Track, rank and evaluate open LLMs and chatbots
Calculate GPU requirements for running LLMs
Find recent high-liked Hugging Face models
Display and filter leaderboard models
Determine GPU requirements for large language models
View and compare language model evaluations
Convert PaddleOCR models to ONNX format
Benchmark models using PyTorch and OpenVINO
Predict customer churn based on input details
Merge Lora adapters with a base model
Display LLM benchmark leaderboard and info
Measure BERT model performance using WASM and WebGPU
Load AI models and prepare your space
The Low-bit Quantized Open LLM Leaderboard is a platform designed to track, rank, and evaluate open large language models (LLMs) and chatbots with a focus on low-bit quantization. It provides insights into how these models perform when compressed to lower precision (e.g., 4-bit or 8-bit), enabling efficient deployment on edge devices. The leaderboard helps researchers and developers explore and compare the performance of various LLMs in resource-constrained environments.
• Model Benchmarking: Comprehensive evaluation of open-source LLMs using low-bit quantization.
• Quantization Tools: Built-in support for applying quantization techniques to reduce model size.
• Accuracy Metrics: Tracks performance across tasks like text generation, question answering, and conversational tasks.
• Efficiency Insights: Displays memory usage and inference speed for quantized models.
• Real-time Updates: Regularly updated leaderboard with the latest models and optimizations.
• Community Engagement: Open for contributions, fostering collaboration in the AI research community.
• Transparency: Detailed documentation of evaluation methodologies and metrics.
1. What models are included in the leaderboard?
The leaderboard includes a variety of open-source LLMs, focusing on models optimized for low-bit quantization. Popular models like BERT, GPT, and smaller variants are regularly featured.
2. How is the performance of quantized models measured?
Performance is measured using standard benchmarks like text generation quality, question answering accuracy, and inference speed. Additional metrics include memory usage and computational efficiency.
3. Can I use the leaderboard for commercial purposes?
Yes, the leaderboard is designed to support both research and practical applications. It provides valuable insights for deploying quantized models in real-world scenarios, such as edge devices.
4. How often is the leaderboard updated?
The leaderboard is updated regularly to include new models, improvements in quantization techniques, and feedback from the community.
5. Can I contribute to the leaderboard?
Absolutely! The platform encourages contributions, such as submitting new models, improving quantization techniques, or providing feedback on existing entries.