Neelectric/Llama-3.1-8B-Instruct_SFT_mathsp_ewc_v00.16

TEXT GENERATIONConcurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 14, 2026Architecture:Transformer Featherless Exclusive Cold

Neelectric/Llama-3.1-8B-Instruct_SFT_mathsp_ewc_v00.16 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, leveraging the Neelectric/OpenR1-Math-220k_all_Llama3_4096toks dataset. With a context length of 32768 tokens, it is designed for applications requiring robust mathematical capabilities.

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Model Overview

Neelectric/Llama-3.1-8B-Instruct_SFT_mathsp_ewc_v00.16 is an 8 billion parameter instruction-tuned model, built upon the robust meta-llama/Llama-3.1-8B-Instruct architecture. This model has undergone supervised fine-tuning (SFT) using the TRL library, specifically targeting enhanced performance in mathematical domains.

Key Capabilities

  • Mathematical Reasoning: The model is fine-tuned on the extensive Neelectric/OpenR1-Math-220k_all_Llama3_4096toks dataset, making it particularly adept at understanding and solving mathematical problems.
  • Instruction Following: As an instruction-tuned model, it is designed to follow user prompts effectively, providing relevant and coherent responses.
  • Extended Context: With a context length of 32768 tokens, it can process and generate longer sequences of text, beneficial for complex problem descriptions or multi-step reasoning.

Training Details

The model was trained using a Supervised Fine-Tuning (SFT) approach. The training process utilized specific versions of popular machine learning frameworks, including TRL 1.1.0.dev0, Transformers 4.57.6, Pytorch 2.9.0, Datasets 5.0.0, and Tokenizers 0.22.2. Further details on the training run can be visualized via Weights & Biases.

Ideal Use Cases

This model is well-suited for applications requiring strong mathematical problem-solving abilities, such as:

  • Educational tools for math assistance.
  • Automated problem solvers for arithmetic, algebra, and other mathematical concepts.
  • Research and development in AI for mathematical reasoning.

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