Gguf Quantization
CheckedReduces LLM file sizes & speeds up inference by quantizing GGuf models – making them more efficient to run and deploy.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
- 1
Run this in your project
terminal$ npx skills add davila7/claude-code-templates --skill gguf-quantization -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
davila7
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Install target
Live repository
Not pinned. Installing fetches whatever the repository holds at the time you run the command.
Automated safety checks
This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.
Declares what it does: passed
SKILL.md parses and declares a name and a description.
SKILL.md exists, its frontmatter parses, and it declares a name and a description.
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Makes no network calls.
Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.
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No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.
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No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.
Pinned to a commit: passed
Scanned davila7/claude-code-templates at 79182c51fb.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 79182c51fb
- Scanned
- 3 files, 1,210 lines
About this skill
What it does
This skill allows you to quantize large language models (LLMs) into the GGUF format. GGUF is a file format designed for efficient inference, particularly on CPU and lower-end hardware. Quantization reduces model size and memory footprint while maintaining acceptable performance.
When to use it
- Deploying LLMs on resource-constrained devices: Run models on Raspberry Pis, mobile phones, or other devices with limited RAM.
- Reducing inference latency: Smaller quantized models generally load faster and have quicker inference times.
- Sharing models more easily: GGUF files are often easier to distribute and share than larger original model formats.
- Experimenting with different quantization levels: Evaluate the trade-off between model size, speed, and accuracy by trying various quantization methods.
Key capabilities
- Converts LLMs into GGUF format.
- Supports various quantization methods (e.g., Q4_K_M, Q5_K_M).
- Reduces model file sizes significantly.
- Optimizes for CPU inference.
Example prompts
- "Quantize this Llama 2 model to a Q4_K_M GGUF format."
- "Convert the Mistral-7B model into GGUF using the lowest quantization level possible."
- “Create a GGUF version of this Falcon model, suitable for running on a Raspberry Pi.”
Tips & gotchas
- Ensure you have sufficient disk space to store both the original model and the quantized GGUF file.
- Lower quantization levels (e.g., Q2) will result in smaller files but may also lead to noticeable accuracy degradation.