Merges
Collection
Experimental LLM merging
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1292 items
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Updated
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7
Merge of top 7B models and the SLERP of other 7B models
mergekit is a toolkit for merging pre-trained language models. mergekit uses an out-of-core approach to perform unreasonably elaborate merges in resource-constrained situations. Merges can be run entirely on CPU or accelerated with as little as 8 GB of VRAM. Many merging algorithms are supported, with more coming as they catch my attention.
Eval
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"harness|hendrycksTest-security_studies|5": { "acc": 0.7346938775510204, "acc_stderr": 0.028263889943784603, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784603 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454115, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454115 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.85, "acc_stderr": 0.03588702812826371, "acc_norm": 0.85, "acc_norm_stderr": 0.03588702812826371 }, "harness|hendrycksTest-virology|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness|truthfulqa:mc|0": { "mc1": 0.4834761321909425, "mc1_stderr": 0.017493940190057723, "mc2": 0.6447306680251751, "mc2_stderr": 0.015519245883344577 }, "harness|winogrande|5": { "acc": 0.8366219415943172, "acc_stderr": 0.010390695970273764 }, "harness|gsm8k|5": { "acc": 0.7202426080363912, "acc_stderr": 0.012364384016735319 } }
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/details_MaziyarPanahi__TheTop-5x7B-Instruct-S3-v0.1)
| Metric |Value|
|---------------------------------|----:|
|Avg. |74.03|
|AI2 Reasoning Challenge (25-Shot)|70.90|
|HellaSwag (10-Shot) |88.00|
|MMLU (5-Shot) |65.13|
|TruthfulQA (0-shot) |64.47|
|Winogrande (5-shot) |83.66|
|GSM8k (5-shot) |72.02|