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unsloth-long-context

Train large language models on sequences exceeding 32K tokens with optimized RoPE scaling and Triton kernels.

Updated Aug 27, 2026
Search Tags:#memory efficiency#unsloth#long context#llm training#rope scaling#triton kernels
One-click install
npx skills add https://github.com/cuba6112/skillfactory --skill unsloth-long-context
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth-long-context
Source: https://github.com/cuba6112/skillfactory/tree/main/skills/unsloth-long-context
Command: npx skills add https://github.com/cuba6112/skillfactory --skill unsloth-long-context

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires unsloth, triton, torch, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of training large language models on extremely long text sequences, which often leads to Out-of-Memory (OOM) errors and performance degradation.

Core Features & Use Cases

  • Extended Context Training: Enables training models with context lengths of 89K+ tokens on high-VRAM GPUs.
  • Memory Optimization: Achieves significant memory savings (30%+) compared to standard libraries like Flash Attention 2.
  • Use Case: Training a model to summarize entire books or analyze lengthy codebases where standard context windows are insufficient.

Quick Start

Initialize a model for training with a maximum sequence length of 65536 tokens.

Frequently Asked Questions about unsloth-long-context

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I train LLMs on long context lengths without running out of GPU memory?▼

Train LLMs on long context lengths without OOM errors by optimizing RoPE scaling and using memory-efficient custom Triton attention kernels, achieving over 30% memory savings compared to standard libraries.

Does Flash Attention 2 support training models with context lengths exceeding 32K tokens?▼

While Flash Attention 2 processes extended sequences, this approach utilizes custom Triton kernels and specialized gradient checkpointing to handle sequences exceeding 32K tokens more memory-efficiently.

How do I configure RoPE scaling for training on an 89K token context window?▼

Configure RoPE scaling for an 89K token context window by initializing your model with a specified maximum sequence length and leveraging custom Triton kernels for stable training.

What is the best way to fine-tune models to analyze lengthy codebases or entire books?▼

The best way to fine-tune models for lengthy codebases or entire books is using extended context training, which enables processing of 89K+ tokens on high-VRAM GPUs through specialized gradient checkpointing.

What are the GPU requirements for training models with a 65536 token sequence length?▼

Training models with a 65536 token sequence length requires high-VRAM GPUs to handle the extended context window, utilizing optimized RoPE scaling and memory-efficient attention to prevent OOM errors.

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