library_name: transformers
model_index:
- name: Lance AI
results: []
tags:
- text-generation
- gpt
- pytorch
- causal-lm
- lance-ai
license: apache-2.0
widget:
- text: 'The future of AI is here with Lance AI. Type something:'
inference:
parameters:
max_length: 100
temperature: 0.7
top_p: 0.9
do_sample: true
Lance AI – We are the Future
🚀 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.
🌟 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 = "NeuraCraft/Lance-AI" 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