ranjeethinge
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phi3-sustainable-materials

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 ---
-base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
-library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/Phi-3-mini-4k-instruct-bnb-4bit
-- lora
-- sft
-- transformers
-- trl
-- unsloth
 ---
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-### Framework versions
-- PEFT 0.18.1

 ---
+title: Phi-3 Sustainable Materials Classifier
 tags:
+- text-generation
+- sustainable-materials
+- material-classification
+- phi-3
+- fine-tuned
+license: apache-2.0
+language:
+- en
 ---
+# Phi-3 Sustainable Materials Classifier
+Fine-tuned **Phi-3-mini-4k-instruct** model for classifying sustainable materials from product descriptions.
+## Model Description
+This model is a LoRA fine-tuned version of Microsoft's Phi-3-mini-4k-instruct, specifically trained to identify and classify sustainable materials in product descriptions. It can recognize:
+- **Renewable Materials**: Cork, Bamboo, Wood, Stone Paper, Kraft Paper, Wheat Straw, Bio-Based Plastics, and more
+- **Recycled Materials**: Recycled PET, RPET Polyester, Recycled Paper/Cardboard, Recycled Cotton, and more
+- **Certifications**: GRS Certified, RCS Certified, AWARE™, REPREVE
+## Intended Use
+- Product sustainability analysis
+- E-commerce material classification
+- Environmental compliance checking
+- Sustainable sourcing verification
+## How to Use
+### Using Hugging Face Inference API
+```python
+import requests
+API_URL = "https://api-inference.huggingface.co/models/YOUR_USERNAME/phi3-sustainable-materials"
+headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
+def classify_material(product_name):
+    prompt = f"""Below is product data. Classify the sustainable material.
+### Input:
+Product: {product_name}
+### Response:
+"""
+    response = requests.post(API_URL, headers=headers, json={
+        "inputs": prompt,
+        "parameters": {
+            "max_new_tokens": 256,
+            "temperature": 0.7,
+            "return_full_text": False
+        }
+    })
+    return response.json()
+# Example
+result = classify_material("Recycled Cardboard Notebook")
+print(result)
+```
+### Expected Output Format
+```json
+{
+  "material_name": "Recycled Cardboard",
+  "reason": "Product explicitly mentions recycled cardboard material",
+  "confidence": "100% confident",
+  "confidence_level": "high"
+}
+```
+## Training Data
+Trained on a custom dataset of product descriptions with sustainable material annotations, covering:
+- Product names and descriptions
+- Material types and classifications
+- Confidence levels and reasoning
+## Training Procedure
+- **Base Model**: microsoft/Phi-3-mini-4k-instruct
+- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
+- **LoRA Rank**: 16
+- **Target Modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- **Training Epochs**: 3
+- **Learning Rate**: 2e-4
+- **Max Sequence Length**: 2048
+- **Prompt Format**: Alpaca instruction format
+## Limitations
+- Optimized for English product descriptions
+- Best performance on explicitly mentioned materials
+- May require additional context for generic sustainability claims
+- Confidence levels are model-predicted and should be validated
+## License
+This model is released under the Apache 2.0 License, inheriting from the base Phi-3 model.
+## Citation
+If you use this model, please cite:
+```
+@misc{phi3-sustainable-materials,
+  author = {Your Name},
+  title = {Phi-3 Sustainable Materials Classifier},
+  year = {2026},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/YOUR_USERNAME/phi3-sustainable-materials}
+}
+```
+## Contact
+For questions or feedback, please open an issue on the model repository.