Base Model: unsloth/Qwen3-8B-unsloth-bnb-4bit
Training Method: QLoRA with Unsloth optimization
Dataset: Custom diet advice 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: 0.3635
Final Evaluation Loss: 0.076
Training Time: ~4 minutes on A100
GPU Memory Usage: ~5.7 GB
Samples per Second: 3.57

Usage

python

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/diet-advisor-qwen-qlora")

# Enable inference mode
FastLanguageModel.for_inference(model)

# Use for diet advice
prompt = """Human: I'm a 30-year-old female seeking dietary advice. I'm interested in Mediterranean cuisine.

**My Health Profile:**
• Weight: 65kg, Height: 165cm
• Activity Level: Moderate exercise
• Health Goals: Weight maintenance
• Dietary Restrictions: None

Can you suggest a Mediterranean meal plan?"""

# 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=300, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

Expected Output Format

The model provides structured dietary analysis with:

Age and gender-specific recommendations
Professional nutrition formatting
Personalized meal planning
Health goal considerations
Clear dietary guidelines

Example response format:

Assistant: I'll create a personalized Mediterranean meal plan based on your health profile.

## 🍽️ Mediterranean Recommendations for Your Health

**Breakfast:** Greek yogurt with berries and nuts
**Lunch:** Mediterranean salad with grilled chicken
**Dinner:** Baked fish with roasted vegetables

## 📋 Age-Specific Tips (30 years old)

• Focus on nutrient-dense foods for sustained energy
• Include calcium-rich foods for bone health
• Balance convenience with nutrition quality

**Remember:** These recommendations are tailored to your profile. Consult with a registered dietitian for detailed meal planning.

Training Details

Dataset Size: 1,200 diet consultation examples
Training Examples: 1,080 (90%)
Validation Examples: 120 (10%)
Loss Convergence: 3.15 → 0.36 (excellent convergence)
Evaluation Performance: 0.076 eval loss (strong 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

License

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

⚠️ Disclaimer: This model is for educational purposes only. Always consult qualified healthcare professionals and registered dietitians for medical advice and personalized nutrition planning.

Citation

If you use this model, please cite:

bibtex

@model{diet-advisor-qwen-qlora,
  author = {kaushik2202},
  title = {Diet Advisor QLoRA - Personalized Nutrition Assistant},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/kaushik2202/diet-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

Personalized meal planning
Dietary advice consultation
Nutrition education
Health-conscious recipe suggestions
Lifestyle-based food recommendations