doupari/llama3.1_8b_sft-llopa-k28-no_system-nemotron-math-high.math.q60000-llopa-k28-no_system

TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kPublished:Apr 28, 2026Architecture:Transformer Cold

doupari/llama3.1_8b_sft-llopa-k28-no_system-nemotron-math-high.math.q60000-llopa-k28-no_system is an 8 billion parameter language model based on the Llama 3.1 architecture, featuring a 32,768 token context length. This model is a merged Hugging Face Transformers checkpoint, converted from a local downstream PEFT-style training checkpoint. It is specifically fine-tuned for mathematical reasoning and high-precision numerical tasks, leveraging Nemotron-Math training data. Its primary strength lies in advanced mathematical problem-solving and quantitative analysis.

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Model Overview

doupari/llama3.1_8b_sft-llopa-k28-no_system-nemotron-math-high.math.q60000-llopa-k28-no_system is an 8 billion parameter language model built upon the Llama 3.1 architecture, offering a substantial context window of 32,768 tokens. This model represents a merged Hugging Face Transformers checkpoint, originating from a local downstream PEFT-style training process.

Key Capabilities

  • Advanced Mathematical Reasoning: The model is specifically fine-tuned using Nemotron-Math training data, indicating a strong focus on mathematical problem-solving and numerical accuracy.
  • High-Precision Tasks: Its training regimen suggests optimization for tasks requiring high precision, particularly within quantitative domains.
  • Llama 3.1 Foundation: Benefits from the robust base capabilities and architectural advancements of the Llama 3.1 series.
  • Extended Context: A 32,768 token context length allows for processing and understanding longer, more complex mathematical problems or detailed technical documents.

Good For

  • Mathematical Problem Solving: Ideal for applications requiring the solution of complex equations, proofs, or quantitative analysis.
  • Scientific Computing Support: Can assist in tasks related to scientific research, data analysis, and engineering calculations.
  • Technical Content Generation: Suitable for generating or understanding content with a strong mathematical or logical component.