Kukedlc/Ramakrishna-7b-v3
Kukedlc/Ramakrishna-7b-v3 is a 7 billion parameter language model created by Kukedlc, formed by merging several models including automerger/YamShadow-7B, Kukedlc/Neural4gsm8k, and Kukedlc/NeuralSirKrishna-7b using the DARE TIES merge method. This model is designed for general language tasks, leveraging the combined strengths of its constituent models. It is suitable for applications requiring a robust 7B parameter model.
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Kukedlc/Ramakrishna-7b-v3: A Merged Language Model
Ramakrishna-7b-v3 is a 7 billion parameter language model developed by Kukedlc. It is a product of merging multiple specialized models using the LazyMergekit tool, specifically employing the dare_ties merge method.
Key Components and Merge Strategy
This model integrates capabilities from several base models, each contributing to its overall performance. The primary base model is automerger/YamShadow-7B, which is then combined with contributions from:
Kukedlc/Neural4gsm8kKukedlc/NeuralSirKrishna-7bmlabonne/NeuBeagle-7BKukedlc/Ramakrishna-7bKukedlc/NeuralGanesha-7b
The merge configuration specifies varying densities and weights for each contributing model, indicating a deliberate effort to balance their influences. The int8_mask parameter is enabled, and the model uses bfloat16 for its data type, suggesting optimizations for efficiency and performance.
Usage
Developers can easily integrate Ramakrishna-7b-v3 into their projects using the Hugging Face transformers library. The provided Python code snippet demonstrates how to load the model and tokenizer, apply a chat template for conversational prompts, and generate text. This setup allows for straightforward deployment in various natural language processing tasks.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.