainable Fashion Eval
+- **Differences**: All synthetic, but evaluation focuses on multi-turn instructions, ensuring coverage of real-world complexities.
+
+---
+
+# Evaluation Results
+
+## Summary
+
+The model’s performance was measured via:
+- **Loss** on the held-out evaluation set every few steps (`--eval_steps=5`)
+- **Qualitative** checks for correctness, clarity, and coherence
+
+Results indicated:
+- **Low perplexity** on domain-specific