Neelectric/Llama-3.1-8B-Instruct_SFT_Math-220kv00.13
Neelectric/Llama-3.1-8B-Instruct_SFT_Math-220kv00.13 is an 8 billion parameter instruction-tuned causal language model, fine-tuned by Neelectric from the Llama-3.1-8B-Instruct architecture. This model is specifically optimized for mathematical reasoning and problem-solving, leveraging a specialized dataset for supervised fine-tuning. It is designed to excel in tasks requiring numerical understanding and logical mathematical operations, offering a focused capability for quantitative applications.
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Model Overview
Neelectric/Llama-3.1-8B-Instruct_SFT_Math-220kv00.13 is an 8 billion parameter language model derived from the meta-llama/Llama-3.1-8B-Instruct base model. It has undergone supervised fine-tuning (SFT) using the Neelectric/Replay_0.05.OpenR1-Math-220k_extended.wildguardmix.Llama3_4096toks dataset, specifically targeting enhanced performance in mathematical tasks.
Key Capabilities
- Mathematical Reasoning: Specialized fine-tuning on a math-centric dataset aims to improve the model's ability to understand and solve mathematical problems.
- Instruction Following: Retains the instruction-following capabilities of its Llama-3.1-8B-Instruct base, allowing for guided interactions.
- Context Length: Supports a context length of 32768 tokens, enabling processing of longer mathematical problems or discussions.
Training Details
The model was trained using the TRL (Transformer Reinforcement Learning) library, with specific framework versions including TRL 0.26.0.dev0 and Transformers 4.57.3. This SFT approach focuses on direct optimization for mathematical accuracy and response generation.
Good For
- Applications requiring strong mathematical problem-solving.
- Educational tools for math assistance.
- Research into fine-tuning LLMs for domain-specific numerical tasks.