Neelectric/Llama-3.1-8B-Instruct_SFT_mathsp_ewc_v00.11.2
Neelectric/Llama-3.1-8B-Instruct_SFT_mathsp_ewc_v00.11.2 is an 8 billion parameter instruction-tuned language model, fine-tuned from meta-llama/Llama-3.1-8B-Instruct. This model is specifically optimized for mathematical reasoning and problem-solving tasks, trained on the Neelectric/OpenR1-Math-220k_all_Llama3_4096toks dataset. With a context length of 32768 tokens, it is designed to excel in complex mathematical and logical challenges.
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Model Overview
Neelectric/Llama-3.1-8B-Instruct_SFT_mathsp_ewc_v00.11.2 is an 8 billion parameter instruction-tuned model, building upon the robust meta-llama/Llama-3.1-8B-Instruct architecture. Its primary differentiation lies in its specialized fine-tuning for mathematical reasoning and problem-solving.
Key Capabilities
- Enhanced Mathematical Performance: Fine-tuned on the
Neelectric/OpenR1-Math-220k_all_Llama3_4096toksdataset, this model is specifically trained to handle a wide range of mathematical queries and tasks. - Instruction Following: Retains the strong instruction-following capabilities of its base Llama-3.1-8B-Instruct model.
- Extended Context Window: Features a substantial context length of 32768 tokens, allowing for processing and understanding longer, more complex mathematical problems or multi-step reasoning tasks.
Training Details
The model was trained using Supervised Fine-Tuning (SFT) with the TRL framework, leveraging a dataset specifically curated for mathematical content. This targeted training approach aims to improve its accuracy and reasoning abilities in quantitative domains.
Good for
- Applications requiring strong mathematical problem-solving.
- Educational tools for math assistance.
- Research in quantitative reasoning with LLMs.
- Tasks benefiting from a large context window for detailed problem descriptions.