merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the passthrough merge method.
Models Merged
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Enhance Upper Layers (Deep Layers):
- Focus on deep layers: Higher layers (e.g., from 30 upwards in a model with many layers) tend to capture abstract concepts and complex semantic connections between words or phrases. Increasing their weight or importance (scaling up projections like o_proj or down_proj) can improve the model's reasoning capabilities.
- Increase model depth: If possible, you can also add new layers or extend the range of existing ones, instead of cutting them. Greater depth allows the model to capture high-level patterns and improve its ability to solve complex cognitive tasks.
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Optimize Intermediate Layers to Improve Semantic Coherence:
- Enhance intermediate layers: These layers are responsible for linking low-level understanding (syntax and structure) to high-level understanding (abstraction). By enhancing these layers, the model will be able to maintain stronger coherence in cognitive processes. You can do this by increasing projection parameters and reducing penalties on the weights of these layers.
- Increase long-term memory capacity: If you configure the intermediate layers with a greater capacity to "memorize" longer contexts, the model will be better at maintaining the logical thread of more complex texts.
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Increase Capacity of Critical Projections:
- Positive scaling of projections: Projections like o_proj and down_proj handle the transformation and transmission of information between layers. Increasing their weight, rather than reducing or zeroing it, amplifies the model's ability to process and propagate relevant information. This can improve the model's ability to make deep inferences, resolve linguistic ambiguities, and maintain logical coherence in longer texts.
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Custom Configuration for Complex Tasks:
- Focus on specific tasks: If you want to enhance the model for advanced cognitive tasks, you can customize the configuration depending on the type of task. For example, for long-term coherent and fluent text generation, you can emphasize the final layers and projection parameters, while for classification tasks, you can give more weight to the intermediate layers.
- Improve abstraction and contextual understanding: Tasks such as logical reasoning or long text comprehension require greater abstraction capacity. By enhancing the model's final layers, where more abstract concepts develop, you can make the model more performant in these areas.
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Add or Improve Attention Functions:
- Multi-Head Attention: Attention is a key mechanism for abstraction and inference. By increasing the number of attention heads or enhancing their contribution in the advanced stages of the model, you can improve the model's ability to focus on complex semantic relationships in the text.
- Strengthen attention capacity in upper layers: By giving greater importance to attention in deeper layers, the model will be able to handle long contexts and maintain long-term coherence, a critical factor in tasks requiring deep cognitive understanding.
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Maintain Computational Capacity:
- Don't reduce intermediate and upper layers: Unlike a reduction process where you eliminate or reduce the importance of some layers, here it's crucial to maintain (or enhance) all layers from a computational perspective. Each layer has a role in creating more refined representations, and by maintaining the integrity of the entire network, the model will be able to handle complex reasoning tasks.
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Effect of the Process:
- Increased accuracy and understanding: A model optimized to increase cognitive abilities will be more precise in complex tasks, such as generating fluent text, resolving semantic ambiguities, reasoning on long texts, and understanding context.
- Greater computational load: However, with this configuration, the computational load and inference times will also increase, as you are increasing the complexity of the model's operations. This is the trade-off: more cognitive capabilities mean more resource usage. The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: Sao10K/Fimbulvetr-11B-v2
layer_range: [0, 4]
- sources:
- model: Sao10K/Fimbulvetr-11B-v2
layer_range: [4, 8]
parameters:
scale:
- filter: o_proj
value: 1.5
- filter: down_proj
value: 1.5
- sources:
- model: Sao10K/Fimbulvetr-11B-v2
layer_range: [8, 12]
parameters:
scale:
- filter: o_proj
value: 1.5
- filter: down_proj
value: 1.5
- sources:
- model: Sao10K/Fimbulvetr-11B-v2
layer_range: [12, 16]
parameters:
scale:
- filter: o_proj
value: 2.0
- filter: down_proj
value: 2.0
- sources:
- model: Sao10K/Fimbulvetr-11B-v2
layer_range: [16, 20]
parameters:
scale:
- filter: o_proj
value: 2.0
- filter: down_proj
value: 2.0
- sources:
- model: Sao10K/Fimbulvetr-11B-v2
layer_range: [20, 24]
parameters:
scale:
- filter: o_proj
value: 2.5
- filter: down_proj
value: 2.5
- sources:
- model: Sao10K/Fimbulvetr-11B-v2
layer_range: [24, 28]
parameters:
scale:
- filter: o_proj
value: 2.5
- filter: down_proj
value: 2.5
- sources:
- model: Sao10K/Fimbulvetr-11B-v2
layer_range: [28, 32]
parameters:
scale:
- filter: o_proj
value: 3.0
- filter: down_proj
value: 3.0
- sources:
- model: Sao10K/Fimbulvetr-11B-v2
layer_range: [32, 36]
parameters:
scale:
- filter: o_proj
value: 3.0
- filter: down_proj
value: 3.0
- sources:
- model: Sao10K/Fimbulvetr-11B-v2
layer_range: [36, 40]
parameters:
scale:
- filter: o_proj
value: 3.5
- filter: down_proj
value: 3.5
- sources:
- model: Sao10K/Fimbulvetr-11B-v2
layer_range: [40, 44]
parameters:
scale:
- filter: o_proj
value: 3.5
- filter: down_proj
value: 3.5
- sources:
- model: Sao10K/Fimbulvetr-11B-v2
layer_range: [44, 47]
parameters:
scale:
- filter: o_proj
value: 4.0
- filter: down_proj
value: 4.0
merge_method: passthrough
dtype: bfloat16