Update README.md

README.md

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     temperature: 0.7
     top_p: 0.9
     do_sample: true
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+---
+
+
+Lance AI – A Custom-Built AI for the Future
+
+
+
+🚀 Lance AI is a custom-built text generation model, designed from scratch to serve as the foundation for a more advanced AI. Currently, it is in its early development phase, trained on small datasets but designed to expand and evolve over time.
+
+🌟 Key Features
+
+✅ Custom-built architecture (Not based on GPT-2/GPT-3)
+✅ Supports Hugging Face's transformers
+✅ Small-scale model with room for growth
+✅ Lightweight, efficient, and optimized for local and cloud inference
+✅ Planned real-time internet access & vision capabilities
+
+
+---
+
+📥 Installation & Setup
+
+You can load Lance AI using transformers:
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "YourHuggingFaceUsername/LanceAI"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+input_text = "The future of AI is"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=100)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+
+---
+
+🛠 How to Use Lance AI
+
+1️⃣ Direct Text Generation
+
+Lance AI can generate text from simple prompts:
+
+prompt = "In the year 2050, humanity discovered"
+inputs = tokenizer(prompt, return_tensors="pt")
+output = model.generate(**inputs, max_length=50)
+
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+
+2️⃣ Fine-tuning for Custom Applications
+
+You can fine-tune Lance AI for your own dataset using Hugging Face’s Trainer API.
+
+from transformers import Trainer, TrainingArguments
+
+training_args = TrainingArguments(
+    output_dir="./lance_ai_finetuned",
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=8,
+    num_train_epochs=3,
+    save_steps=500
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=your_dataset,
+    eval_dataset=your_eval_dataset
+)
+
+trainer.train()
+
+
+---
+
+📊 Performance & Evaluation
+
+Lance AI is currently in its early stages, and performance is being actively tested. Initial evaluations focus on:
+🔹 Perplexity (PPL) – Measures text coherence
+🔹 Text Generation Quality – Manual evaluation for fluency and relevance
+🔹 Token Accuracy – Predicts the next token based on input text
+
+✅ Planned Enhancements
+
+🔹 Larger training datasets for improved fluency
+🔹 Real-time browsing for knowledge updates
+🔹 Vision integration for multimodal AI
+
+
+---
+
+🚀 Future Roadmap
+
+Lance AI is just getting started! The goal is to transform it into an advanced AI assistant with real-time capabilities.
+📅 Planned Features:
+
+✅ Larger model with better efficiency
+
+🔜 Internet browsing for real-time knowledge updates
+
+🔜 Image and video generation capabilities
+
+🔜 AI-powered PC automation
+
+
+
+---
+
+🏗 Development & Contributions
+
+Lance AI is being developed by NeuraCraft. Contributions, suggestions, and testing feedback are welcome!
+
+📬 Contact & Updates:
+
+Developer: NeuraCraft
+
+Project Status: 🚧 In Development
+
+Follow for updates: Coming soon