Llama 3.1 Daredevilish Instruct
- This model is an experimental Llama 3.1-based merge, inspired by the approach used in mlabonne/Daredevil-8B.
- It combines top-performing Llama 3.1 8B models on the MMLU-Pro benchmark from the Open LLM Leaderboard as of January 21, 2025.
- Its straightforward language makes it accessible and potentially valuable for everyday use.
Model Details
- Architecture: Llama 3.1 (8.03B parameters)
- Training: Merged from top MMLU-Pro models without additional finetuning
- Release Date: January 21, 2025
Merge Configuration
The model was created using mergekit with the following merge configuration:
models:
- model: DreadPoor/LemonP-8B-Model_Stock
parameters:
density: 0.6
weight: 0.16
- model: Youlln/1PARAMMYL-8B-ModelStock
parameters:
density: 0.6
weight: 0.13
- model: jaspionjader/f-2-8b
parameters:
density: 0.6
weight: 0.10
- model: Etherll/SuperHermes
parameters:
density: 0.6
weight: 0.08
merge_method: dare_ties
base_model: meta-llama/Llama-3.1-8B-Instruct
dtype: bfloat16
Usage and Limitations
This experimental model is designed for research and development purposes. Users should be aware of potential biases and limitations inherent in language models. Always validate outputs and use the model responsibly.
Future Work
Further evaluation and fine-tuning may be necessary to optimize performance across various tasks. Researchers are encouraged to build upon this experimental merge to advance the capabilities of Llama-based models.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here! Summarized results can be found here!
Metric | Value (%) |
---|---|
Average | 29.32 |
IFEval (0-Shot) | 79.41 |
BBH (3-Shot) | 32.22 |
MATH Lvl 5 (4-Shot) | 16.77 |
GPQA (0-shot) | 7.61 |
MuSR (0-shot) | 7.92 |
MMLU-PRO (5-shot) | 31.97 |
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Evaluation results
- averaged accuracy on IFEval (0-Shot)Open LLM Leaderboard79.410
- normalized accuracy on BBH (3-Shot)test set Open LLM Leaderboard32.220
- exact match on MATH Lvl 5 (4-Shot)test set Open LLM Leaderboard16.770
- acc_norm on GPQA (0-shot)Open LLM Leaderboard7.610
- acc_norm on MuSR (0-shot)Open LLM Leaderboard7.920
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard31.970