# Choosing a Base Model


📂 fine-tuning

## FINE-TUNING GUIDE

# Choosing a Base Model

Select from evidence on your workload, not a generic leaderboard. Start with the smallest checkpoint that meets your quality target, then verify its terms, training support, and deployment path before investing in a full run.

## Run a base-model bake-off first

### 1. Freeze the test set

Build representative success, failure, boundary, safety, and out-of-distribution cases. Keep them out of training and prompt iteration.

### 2. Compare baselines

Test the same rubric with prompting, few-shot examples, retrieval, and candidate base models. Fine-tuning should solve a measured gap.

### 3. Pilot cheaply

Run a short adapter experiment, record the full configuration, and promote only if the gain survives held-out and safety evaluation.

## Selection gate

- The untuned checkpoint beats a prompt-only and retrieval baseline on your held-out cases.

- The license and acceptable-use terms cover your product, geography, distribution, and derivatives.

- The tokenizer, chat template, context length, and output format match the intended serving stack.

- A small pilot fits with enough headroom for activations, optimizer state, batches, and checkpoints.

- The serving stack supports the adapter or merged checkpoint format you intend to ship.

- You can test task quality, safety regressions, latency, throughput, and cost before promotion.

## Illustrative open-weight families

Snapshot reviewed August 2026. This is a starting set, not a ranking or a promise that every checkpoint supports every tuning method. Follow the official source for current files and terms.

### Gemma

A Google-supported open-weight family with documented local and cloud tuning paths.

Check: Choose the exact checkpoint and format only after checking the current Gemma terms, framework guide, and memory requirements.

### Qwen

A broad open-weight family worth evaluating for multilingual, coding, and multimodal workloads.

Check: The collection contains models with very different architectures and sizes; verify each model card and chat template.

### Mistral

Open-weight models with official deployment and, for selected checkpoints, fine-tuning guidance.

Check: Some recent checkpoints are mixture-of-experts models whose total weight footprint is much larger than their active parameter count.

### gpt-oss

OpenAI open-weight reasoning models with an official model card and fine-tuning notes.

Check: Preserve the required Harmony response format and benchmark memory on the exact training stack before selecting a checkpoint.

### Nemotron

NVIDIA’s open-weight family aimed at agent and tool-use workloads, in tiers from compact to very large mixture-of-experts checkpoints, with data and recipes published for parts of the family.

Check: Tiers differ in license and architecture; verify the full weight footprint of a mixture-of-experts checkpoint and your framework’s support for it before piloting.

### GLiNER

Small encoder models for extraction, classification, and PII detection that train quickly and serve cheaply; tuned task models of this class can match far larger generalists on narrow tasks.

Check: These are schema-driven task models, not chat models; confirm the task fits the interface and beat a generalist baseline on your own labels before committing.

## Measure memory

Parameter count alone does not predict training memory. Precision, sequence length, attention implementation, optimizer, batch size, adapters, and sharding all matter. The costliest boundary is the single accelerator: once a pilot no longer fits one device even with adapters and quantization, training becomes a distributed-systems project.

## Read the exact license

“Open weight” is not a license category: some releases publish weights, data, and training code under permissive terms, while others ship weights alone with revenue or field-of-use conditions. Review the checkpoint’s terms and notices; do not infer rights from the model family name.

## Re-run safety tests

A tuned model can regress outside the target task. Test base capabilities, abuse cases, privacy leakage, and refusal behavior before each release.

Never purchase hardware from a generic VRAM estimate. Reproduce a representative pilot with the exact checkpoint, precision, sequence length, and framework, or obtain a current provider quote that includes training, storage, serving, and data-transfer costs.

## Fine-Tuning Guide

## Fine-Tuning Guide