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---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# merged_model_output
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DELLA](https://arxiv.org/abs/2406.11617) merge method using /media/administrator/oiseauxai1data/modelweights/Smart-base-V2 as a base.
### Models Merged
The following models were included in the merge:
* /media/administrator/oiseauxai1data1/Dark-Base-V3
* /media/administrator/oiseauxai1data/modelweights/Story-Base-V3
* /media/administrator/oiseauxai1data1/Middle-Base-V3
### Configuration
The following YAML configuration was used to produce this model:
```yaml
# --- Mergekit Example: della_linear ---
# Method: Implements the DELLA concept (Deep Ensembling with Layer-wise Linear Averaging).
# This typically involves a sophisticated layer-wise linear combination of models.
base_model: /media/administrator/oiseauxai1data/modelweights/Smart-base-V2 # The foundational model
models:
- model: /media/administrator/oiseauxai1data1/Dark-Base-V3
parameters:
weight: [0.3, 0.2, 0.5] # Contribution of this model (e.g., 50%) (can also use a gradiant) [0.1, 0.1, 0.1, 0.2, 0.5]
density: 0.60 # Sparsity/pruning factor for this model's contribution.
epsilon: 0.15 # Single epsilon for the pruning
- model: /media/administrator/oiseauxai1data/modelweights/Story-Base-V3
parameters:
weight: [0.5, 0.2, 0.3] # Contribution of this model (e.g., 50%) (can also use a gradiant) [0.1, 0.1, 0.1, 0.2, 0.5]
density: 0.50 # Sparsity/pruning factor for this model's contribution.
epsilon: 0.15 # Single epsilon for the pruning
- model: /media/administrator/oiseauxai1data1/Middle-Base-V3
parameters:
weight: [0.2, 0.6, 0.2] # Contribution of this model (e.g., 50%) (can also use a gradiant) [0.1, 0.1, 0.1, 0.2, 0.5]
density: 0.50 # Sparsity/pruning factor for this model's contribution.
epsilon: 0.15 # Single epsilon for the pruning
model_name: L3.3-70B-Amalgamma-V9 # Name of your merge
dtype: float32 # Input size float32, float16, bfloat16
out_dtype: bfloat16 # output size float32, float16, bfloat16
merge_method: della
parameters:
normalize: false # If true (default), weights are normalized to sum to 1.
# If false, absolute weights are used.
lambda: 1.1 # Single lambda for scaling the final merged deltas
tokenizer_source: base # Or 'base' if base_model is set, or 'union', careful with this one
chat_template: llama3 # Template for chat (Chatml, llama3, etc...)
license: apache-2.0 # License type
```
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