ExLlamaV2 quantizations
Collection
All my EXL2 quants here.
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32 items
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Updated
This is an ExLlamaV2 quantized model of saishf/Kuro-Lotus-10.7B using the default calibration dataset. The quants are uploaded on individual branches and the list is here: 4bpw 3.75bpw 3.5bpw 3.25bpw 3bpw
Prompt format is Alpaca.
Original Model card
merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the SLERP merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: Himitsui/KuroMitsu-11B
layer_range: [0, 48]
- model: BlueNipples/SnowLotus-v2-10.7B
layer_range: [0, 48]
merge_method: slerp
base_model: Himitsui/KuroMitsu-11B
parameters:
t:
- filter: self_attn
value: [0.6, 0.7, 0.8, 0.9, 1]
- filter: mlp
value: [0.4, 0.3, 0.2, 0.1, 0]
- value: 0.5
dtype: bfloat16
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 71.90 |
| AI2 Reasoning Challenge (25-Shot) | 68.69 |
| HellaSwag (10-Shot) | 87.51 |
| MMLU (5-Shot) | 66.64 |
| TruthfulQA (0-shot) | 58.27 |
| Winogrande (5-shot) | 84.21 |
| GSM8k (5-shot) | 66.11 |
Model tree for mpasila/Kuro-Lotus-10.7B-exl2
Collection including mpasila/Kuro-Lotus-10.7B-exl2
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard68.690
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard87.510
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard66.640
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard58.270
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard84.210
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard66.110