VICIOUS_MESH-12B
Collection
Vicious_Mesh-12B is the exciting new evolution of our MISCHIEVOUS-12B model, revealed through a playful anagram. :]
•
10 items
•
Updated
•
1
VICIOUS_MESH-12B-NEMO
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the TIES merge method using mistralai/Mistral-Nemo-Instruct-2407 as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
models:
- model: Mistral-Nemo-Instruct-2407
#no parameters necessary for base model
- model: VICIOUS_MESH-12B-BETA
parameters:
density: 1
weight: 1
merge_method: ties
base_model: Mistral-Nemo-Instruct-2407
tokenizer_source: base
chat_template: "auto"
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
regularization:
- method: weight_clipping
clip_range: [-0.1, 0.1]
- method: l2_norm
scale: 0.002
# Postprocessing to refine the merged model
postprocessing:
- operation: dynamic_scaling
scale_range: [0.9, 1.1]
- operation: gaussian_smoothing
sigma: 1.0
- operation: normalize
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 23.15 |
| IFEval (0-Shot) | 40.22 |
| BBH (3-Shot) | 34.40 |
| MATH Lvl 5 (4-Shot) | 11.71 |
| GPQA (0-shot) | 9.84 |
| MuSR (0-shot) | 12.57 |
| MMLU-PRO (5-shot) | 30.18 |
- 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 bamec66557/VICIOUS_MESH-12B-NEMO
Base model
mistralai/Mistral-Nemo-Base-2407
Finetuned
mistralai/Mistral-Nemo-Instruct-2407
Collection including bamec66557/VICIOUS_MESH-12B-NEMO
Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard40.220
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard34.400
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard11.710
- acc_norm on GPQA (0-shot)Open LLM Leaderboard9.840
- acc_norm on MuSR (0-shot)Open LLM Leaderboard12.570
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard30.180