Neelectric/Llama-3.1-8B-Instruct_SFT_mathsp_ewc_v00.01
Neelectric/Llama-3.1-8B-Instruct_SFT_mathsp_ewc_v00.01 is an 8 billion parameter instruction-tuned language model, fine-tuned from Meta's Llama-3.1-8B-Instruct. It was specifically trained on the Neelectric/OpenR1-Math-220k_all_Llama3_4096toks dataset using SFT, making it optimized for mathematical reasoning and problem-solving tasks. With a context length of 32768 tokens, this model is designed for applications requiring robust mathematical capabilities.
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Model Overview
Neelectric/Llama-3.1-8B-Instruct_SFT_mathsp_ewc_v00.01 is an 8 billion parameter instruction-tuned language model, building upon the foundation of Meta's Llama-3.1-8B-Instruct. This model has been specifically fine-tuned using Supervised Fine-Tuning (SFT) on the Neelectric/OpenR1-Math-220k_all_Llama3_4096toks dataset.
Key Capabilities
- Mathematical Reasoning: Optimized for handling mathematical problems and generating accurate solutions due to its specialized training dataset.
- Instruction Following: Retains strong instruction-following capabilities inherited from its base Llama-3.1-8B-Instruct model.
- Extended Context: Supports a context length of 32768 tokens, allowing for processing longer and more complex mathematical prompts or discussions.
Training Details
The model was trained using the TRL library, a framework for Transformer Reinforcement Learning. The training procedure involved SFT, focusing on enhancing its performance in mathematical domains. The development utilized specific versions of key frameworks including TRL 1.1.0.dev0, Transformers 4.57.6, Pytorch 2.9.0, Datasets 4.8.5, and Tokenizers 0.22.2.
Use Cases
This model is particularly well-suited for applications requiring strong mathematical understanding and problem-solving, such as:
- Educational tools for math assistance.
- Automated problem-solving in quantitative fields.
- Generating explanations for mathematical concepts.