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uukuguy_-_speechless-mistral-7b-dare-0.85-8bits
RichardErkhov/uukuguy_-_speechless-mistral-7b-dare-0.85-8bits
uukuguy_-_speechless-mistral-7b-dare-0.85-8bits is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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From the Hugging Face model README
Quantization made by Richard Erkhov.
speechless-mistral-7b-dare-0.85 - bnb 8bits
- Model creator: https://huggingface.co/uukuguy/
- Original model: https://huggingface.co/uukuguy/speechless-mistral-7b-dare-0.85/
Original model description:
license: llama2
Experiment for DARE(Drop and REscale), most of the delta parameters can be directly set to zeros without affecting the capabilities of SFT LMs and larger models can tolerate a higher proportion of discarded parameters.
Merged with below DARE models.
weight_mask_rate: 0.85 / use_weight_rescale: True / mask_stratery: random / scaling_coefficient: 1.0
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | DROP |
|---|---|---|---|---|---|---|---|---|
| Intel/neural-chat-7b-v3-1 | 59.06 | 66.21 | 83.64 | 62.37 | 59.65 | 78.14 | 19.56 | 43.84 |
| migtissera/SynthIA-7B-v1.3 | 57.11 | 62.12 | 83.45 | 62.65 | 51.37 | 78.85 | 17.59 | 43.76 |
| bhenrym14/mistral-7b-platypus-fp16 | 56.89 | 63.05 | 84.15 | 64.11 | 45.07 | 78.53 | 17.36 | 45.92 |
| jondurbin/airoboros-m-7b-3.1.2 | 56.24 | 61.86 | 83.51 | 61.91 | 53.75 | 77.58 | 13.87 | 41.2 |
| teknium/CollectiveCognition-v1.1-Mistral-7B | 53.87 | 62.12 | 84.17 | 62.35 | 57.62 | 75.37 | 15.62 | 19.85 |
| uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b | 53.34 | 64.33 | 84.4 | 63.72 | 52.52 | 78.37 | 21.38 | 8.66 |
2023.12.04
It seems that there are some issues with the calculation of the GSM8K and DROP metrics on the Open LLM Leaderboard. Currently, the DROP metric has been removed from the official website, while the calculation of GSM8K metric remains chaotic, with significant differences in values among various models. Therefore, I am temporarily using ARC, HellaSwag, MMLU, TruthfulQA, and Winogrande metrics to evaluate the performance of DARE.
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande |
|---|---|---|---|---|---|---|
| CollectiveCognition-v1.1-Mistral-7B | 68.326 | 62.12 | 84.17 | 62.35 | 57.62 | 75.37 |
| CollectiveCognition-v1.1-Mistral-7B-dare-0.85 | 66.676 | 61.01 | 84.31 | 64.34 | 44.87 | 78.85 |
| airoboros-m-7b-3.1.2 | 67.722 | 61.86 | 83.51 | 61.91 | 53.75 | 77.58 |
| airoboros-m-7b-3.1.2-dare-0.85 | 66.144 | 61.09 | 83.57 | 64.05 | 43.64 | 78.37 |
| SynthIA-7B-v1.3 | 67.688 | 62.12 | 83.45 | 62.65 | 51.37 | 78.85 |
| SynthIA-7B-v1.3-dare-0.85 | 66.340 | 61.01 | 83.50 | 64.49 | 43.77 | 78.93 |
| speechless-mistral-7b-dare-0.85 (merge 6 DARE models) | 68.516 | 63.57 | 84.82 | 64.29 | 50.66 | 79.24 |
From the official website evaluation results, after deleting 85% of the incremental parameters, the overall indicators remain above 97.5% of the original performance indicators. Among them, ARC slightly decreases, TruthfulQA significantly decreases, MMLU significantly increases, and HellaSwagt and Winogrande slightly increase. The most significant impact is the significant decrease in TruthfulQA, while other indicators are relatively well maintained, with MMLU showing a noticeable increase.
- Type
- mistral
- Created
- Sep 25, 2024
- Updated
- Sep 25, 2024