Description
This repo contains bf16 files of Lonepino-11B. Just a normal model.
Model used
- Intel/neural-chat-7b-v3-3-Slerp
- NeverSleep/Noromaid-7b-v0.2
- chargoddard/loyal-piano-m7-cdpo
- maywell/PiVoT-0.1-Starling-LM-RP
The secret sauce
neural-maid-11B:
slices:
- sources:
- model: Intel/neural-chat-7b-v3-3-Slerp
layer_range: [0, 24]
- sources:
- model: NeverSleep/Noromaid-7b-v0.2
layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
loyal-PiVoT-11B:
slices:
- sources:
- model: chargoddard/loyal-piano-m7-cdpo
layer_range: [0, 24]
- sources:
- model: maywell/PiVoT-0.1-Starling-LM-RP
layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
Lonepino-11B:
slices:
- sources:
- model: "./neural-maid-11B"
layer_range: [0, 48]
- model: "./loyal-PiVoT-11B"
layer_range: [0, 48]
merge_method: slerp
base_model: "./neural-maid-11B"
parameters:
t:
- value: 0.4
dtype: bfloat16
Prompt template
Alpaca. Or chatml. Or any you like.
=w=
I use mergekit for all the manipulation told here.
Thanks to the Undi95 for the original 11B mistral merge recipe.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 70.10 |
AI2 Reasoning Challenge (25-Shot) | 68.26 |
HellaSwag (10-Shot) | 84.57 |
MMLU (5-Shot) | 63.76 |
TruthfulQA (0-shot) | 63.45 |
Winogrande (5-shot) | 78.93 |
GSM8k (5-shot) | 61.64 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard68.260
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard84.570
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard63.760
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard63.450
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard78.930
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard61.640