The barc0/llama3.2-1b-instruct-fft-transduction-engineer_lr1e-5_epoch4 model is a 1 billion parameter instruction-tuned causal language model, fine-tuned from Meta Llama-3.2-1B-Instruct. It specializes in transduction engineering tasks, having been trained on specific datasets focused on augmented transduction problems. This model is designed for applications requiring problem-solving and code generation related to transduction.
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Model Overview
This model, llama3.2-1b-instruct-fft-transduction-engineer_lr1e-5_epoch4, is a 1 billion parameter instruction-tuned language model. It is a fine-tuned variant of the meta-llama/Llama-3.2-1B-Instruct base model, specifically adapted for transduction engineering tasks.
Key Capabilities
- Specialized Fine-tuning: The model has undergone fine-tuning on a curated set of datasets, including
barc0/transduction_angmented_100k-gpt4-description-gpt4omini-code_generated_problems,barc0/transduction_angmented_100k_gpt4o-mini_generated_problems, andbarc0/transduction_rearc_dataset_400k. This training focuses on augmented transduction problems, suggesting proficiency in tasks related to transforming inputs based on specific rules or patterns. - Instruction Following: As an instruction-tuned model, it is designed to follow prompts and generate responses in line with given instructions.
Training Details
The model was trained with a learning rate of 1e-05 over 4 epochs, utilizing a total batch size of 256 across 8 GPUs. The training achieved a final validation loss of 0.0409.
Good For
- Transduction Engineering: Ideal for use cases involving transduction problems, particularly those requiring code generation or problem descriptions based on augmented data.
- Research and Development: Suitable for researchers and developers exploring specialized language model applications in engineering domains.