Digital Event Horizon
The Open LLM Leaderboard provides valuable insights into the carbon footprint of large language models, highlighting the need for sustainable development and responsible model design.
Community fine-tunes tend to be more CO2-efficient than official models during inference. Community fine-tunes often undergo benchmark-specific adaptations, leading to shorter outputs and reduced energy consumption. Official models from high-quality model creators have higher CO2 emissions due to their larger parameter sizes. Instruction-tuned models can exhibit unexpected behavior, such as verbosity and overfitting specific prompt formats. Community fine-tunes may prioritize concise responses, potentially reducing both computational load and emissions.
The recent study on the Open LLM Leaderboard has highlighted the significance of carbon emissions from large language models during inference. The study analyzed over 3,000 models and found that community fine-tunes tend to be more CO2-efficient than official models.
One possible explanation for this trend is that community fine-tunes often undergo benchmark-specific adaptations, leading to shorter outputs and reduced energy consumption. For instance, the community fine-tune of Qwen2-72B achieves an average leaderboard score above 40 across multiple evaluation benchmarks.
In contrast, official models from high-quality model creators, such as research groups or community consortiums, tend to have higher CO2 emissions due to their larger parameter sizes. However, the relationship between model size and CO2 emissions is not always proportional, leading to diminishing returns.
The study also found that instruction-tuned models can exhibit unexpected behavior, such as verbosity and overfitting specific prompt formats. For example, the Qwen-72B-Instruct model produces very long responses, which may be attributed to its inability to follow instructions and continue the prompt.
Furthermore, the study suggests that community fine-tunes may prioritize concise responses, potentially reducing both computational load and emissions. This trend is observed in the comparison between the base model Qwen2-72B and its community fine-tune calme-2.1-qwen2-72b, where the latter produces a shorter response.
In conclusion, the study highlights the importance of considering CO2 emissions from large language models during inference. By understanding the trends and patterns in these emissions, we can work towards creating more energy-efficient models that balance performance with environmental responsibility.
The Open LLM Leaderboard provides valuable insights into the carbon footprint of large language models, highlighting the need for sustainable development and responsible model design.
Research Items:
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Affected Software:
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Key Points:
Community fine-tunes tend to be more CO2-efficient than official models during inference.
Community fine-tunes often undergo benchmark-specific adaptations, leading to shorter outputs and reduced energy consumption.
Official models from high-quality model creators have higher CO2 emissions due to their larger parameter sizes.
Instruction-tuned models can exhibit unexpected behavior, such as verbosity and overfitting specific prompt formats.
Community fine-tunes may prioritize concise responses, potentially reducing both computational load and emissions.
https://huggingface.co/blog/leaderboard-emissions-analysis
Carbon Emissions from Large Language Models: A Deep Dive
The recent study on the Open LLM Leaderboard has highlighted the significance of carbon emissions from large language models during inference. The study analyzed over 3,000 models and found that community fine-tunes tend to be more CO2-efficient than official models.
One possible explanation for this trend is that community fine-tunes often undergo benchmark-specific adaptations, leading to shorter outputs and reduced energy consumption. For instance, the community fine-tune of Qwen2-72B achieves an average leaderboard score above 40 across multiple evaluation benchmarks.
In contrast, official models from high-quality model creators, such as research groups or community consortiums, tend to have higher CO2 emissions due to their larger parameter sizes. However, the relationship between model size and CO2 emissions is not always proportional, leading to diminishing returns.
The study also found that instruction-tuned models can exhibit unexpected behavior, such as verbosity and overfitting specific prompt formats. For example, the Qwen-72B-Instruct model produces very long responses, which may be attributed to its inability to follow instructions and continue the prompt.
Furthermore, the study suggests that community fine-tunes may prioritize concise responses, potentially reducing both computational load and emissions. This trend is observed in the comparison between the base model Qwen2-72B and its community fine-tune calme-2.1-qwen2-72b, where the latter produces a shorter response.
In conclusion, the study highlights the importance of considering CO2 emissions from large language models during inference. By understanding the trends and patterns in these emissions, we can work towards creating more energy-efficient models that balance performance with environmental responsibility.
started: 2025-01-09 13:32:38.784360
ending: 2025-01-09 13:50:31.120515
Related Information:
https://huggingface.co/blog/leaderboard-emissions-analysis
Published: Thu Jan 9 13:52:23 2025 by llama3.2 3B Q4_K_M
