menesnas/gemma_4_pharmacy_merged
The menesnas/gemma_4_pharmacy_merged is a 7.9 billion parameter Gemma-4 based causal language model developed by menesnas. It was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. This model is specifically optimized for pharmacy-related applications, leveraging its fine-tuning to excel in this specialized domain.
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Overview
The menesnas/gemma_4_pharmacy_merged model is a 7.9 billion parameter language model based on the Gemma-4 architecture. It was developed by menesnas and fine-tuned using the Unsloth library in conjunction with Huggingface's TRL library. This approach allowed for significantly faster training, reportedly 2x quicker than standard methods.
Key Characteristics
- Base Model: Gemma-4
- Parameter Count: 7.9 billion
- Training Optimization: Utilizes Unsloth and Huggingface TRL for accelerated fine-tuning.
- Context Length: Supports a context length of 32768 tokens.
Intended Use
This model is specifically fine-tuned for applications within the pharmacy domain. Its specialized training makes it suitable for tasks requiring knowledge and understanding of pharmaceutical concepts, terminology, and related information. Developers looking for a Gemma-4 based model with enhanced performance in pharmacy-specific contexts would find this model particularly useful.