Neelectric/Llama-3.1-8B-Instruct_SFT_mathsp_ewc_v00.19

TEXT GENERATIONPricing:Input $0.2 / Cached $0.028 / Output $0.32Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 11, 2026Architecture:Transformer Featherless Exclusive Cold

Neelectric/Llama-3.1-8B-Instruct_SFT_mathsp_ewc_v00.19 is an 8 billion parameter instruction-tuned causal language model, fine-tuned by Neelectric from Meta's Llama-3.1-8B-Instruct. This model specializes in mathematical reasoning and problem-solving, having been trained on the OpenR1-Math-220k dataset. It leverages a 32768 token context length, making it suitable for complex mathematical tasks requiring extensive context.

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Model Overview

Neelectric/Llama-3.1-8B-Instruct_SFT_mathsp_ewc_v00.19 is an 8 billion parameter instruction-tuned model developed by Neelectric. It is a specialized fine-tune of the meta-llama/Llama-3.1-8B-Instruct base model, specifically optimized for mathematical reasoning and problem-solving.

Key Capabilities

  • Mathematical Reasoning: The model has undergone Supervised Fine-Tuning (SFT) on the Neelectric/OpenR1-Math-220k_all_Llama3_4096toks dataset, enhancing its ability to understand and solve mathematical problems.
  • Instruction Following: As an instruction-tuned model, it is designed to follow user prompts effectively for various tasks, particularly those involving numerical and logical operations.
  • Extended Context: It supports a context length of 32768 tokens, allowing for the processing of longer and more complex mathematical problems or multi-step reasoning tasks.

Training Details

The model was trained using the TRL (Transformers Reinforcement Learning) library, indicating a focus on refining its response generation based on specific objectives. The training procedure involved SFT on a dedicated mathematical dataset, distinguishing it from general-purpose instruction-tuned models.

Should I use this for my use case?

This model is particularly well-suited for applications requiring strong mathematical capabilities, such as:

  • Automated math problem solvers.
  • Educational tools for explaining mathematical concepts.
  • Generating code or solutions for mathematical algorithms.
  • Any task where precise numerical reasoning and logical deduction are paramount.

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