FlyPig23/Llama3.2-3B_Paper_Impact_patent_SFT_1ep
FlyPig23/Llama3.2-3B_Paper_Impact_patent_SFT_1ep is a 3.2 billion parameter Llama 3.2-based instruction-tuned model. It is fine-tuned on the 'paper_impact_patents_train' dataset, specializing in tasks related to patent analysis and scientific paper impact. This model is optimized for understanding and generating content within the domain of patent and research literature, offering a context length of 32768 tokens.
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Model Overview
FlyPig23/Llama3.2-3B_Paper_Impact_patent_SFT_1ep is a specialized large language model, fine-tuned from the meta-llama/Llama-3.2-3B-Instruct base model. With 3.2 billion parameters and a substantial context length of 32768 tokens, this model is designed for domain-specific applications.
Key Capabilities
- Domain-Specific Fine-tuning: The model has been fine-tuned exclusively on the
paper_impact_patents_traindataset, indicating a strong specialization in content related to scientific paper impact and patent analysis. - Llama 3.2 Architecture: Built upon the Llama 3.2 instruction-tuned architecture, it inherits robust language understanding and generation capabilities.
- Optimized for Specific Data: Its training on a focused dataset suggests enhanced performance for tasks requiring knowledge and reasoning within the patent and research literature domains.
Training Details
The model underwent 1 epoch of supervised fine-tuning with a learning rate of 2e-05 and a total training batch size of 128 across 4 GPUs. Evaluation on the training set showed a loss of 0.0694.
Ideal Use Cases
This model is particularly well-suited for applications requiring deep understanding or generation of text related to:
- Analyzing the impact of scientific papers.
- Processing and interpreting patent documents.
- Information extraction from research literature.
- Specialized question answering within the patent and academic research fields.