HeynerMarqu/pathology_lora_model
The HeynerMarqu/pathology_lora_model is an 8 billion parameter Llama 3-based language model, fine-tuned from unsloth/llama-3-8b-Instruct-bnb-4bit. Developed by HeynerMarqu, this model was trained using Unsloth and Huggingface's TRL library for accelerated fine-tuning. It is specifically optimized for tasks related to pathology, leveraging its Llama 3 foundation for specialized domain understanding.
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Model Overview
The HeynerMarqu/pathology_lora_model is an 8 billion parameter language model, fine-tuned by HeynerMarqu. It is based on the unsloth/llama-3-8b-Instruct-bnb-4bit architecture, indicating its foundation in the Llama 3 family of models.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/llama-3-8b-Instruct-bnb-4bit, leveraging the capabilities of the Llama 3 architecture. - Parameter Count: Features 8 billion parameters, offering a balance between performance and computational efficiency.
- Training Method: The model was fine-tuned using Unsloth and Huggingface's TRL library, which enabled a 2x faster training process.
- Context Length: Supports an 8192-token context window.
Intended Use
This model is specifically designed and fine-tuned for applications within the pathology domain. Its specialized training aims to enhance its understanding and generation capabilities for pathology-related texts and tasks, making it suitable for researchers, clinicians, or developers working with medical pathology data.