metadata
language:
- en
- ko
license: apache-2.0
library_name: transformers
tags:
- mergekit
- merge
- text-generation-inference
- not-for-all-audiences
base_model:
- bamec66557/MNRP_0.5
- bamec66557/MISCHIEVOUS-12B
model-index:
- name: MISCHIEVOUS-12B-Mix_0.1v
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 36.36
name: strict accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/MISCHIEVOUS-12B-Mix_0.1v
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 34.36
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/MISCHIEVOUS-12B-Mix_0.1v
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 12.76
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/MISCHIEVOUS-12B-Mix_0.1v
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 10.4
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/MISCHIEVOUS-12B-Mix_0.1v
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 11.54
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/MISCHIEVOUS-12B-Mix_0.1v
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 29.71
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bamec66557/MISCHIEVOUS-12B-Mix_0.1v
name: Open LLM Leaderboard
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:
- bamec66557/MNRP_0.5
- bamec66557/MISCHIEVOUS-12B
Configuration
The following YAML configuration was used to produce this model:
slices:
- Sources:
- model: bamec66557/MNRP_0.5
layer_range: [0, 40] # Merge layer range for MNRP_0.5 model
- model: bamec66557/MISCHIEVOUS-12B
layer_range: [0, 40] # Merge layer range for MISCHIEVOUS-12B model.
# Adjust the merge ratio per layer to drive smoother integration
# Each filter affects a specific mechanism within the model
parameters:
t:
- Filter: self_attn
value: [0.2, 0.4, 0.6, 0.8, 1.0] # Progressive merging of self-attention layers
- filter: mlp
value: [0.8, 0.6, 0.4, 0.2, 0.0] # Merge MLP layers with opposite proportions
- filter: layer_norm
value: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5] # Layer Normalisation should be merged uniformly
- value: 0.7 # Default
merge_method: slerp # change merge method to slerp
base_model: bamec66557/MISCHIEVOUS-12B # base model for merge
dtype: bfloat16 # data type for efficient and fast operations when merging
# Additional available options
regularisation:
- method: l2_norm # Stabilise merged model weights with L2 normalisation
scale: 0.01
postprocessing:
- operation: smoothing # Smooth the weights after merging
kernel_size: 3
- operation: normalise # normalise the overall weights
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 22.52 |
IFEval (0-Shot) | 36.36 |
BBH (3-Shot) | 34.36 |
MATH Lvl 5 (4-Shot) | 12.76 |
GPQA (0-shot) | 10.40 |
MuSR (0-shot) | 11.54 |
MMLU-PRO (5-shot) | 29.71 |