Neelectric/Llama-3.1-8B-Instruct_SFT_mathsp_ewc_v00.09
Neelectric/Llama-3.1-8B-Instruct_SFT_mathsp_ewc_v00.09 is an 8 billion parameter instruction-tuned language model developed by Neelectric, fine-tuned from Meta's Llama-3.1-8B-Instruct. This model specializes in mathematical and reasoning tasks, leveraging a fine-tuning process on the OpenR1-Math-220k dataset. It is designed for applications requiring strong performance in mathematical problem-solving and logical inference, supporting a context length of 32768 tokens.
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Overview
Neelectric/Llama-3.1-8B-Instruct_SFT_mathsp_ewc_v00.09 is an 8 billion parameter instruction-tuned model, building upon Meta's Llama-3.1-8B-Instruct. Developed by Neelectric, this model has undergone supervised fine-tuning (SFT) using the TRL framework.
Key Capabilities
- Mathematical Reasoning: The model is specifically fine-tuned on the
Neelectric/OpenR1-Math-220k_all_Llama3_4096toksdataset, indicating a strong focus on mathematical problem-solving and related reasoning tasks. - Instruction Following: As an instruction-tuned variant, it is designed to accurately follow user prompts and generate relevant responses.
- Extended Context: It supports a substantial context length of 32768 tokens, allowing for processing longer inputs and maintaining coherence over extended interactions.
Training Details
The model was trained using the TRL (Transformers Reinforcement Learning) library. The fine-tuning process involved SFT, leveraging a specialized mathematical dataset to enhance its performance in this domain.
Good For
- Applications requiring robust mathematical problem-solving.
- Tasks involving logical reasoning and inference.
- Instruction-following scenarios where precise and context-aware responses are crucial.