How to pull a model with Q5_K_S quantization for higher quality

What this does

Downloads a model quantized with the higher-precision Q5_K_S format, preserving more of the original weights' fidelity than Q4 variants. After this guide the Q5_K_S model will be ready for inference tasks where output quality matters more than marginal storage savings.

Steps

1. Locate the Q5_K_S variant for the target model. Check the Ollama library or model documentation for :q5_k_s availability.

   ollama pull llama3.2:q5_k_s
   

   Expected output: Download progress bars followed by success.

2. Compare file size against the Q4_K_M variant. Q5_K_S files are approximately 1.5x the size of Q4_K_M.

   ollama list | grep -E "q5_k_s|q4_k_m"
   

   Expected output: Two rows showing different sizes for the two quantization formats.

3. Run a test prompt to verify quality. Q5_K_S should produce more nuanced outputs than Q4 variants.

   ollama run llama3.2:q5_k_s "Explain the concept of recursion in programming."
   

   Expected output: A detailed, coherent explanation with examples.

• Record the local run evidence. Save the exact command, runtime or package version, model name if applicable, and observed output so the result can be reproduced later.

Verification

ollama show llama3.2:q5_k_s | grep -i quant
# Expected: "quantization: q5_k_s" or equivalent in the metadata output

Common failures

• not found - Q5_K_S variant does not exist for this model; check available tags or fall back to Q5_K_M.
• out of memory - Q5_K_S requires more RAM than Q4 variants; verify system memory before loading (7B models need ~6 GB).
• only Q4 variants available - Some publishers release only Q4 quantizations; use Q4_K_M instead.
• slow inference - Higher precision requires more compute per token; expected trade-off for better quality.

Operator checkpoint

Before treating this as solved, write down the local runtime, model or package version, hardware/backend if relevant, and the verification output. This keeps the guide useful as a Will-It-Run style decision instead of a one-off command transcript.

Related guides

• How to pull a model with Q4_K_M quantization for balanced quality and size
• How to pull a model with Q8_0 quantization for maximum quality