Update README.md

README.md

@@ -72,61 +72,5 @@ The model was fine-tuned using **parameter-efficient techniques**, including:
 - **Mathematical & Logical Reasoning**: Can assist in **education**, **problem-solving**, and **automated theorem proving**.  
 - **Research & Development**: Useful for **scientific research**, **data analysis**, and **language modeling experiments**.  
 
-### **Deployment**
-The model supports **4-bit and 8-bit quantization**, making it **deployable on resource-constrained devices** while maintaining high performance.
-
----
-
-## **Limitations & Ethical Considerations**
-
-### **Limitations**
-- **Bias & Hallucination**: The model may still **generate biased or hallucinated outputs**, especially in **highly subjective** or **low-resource** domains.  
-- **Computation Requirements**: While optimized, the model **still requires significant GPU resources** for inference at full precision.  
-- **Context Length Constraints**: Long-context understanding is improved, but **performance may degrade** on extremely long prompts.
-
-### **Ethical Considerations**
-- **Use responsibly**: The model should not be used for **misinformation**, **deepfake generation**, or **harmful AI applications**.  
-- **Bias Mitigation**: Efforts have been made to **reduce bias**, but users should **validate outputs** in sensitive applications.  
-
----
-
-## **How to Use the Model**
-
-### **Example Code for Inference**
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "Daemontatox/PathFinderAI4.0"
-
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-
-input_text = "Explain the significance of reinforcement learning in AI."
-inputs = tokenizer(input_text, return_tensors="pt")
-
-output = model.generate(**inputs, max_length=200)
-print(tokenizer.decode(output[0], skip_special_tokens=True))
-
-Using with Unsloth (Optimized LoRA Inference)
-
-from unsloth import FastAutoModelForCausalLM
-
-model = FastAutoModelForCausalLM.from_pretrained(model_name,
-    load_in_4bit=True  # Efficient deployment
-)
-
-
----
-```
-
-## Acknowledgments
-
-Special thanks to:
-
-**Unsloth AI** for their efficient fine-tuning framework.
-
-The open-source AI community for continuous innovation.
-
 
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