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README.md
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license: apache-2.0
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---
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/9OI19I3DhuPp_i8Uhp6ss.png)
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# Example Output
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> Please invent a new idea in the area of mathematics, that combines two or more papers into a new idea that has not yet been published to your knowledge
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---
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license: apache-2.0
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base_model:
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- cognitivecomputations/dolphin-2.2-70b
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- WizardLM/WizardMath-70B-V1.0
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- migtissera/SynthIA-70B-v1.2b
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- epfl-llm/meditron-70b
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tags:
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- mergekit
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- merge
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---
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/9OI19I3DhuPp_i8Uhp6ss.png)
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This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
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## Merge Details
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### Merge Method
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This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.
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### Models Merged
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The following models were included in the merge:
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* [cognitivecomputations/dolphin-2.2-70b](https://huggingface.co/cognitivecomputations/dolphin-2.2-70b)
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* [WizardLM/WizardMath-70B-V1.0](https://huggingface.co/WizardLM/WizardMath-70B-V1.0)
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* [migtissera/SynthIA-70B-v1.2b](https://huggingface.co/migtissera/SynthIA-70B-v1.2b)
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* [epfl-llm/meditron-70b](https://huggingface.co/epfl-llm/meditron-70b)
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### Configuration
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The following YAML configuration was used to produce this model:
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```yaml
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merge_method: linear # use linear so we can include multiple models, albeit at a zero weight
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parameters:
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weight: 1.0 # weight everything as 1 unless specified otherwise - linear with one model weighted at 1 is a no-op like passthrough
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slices:
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- sources:
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- model: cognitivecomputations/dolphin-2.2-70b # embed_tokens comes along with the ride with whatever is the first layer
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layer_range: [0, 1]
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- model: migtissera/SynthIA-70B-v1.2b # add dummy second model with 0 weight so tokenizer-based merge routine is invoked for embed_tokens
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layer_range: [0, 1]
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parameters:
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weight: 0
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- sources:
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- model: cognitivecomputations/dolphin-2.2-70b
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layer_range: [1, 20]
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- sources:
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- model: migtissera/SynthIA-70B-v1.2b
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layer_range: [10, 30]
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- sources:
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- model: WizardLM/WizardMath-70B-V1.0
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layer_range: [20, 40]
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- sources:
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- model: epfl-llm/meditron-70b
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layer_range: [25, 45]
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- sources:
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- model: cognitivecomputations/dolphin-2.2-70b
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layer_range: [30, 50]
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- sources:
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- model: migtissera/SynthIA-70B-v1.2b
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layer_range: [40, 60]
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- sources:
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- model: WizardLM/WizardMath-70B-V1.0
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layer_range: [50, 70]
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- sources:
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- model: epfl-llm/meditron-70b
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layer_range: [55, 75]
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- sources:
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- model: cognitivecomputations/dolphin-2.2-70b
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layer_range: [60, 79]
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- sources: # same as above, but for lm_head with the last layer
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- model: cognitivecomputations/dolphin-2.2-70b
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layer_range: [79, 80]
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- model: migtissera/SynthIA-70B-v1.2b
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layer_range: [79, 80]
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parameters:
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weight: 0
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dtype: float16
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tokenizer_source: model:cognitivecomputations/dolphin-2.2-70b # keep exact tokenizer used by dolphin - or you could use `union` if you add all of the input models to the first/last slice, but they would need to be non-zero weight or you'll get NaNs in your embeddings
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```
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# Example Output
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> Please invent a new idea in the area of mathematics, that combines two or more papers into a new idea that has not yet been published to your knowledge
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