Open-source Large Language Models Leaderboard

Open-source Large
Language Models Leaderboard

Large Language Models (LLMs) have revolutionized natural language processing and have shown impressive results in various language tasks.
The Problem: Several LLMs are available in the market. But, relevant information about these models is scattered on the internet, and it is extremely difficult to evaluate these models.
The Solution: We created this leaderboard to help researchers easily identify the best open-source LLM with an intuitive leadership quadrant graph. We evaluate the performance of open-source LLMs to rank them based on their capabilities and market adoption.View Models


LLAMA2, LLAMA1, and T5, based on our scoring methodology, these models scored 89, 87, and 81 points, respectively. The scoring methodology is explained below. The current leader LLAMA2 is a collection of pretrained and fine-tuned large language models (LLMs) that range in scale from 7 billion to 70 billion parameters. The fine-tuned LLMs, or Llama 2-Chat, are specifically optimized for dialogue applications. These models surpass the performance of most open-source chat models on the benchmarks they were tested on. Llama2 70B model outperforms all open-source models.


RankModelSizeArchitectureOrganizationAdoption Rating
Calculated based on the number of forks and stars on the official model repo.
Capability Rating
Calculated based on the number of tasks and downstream tasks of the model.
A weighted average of the adoption and capability score of the model.
#1LLaMA270BTransformer, AutoregressiveMeta AI869289
#2LLaMA65BTransformer, AutoregressiveMeta AI858987
#4Galactica120BTransformerMeta AI475853
#6GLM130BTransformer, AutoregressiveTsinghua University485049
#7OPT175BTransformerMeta AI494145
#10GPT-NeoX20BTransformer, AutoregressiveEleutherAI423338
#12H32.7BState Space ModelStanford University272124
#14Pythia12BDecoder-only autoregressiveEleuther.ai212121

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Ranking Methodology

We only considered prominent and open-source LLMs to create this leaderboard. Note that this leaderboard can only be considered a high-level indicator of overall performance. Depending on the specific use case and business requirements, a detailed analysis is required to choose the right model. The key parameters we used for the scoring are;

  1. Benchmark results
  2. Model forks

Capability Rating(CR) is calculated based on a weighted sum of benchmark results(BR) published in the Model's research paper.

Rank weights;
For performance ranks #1 to #5, rank weight = 3.

For performance ranks #6 to #10, rank weight = 2.

For performance ranks #11 to #20, rank weight = 1.

Adoption Rating (AR) is calculated based on Model forks (MF) and penalizing that value against model performance. To calculate the adoption rating, we calculate the sum of the normalized value of Forks and Capability score.  Then normalize the resulting value to 100. The Model score is simply the average of scores Adoption Rating and Capability Rating. 

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