VVGONLINE/Vikas-AI
VVGONLINE/Vikas-AI is a 0.8 billion parameter language model developed by VVG ONLINE, optimized for efficient inference on consumer hardware, mobile devices, and browser-based environments. Trained on custom conversational data using LoRA, it is designed for non-commercial text generation tasks. This model is particularly suited for custom chat applications and digital business consulting proofs-of-concept.
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Overview
Vikas-AI is a lightweight, high-performance tiny language model developed by VVG ONLINE. It was trained on custom conversational data using LoRA on an NVIDIA GeForce RTX 2070 (8GB VRAM) setup, making it highly efficient for inference on consumer hardware, mobile devices, and browser-based environments.
Key Capabilities
- Efficient Inference: Optimized for low-resource environments, including consumer GPUs, mobile devices, and web browsers via
transformers.js. - Custom Conversational Data: Trained on specific conversational datasets, making it suitable for custom chat applications.
- Non-Commercial Use: Released under the CC BY-NC 4.0 license, strictly for non-commercial projects and proofs-of-concept.
- Small Footprint: At 0.8 billion parameters, it offers a balance of performance and resource efficiency.
Limitations
As a tiny model, Vikas-AI may struggle with highly complex logic or technical domains compared to larger language models. Its training was conducted with conservative parameters (e.g., max sequence length of 256 tokens) to ensure stability on 8GB VRAM hardware.
Good For
- Developing custom chat interfaces for non-commercial applications.
- Proof-of-concept projects in digital business consulting.
- Experimenting with language models on consumer-grade hardware or in browser environments.