---
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