Made by Liminerity <#
INEX4-7b
INEX4-7b is a merge of the following models using mergekit:
🧩 Configuration
slices:
- sources:
- model: liminerity/Ingot-7b-slerp-7-forged
layer_range: [0, 32]
- model: yam-peleg/Experiment26-7B
layer_range: [0, 32]
merge_method: slerp
base_model: liminerity/Ingot-7b-slerp-7-forged
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
model: liminerity/merge
slices:
- sources:
- model: liminerity/Ingot-7b-slerp-7-forged
layer_range: [0, 32]
- model: liminerity/merge
layer_range: [0, 32]
merge_method: slerp
base_model: liminerity/Ingot-7b-slerp-7-forged
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
model: liminerity/merge1
slices:
- sources:
- model: yam-peleg/Experiment26-7B
layer_range: [0, 32]
- model: liminerity/merge1
layer_range: [0, 32]
merge_method: slerp
base_model: yam-peleg/Experiment26-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: float16
model: liminerity/merge2
slices:
- sources:
- model: liminerity/merge2
layer_range: [0, 32]
- model: liminerity/merge1
layer_range: [0, 32]
merge_method: slerp
base_model: liminerity/merge2
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
model: INEX-7b
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 75.84 |
AI2 Reasoning Challenge (25-Shot) | 72.95 |
HellaSwag (10-Shot) | 88.79 |
MMLU (5-Shot) | 64.70 |
TruthfulQA (0-shot) | 74.42 |
Winogrande (5-shot) | 83.90 |
GSM8k (5-shot) | 70.28 |
- Downloads last month
- 82
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for MSL7/INEX4-7b
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard72.950
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard88.790
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.700
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard74.420
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard83.900
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard70.280