Rank machines based on LLaMA 7B v2 benchmark results
View and submit LLM benchmark evaluations
Convert and upload model files for Stable Diffusion
Download a TriplaneGaussian model checkpoint
Merge Lora adapters with a base model
Evaluate code generation with diverse feedback types
Convert a Stable Diffusion XL checkpoint to Diffusers and open a PR
Evaluate RAG systems with visual analytics
View RL Benchmark Reports
Evaluate and submit AI model results for Frugal AI Challenge
Browse and filter machine learning models by category and modality
Submit models for evaluation and view leaderboard
Calculate memory needed to train AI models
Llm Bench is a benchmarking tool designed to evaluate machine performance using the LLaMA 7B v2 model. It provides a standardized way to rank machines based on their ability to run large language models effectively. This tool is particularly useful for comparing hardware capabilities and ensuring consistent performance across different environments.
• LLaMA 7B v2 Integration: Directly leverages the LLaMA 7B v2 model for benchmarking.
• Performance Evaluation: Measures machine performance through inference speed and accuracy.
• Score Calculation: Generates comparable scores to rank machines.
• Cross-Platform Support: Works across different hardware configurations and operating systems.
• Detailed Benchmark Reports: Provides insights into model performance metrics.
llm-bench --model llama7b_v2
1. What is Llm Bench used for?
Llm Bench is used to evaluate and compare machine performance using the LLaMA 7B v2 model, helping users identify the best hardware for running large language models.
2. Does Llm Bench support other models?
Currently, Llm Bench is optimized for the LLaMA 7B v2 model. Support for additional models may be added in future updates.
3. How long does a benchmark run take?
The duration depends on the hardware. On powerful machines, it typically takes a few minutes, while less powerful systems may require more time.