flemmingmiguel/NeuDist-Ro-7B
NeuDist-Ro-7B is a 7 billion parameter language model developed by flemmingmiguel, created by merging argilla/distilabeled-Marcoro14-7B-slerp and mlabonne/NeuralMarcoro14-7B. This model is an experimental merge of two DPO-tuned versions of the same base model, designed to explore optimal base merges for further fine-tuning. It focuses on identifying strong foundational models through comparative benchmarking.
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Overview
NeuDist-Ro-7B is a 7 billion parameter language model developed by flemmingmiguel. It is an experimental merge of two distinct DPO (Direct Preference Optimization) versions of the Marcoro14-7B model: argilla/distilabeled-Marcoro14-7B-slerp and mlabonne/NeuralMarcoro14-7B. The primary goal of this merge is to identify the most effective base model configuration for subsequent fine-tuning efforts through systematic experimentation and benchmarking.
Key Characteristics
- Model Architecture: A merge of two DPO-tuned 7B parameter models.
- Merge Method: Utilizes the
slerp(spherical linear interpolation) merge method, with specific parameter weighting applied to self-attention and MLP layers. - Experimental Focus: Designed as a testbed to compare and evaluate different merged base models.
Use Cases
- Research & Development: Ideal for researchers and developers exploring model merging techniques and their impact on performance.
- Base Model Selection: Useful for identifying strong foundational models before applying further fine-tuning or domain adaptation.
- Benchmarking: Provides a platform for comparative analysis of merged model configurations.