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Reducing LLM memory with bitsandbytes v1.0.0

Use this resource to load an LLM in 8 or 4 bits to save GPU memory

New

  • 369 downloads
  • 174 likes
  • 214 saves

Free

Install with the CLI

In a terminal, from the folder you want:

npx --yes palatekit@latest install bitsandbytes-8bit-4bit-quantization

With no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.

First time: connect the CLI with

npx --yes palatekit@latest login

The agent guides you through installing bitsandbytes with Transformers, loading a model in 8 or 4 bits, then checking memory usage. It also covers QLoRA fine-tuning and 8-bit optimizers.

Mostly picked by

Updated November 2, 2026

Card for the “Reducing LLM memory with bitsandbytes” skill

Details

Works in
Install guides
Generates with
Your agent's model
Licence
MIT

Similar resources

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What you get

  • SKILL.md: installation, 8-bit and 4-bit loading and memory check
  • references/qlora-training.md, memory-optimization.md and quantization-formats.md: QLoRA training, memory optimization and formats

Good fit for

  • Your GPU lacks the memory to load a model
  • You prepare a QLoRA fine-tune

Not for

    Tags

    • quantization
    • memory
    • qlora
    • transformers

    About

    You say

    “Load a Llama 2 7B model in 4 bits on my 8 GB card and check the memory used.”

    Card for the “Reducing LLM memory with bitsandbytes” skill

    Skill published by Orchestra Research under the MIT license, delivered in its original version.

    Show more

    How to use it

    1. Install the skill in your agent's skills folder
    2. Ask your agent to load an LLM in 8 or 4 bits to save GPU memory
    3. Review and adjust the result

    Content preview

    Files

    • LICENSE1.1 KB
    • NOTICE-PALATEKIT.md0.5 KB
    • references/memory-optimization.md12.9 KB
    • references/qlora-training.md12.2 KB
    • references/quantization-formats.md10.5 KB
    • SKILL.md10.3 KB

    Origin and licence

    Listed unmodified

    Author
    Orchestra Research
    Source
    github.com/orchestra-research/ai-research-skills/tree/773a52944ba4747a18bd4ae9ade53fff041adcbc/10-optimization/bitsandbytes
    Licence
    MIT

    Notice

    « quantizing-models-bitsandbytes » (Orchestra Research, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.

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