Check if a model is fine-tunable

We're starting a new chapter on fine-tuning.

A trained LoRA adapter only helps if we can stack it on a base model that supports fine-tuning. The QVAC SDK accepts Q4_K_M for inference but rejects that quantization for training. We'd see the error halfway through a multi-hour run.

getModelInfo({ name }) returns the catalog model's quantization string. Two things to know before calling it:

1. The catalog constant (QWEN3_600M_INST_Q4) is an object whose .name field is the string. Pass QWEN3_600M_INST_Q4.name.
2. The SDK returns the quantization in lowercase and drops the _0 suffix, so the 600M Q4 model comes back as "q4" instead of "Q4_0".

Pick the base model first, call getModelInfo to confirm, then start the trainer. The check looks like this:

const modelId = await loadModel({ modelSrc: QWEN3_600M_INST_Q4 });
const info = await getModelInfo({ name: QWEN3_600M_INST_Q4.name });

console.log("Quantization:", info.quantization);

const quantization = info.quantization.toUpperCase().replace(/^Q(\d)$/, "Q$1_0");
const fineTunable = ["F32", "F16", "Q4_0", "Q8_0", "TQ1_0", "TQ2_0"].includes(quantization);

console.log("Fine-tunable:", fineTunable ? "yes" : "no");

Swap QWEN3_600M_INST_Q4 for a Q4_K_M constant and the second line flips to no. Pick a fine-tunable model before you start training.

Note: the allowlist covers the quantizations the trainer knows how to update. Other quantizations might work someday but aren't supported in this version of the SDK.

Put it to the test

1. Call getModelInfo({ name: QWEN3_600M_INST_Q4.name }) and read info.quantization into a local variable.
2. Normalize the string (uppercase + restore _0 suffix) and check it against the allowlist ["F32", "F16", "Q4_0", "Q8_0", "TQ1_0", "TQ2_0"]. Log the verdict.