Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+base_model:
+- meta-llama/Llama-3.2-3B-Instruct
+---
+# RustBustersHSV-Llama-3.2-3B-Instruct-LoRA
+
+This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) optimized for laser cleaning customer service interactions. It was developed for RustBustersHSV, a laser cleaning and resurfacing company in Huntsville, Alabama.
+
+## Model Details
+
+- **Model type**: Fine-tuned Llama-3.2-3B-Instruct with LoRA
+- **Language(s)**: English
+- **License**: [Llama 3 Community License](https://llama.meta.com/llama3/license/)
+- **Finetuning approach**: Parameter-efficient fine-tuning with Low-Rank Adaptation (LoRA)
+
+## Intended Uses & Limitations
+
+### Intended Uses
+
+This model is designed to:
+
+- Answer customer inquiries about laser cleaning services
+- Provide detailed information about RustBustersHSV's services
+- Help customers understand the laser cleaning process
+- Address common concerns and objections
+- Guide customers toward requesting a free quote
+
+### Limitations
+
+This model:
+
+- Is not designed to provide specific pricing information
+- Should not be used for non-laser cleaning domains without further adaptation
+- Is limited to English language responses
+- May not have expertise in very technical aspects beyond its training data
+- Should be monitored when deployed in a customer-facing environment
+
+## Training Procedure
+
+### Training Data
+
+The model was fine-tuned on 3,000 synthetic QA pairs categorized into:
+
+- General inquiries about laser cleaning
+- Service-specific questions
+- Logistics and location information
+- Process details
+- Concerns and objections
+- Customer experience
+- Technical aspects
+
+All QA pairs were generated using templates and variations designed to mimic real customer service interactions for a laser cleaning business.
+
+### Training Hyperparameters
+
+- **LoRA Configuration**:
+  - r: 8
+  - lora_alpha: 16
+  - lora_dropout: 0.1
+  - bias: "none"
+  - target_modules: ["q_proj", "v_proj"]
+  - task_type: "CAUSAL_LM"
+
+- **Training Hyperparameters**:
+  - Batch size: 1
+  - Learning rate: 2e-5
+  - Optimizer: AdamW
+  - Sequence length: 128
+  - Epochs: 3
+  - Warmup ratio: 0.1
+  - Early stopping patience: 3
+
+### Framework Versions
+
+- Transformers 4.38.0+
+- PyTorch 2.0+
+- PEFT for LoRA fine-tuning
+
+## Uses
+
+This model is intended to be used as a customer service assistant for a laser cleaning business. It can be integrated into:
+
+- Live chat on a company website
+- Customer inquiry response systems
+- Internal knowledge base for employees
+- Training materials for new customer service representatives
+
+## Bias, Risks, and Limitations
+
+The model is specialized for laser cleaning customer service and may:
+
+- Emphasize the benefits of laser cleaning over alternative methods
+- Always attempt to guide customers toward requesting quotes
+- Have limited knowledge outside the laser cleaning domain
+- Not understand or respond accurately to highly technical queries outside its training
+
+## Training Performance
+
+The model was trained using the AdamW optimizer with a linear learning rate scheduler and warmup. Early stopping was used to prevent overfitting.
+
+## Environmental Impact
+
+- The model was fine-tuned using parameter-efficient LoRA techniques to minimize computational resources
+- Training was performed on TPU to maximize efficiency
+
+## How to Use
+
+You can use this model with the Transformers pipeline:
+
+```python
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load base model
+model_name = "meta-llama/Llama-3.2-3B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Load adapter
+adapter_path = "RustBustersHSV/Llama-3.2-3B-Instruct-RustBusters"
+model = PeftModel.from_pretrained(model, adapter_path)
+
+# Format your prompt appropriately
+system_prompt = """You are Lloyd, the first point of contact for customers of Rustbusters. Please be warm and friendly and offer actionable information. Rustbusters is a laser cleaning company that specializes in removing rust, paint, and other contaminants using advanced laser technology. Our services include industrial cleaning, restoration, paint removal, and surface preparation."""
+user_prompt = "What is laser cleaning and how does it work?"
+
+prompt = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n"
+
+# Generate response
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=512, temperature=0.7, top_p=0.9)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## Community and Contributions
+
+This model is maintained by RustBustersHSV. For questions or issues, please contact [contact information].
+
+## Citation
+
+If you use this model in research, please cite:
+
+```
+@misc{rustbustersllama32,
+  author = {RustBustersHSV},
+  title = {RustBustersHSV-Llama-3.2-3B-Instruct-LoRA},
+  year = {2025},
+  publisher = {Hugging Face},
+  journal = {Hugging Face model repository},
+  howpublished = {\url{https://huggingface.co/RustBustersHSV/Llama-3.2-3B-Instruct-RustBusters}}
+}
+```