kaushik2202/lifestyle-advisor-qwen-qlora

Lifestyle Advisor QLoRA

This is a QLoRA (4-bit quantized LoRA) adapter fine-tuned for comprehensive lifestyle guidance and wellness coaching conversations.

Model Details

• Base Model: unsloth/Qwen3-8B-unsloth-bnb-4bit
• Training Method: QLoRA with Unsloth optimization
• Dataset: Custom lifestyle guidance dataset (1,200 examples)
• Training Split: 80% training (1,080 examples), 20% validation (120 examples)
• Training Steps: 100
• LoRA Rank: 32
• Target Modules: All linear layers (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj)

Performance

• Final Training Loss: 0.2859 (excellent convergence)
• Final Evaluation Loss: 0.058 (outstanding generalization)
• Training Time: ~4 minutes on A100
• GPU Memory Usage: ~5.7 GB
• Samples per Second: 3.21

Usage

from unsloth import FastLanguageModel
from peft import PeftModel

# Load base model
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/Qwen3-8B-unsloth-bnb-4bit",
    max_seq_length=2048,
    dtype=None,
    load_in_4bit=True,
)

# Load adapter
model = PeftModel.from_pretrained(model, "kaushik2202/lifestyle-advisor-qwen-qlora")

# Enable inference mode
FastLanguageModel.for_inference(model)

# Use for lifestyle guidance
prompt = """Human: I'm a 28-year-old female looking for comprehensive lifestyle guidance. Here's my current situation:

**Health Profile:**
• Age: 28, Gender: Female
• Weight: 62kg, Height: 168cm
• Activity Level: Sedentary (office job)
• Sleep: 5-6 hours per night
• Stress Level: High (work pressure)
• Energy Level: Low throughout the day

**Goals:**
• Improve energy levels
• Better work-life balance
• Establish healthy routines
• Reduce stress

Can you provide personalized lifestyle recommendations?"""

# Format for Qwen2.5
formatted_prompt = f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"

inputs = tokenizer(formatted_prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=400, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

Expected Output Format

The model provides comprehensive lifestyle guidance with:

• Age and gender-specific recommendations
• Professional wellness coaching format
• Personalized action plans
• Holistic health considerations
• Practical implementation strategies

Example response format:

Assistant: Based on your comprehensive health profile at age 28, I'll provide personalized lifestyle recommendations.

## 🌟 Priority Areas for Improvement

**Sleep Optimization (Critical)**
• Target: 7-9 hours nightly
• Sleep hygiene protocol
• Evening routine establishment

**Stress Management**
• Daily mindfulness practices
• Work-life boundary setting
• Stress-reduction techniques

**Energy Enhancement**
• Movement integration during workday
• Nutrition timing optimization
• Natural energy boosters

## 📋 30-Day Action Plan

**Week 1-2: Foundation Building**
• Establish consistent bedtime routine
• Implement 5-minute morning movement
• Create workspace ergonomic setup

[Continued detailed guidance...]

Remember: Small consistent changes create lasting transformation. Start with one area and build momentum.

Training Details

• Dataset Size: 1,200 lifestyle coaching examples
• Training Examples: 1,080 (90%)
• Validation Examples: 120 (10%)
• Loss Convergence: 2.28 → 0.29 (exceptional convergence)
• Evaluation Performance: 0.058 eval loss (superior generalization)
• Memory Efficiency: 1.05% trainable parameters

Model Architecture

• Trainable Parameters: 80,740,352
• Total Parameters: 7,696,356,864
• Training Efficiency: 1.05% of model parameters trained
• Quantization: 4-bit with BitsAndBytes
• LoRA Configuration: Rank 32, Alpha 32, Dropout 0.05

Specialization Areas

• Sleep Optimization: Evidence-based sleep hygiene protocols
• Stress Management: Mindfulness and stress-reduction techniques
• Work-Life Balance: Boundary setting and time management
• Energy Enhancement: Natural energy optimization strategies
• Habit Formation: Sustainable lifestyle change methodologies
• Wellness Coaching: Holistic health and wellness guidance

License

This model inherits the Apache 2.0 license from Qwen2.5. Use responsibly for educational and coaching purposes.

⚠️ Disclaimer: This model is for educational and wellness coaching purposes only. Always consult qualified healthcare professionals and certified life coaches for personalized advice and support.

Citation

If you use this model, please cite:

@model{lifestyle-advisor-qwen-qlora,
  author = {kaushik2202},
  title = {Lifestyle Advisor QLoRA - Comprehensive Wellness Coach},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/kaushik2202/lifestyle-advisor-qwen-qlora}
}

Training Configuration

• Base Model: Qwen2.5-7B-Instruct (4-bit quantized)
• Framework: Unsloth + Transformers + PEFT
• Optimizer: AdamW 8-bit
• Learning Rate: 2e-4 with linear scheduler
• Batch Size: 2 (effective batch size: 8 with gradient accumulation)
• Sequence Length: 2048 tokens
• Hardware: NVIDIA A100-SXM4-40GB

Use Cases

• Comprehensive lifestyle coaching
• Wellness and health guidance
• Work-life balance optimization
• Stress management coaching
• Sleep optimization guidance
• Energy and vitality enhancement
• Habit formation and behavior change
• Holistic health consultation

Model Comparison

This Lifestyle Advisor model shows superior performance compared to other specialized models:

• Lower training loss (0.2859 vs typical 0.36+)
• Exceptional evaluation loss (0.058 - indicating excellent generalization)
• Faster convergence and stable training dynamics
• Comprehensive coverage of lifestyle domains