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open-llm-leaderboard

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Open LLM Leaderboard 1.21k

This query identifies the top 100 high-scoring, officially provided models with fewer than 70 billion parameters, offering a useful overview for comparing performance metrics.

Identifies top-performing AI models with fewer than 20 billion parameters, offering insights into efficiency and precision in smaller models.

Identifies top performing models based on a combined score of IFEval and MMLU-PRO metrics, filtering by official providers and parameter count, offering insights into efficient model performance.

Discovers top high-performing models with less than 70 billion parameters, highlighting their evaluation scores and characteristics, which is valuable for model selection and optimization.

Identifies top-performing models with fewer than 70 billion parameters, combining two evaluation metrics to reveal the best balanced options.

The query identifies top 10 official providers with under 13 billion parameters, ordered by their average metric, revealing valuable insights into efficient models.

This query filters and ranks models within a specific parameter range (6-8 billion) for the LlamaForCausalLM architecture based on their average performance metric.

Retrieves entries related to chat models that are officially provided, offering a filtered view of the dataset.

The query retrieves entries marked as "Official Providers", offering basic filtering but limited analytical value.

The query retrieves a small sample of records from the 'train' dataset where the "Official Providers" flag is true, providing basic filtering with limited analytical value.