Rank machines based on LLaMA 7B v2 benchmark results
Find recent high-liked Hugging Face models
Convert a Stable Diffusion XL checkpoint to Diffusers and open a PR
Measure over-refusal in LLMs using OR-Bench
Text-To-Speech (TTS) Evaluation using objective metrics.
Search for model performance across languages and benchmarks
Benchmark models using PyTorch and OpenVINO
Explore GenAI model efficiency on ML.ENERGY leaderboard
Calculate survival probability based on passenger details
Compare and rank LLMs using benchmark scores
Quantize a model for faster inference
SolidityBench Leaderboard
Browse and submit LLM evaluations
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.