e12ex2/merged-secret-hermes
The e12ex2/merged-secret-hermes is an 8 billion parameter language model, merged from NousResearch/Hermes-2-Theta-Llama-3-8B and e12ex2/Foundation-Sec-8B-Instruct-heretic using the TIES method, based on NousResearch/Meta-Llama-3-8B-Instruct. This model combines the strengths of its constituent models, offering a versatile foundation for various instruction-following tasks. It is designed for general-purpose applications requiring robust language understanding and generation capabilities within an 8K context length.
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Model Overview
The e12ex2/merged-secret-hermes is an 8 billion parameter language model created through a strategic merge of existing pre-trained models. It leverages the TIES merge method to combine the capabilities of its components, building upon the robust foundation of NousResearch/Meta-Llama-3-8B-Instruct.
Key Merge Details
This model integrates two distinct instruction-tuned models:
- NousResearch/Hermes-2-Theta-Llama-3-8B: A model known for its instruction-following prowess.
- e12ex2/Foundation-Sec-8B-Instruct-heretic: Another instruction-tuned variant contributing to the merged model's overall performance.
The merge process utilized specific density and weight parameters for each constituent model (0.5 density, 0.6 weight for Foundation-Sec-8B-Instruct-heretic and 0.5 density, 0.4 weight for Hermes-2-Theta-Llama-3-8B), aiming to optimize their combined strengths. The configuration also included normalization and int8 masking, with the tokenizer sourced from the base model.
Intended Use Cases
Given its lineage from instruction-tuned models and the Llama 3 base, merged-secret-hermes is well-suited for a variety of applications requiring:
- General-purpose instruction following: Responding to prompts and carrying out diverse language tasks.
- Text generation: Creating coherent and contextually relevant text.
- Conversational AI: Engaging in dialogue and understanding user queries.
This 8B parameter model offers a balanced approach for developers seeking a capable and efficient language model for deployment.