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--- |
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library_name: transformers |
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model_index: |
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- name: Lance AI |
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results: [] |
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tags: |
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- text-generation |
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- gpt |
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- pytorch |
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- causal-lm |
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- lance-ai |
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license: apache-2.0 |
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widget: |
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- text: 'The future of AI is here with Lance AI. Type something:' |
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inference: |
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parameters: |
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max_length: 100 |
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temperature: 0.7 |
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top_p: 0.9 |
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do_sample: true |
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--- |
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Lance AI – We are the Future |
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🚀 Lance AI is a custom-built text generation model, designed 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. |
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🌟 Key Features |
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✅ Custom-built architecture (Not based on GPT-2/GPT-3) |
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✅ Supports Hugging Face's transformers |
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✅ Small-scale model with room for growth |
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✅ Lightweight, efficient, and optimized for local and cloud inference |
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✅ Planned real-time internet access & vision capabilities |
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--- |
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📥 Installation & Setup |
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You can load Lance AI using transformers: |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "NeuraCraft/Lance-AI" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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input_text = "The future of AI is" |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=100) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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--- |
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🛠 How to Use Lance AI |
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1️⃣ Direct Text Generation |
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Lance AI can generate text from simple prompts: |
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prompt = "In the year 2050, humanity discovered" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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output = model.generate(**inputs, max_length=50) |
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print(tokenizer.decode(output[0], skip_special_tokens=True)) |
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2️⃣ Fine-tuning for Custom Applications |
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You can fine-tune Lance AI for your own dataset using Hugging Face’s Trainer API. |
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from transformers import Trainer, TrainingArguments |
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training_args = TrainingArguments( |
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output_dir="./lance_ai_finetuned", |
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per_device_train_batch_size=8, |
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per_device_eval_batch_size=8, |
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num_train_epochs=3, |
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save_steps=500 |
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) |
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trainer = Trainer( |
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model=model, |
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args=training_args, |
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train_dataset=your_dataset, |
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eval_dataset=your_eval_dataset |
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) |
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trainer.train() |
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--- |
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📊 Performance & Evaluation |
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Lance AI is currently in its early stages, and performance is being actively tested. Initial evaluations focus on: |
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🔹 Perplexity (PPL) – Measures text coherence |
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🔹 Text Generation Quality – Manual evaluation for fluency and relevance |
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🔹 Token Accuracy – Predicts the next token based on input text |
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✅ Planned Enhancements |
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🔹 Larger training datasets for improved fluency |
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🔹 Real-time browsing for knowledge updates |
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🔹 Vision integration for multimodal AI |
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--- |
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🚀 Future Roadmap |
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Lance AI is just getting started! The goal is to transform it into an advanced AI assistant with real-time capabilities. |
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📅 Planned Features: |
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🔜 Larger model with better efficiency |
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🔜 Internet browsing for real-time knowledge updates |
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🔜 Image and video generation capabilities |
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🔜 AI-powered PC automation |
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--- |
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🏗 Development & Contributions |
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Lance AI is being developed by NeuraCraft. Contributions, suggestions, and testing feedback are welcome! |
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📬 Contact & Updates: |
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Developer: NeuraCraft |
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Project Status: 🚧 In Development |
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Follow for updates: Coming soon |
