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Practical guides to fine-tuning, distillation, and deploying small language models.

FunctionGemma 270M for Multi-Turn Tool Calling

Google's FunctionGemma scores 9.9-38.8% on multi-turn tool calling before training and 90.9-96.7% after, and the gap between those two numbers is the whole story of using a 270M function caller.

Gemma 3 270M: What It Can Do

The capability envelope of Google's 270M-parameter Gemma 3 — which tasks it clears after fine-tuning, which it cannot reach at any training budget, and why its benchmark variance is so wide.

Gemma 4 E2B and E4B Explained

The E in Gemma 4 E2B and E4B stands for effective parameters, which is smaller than the total — here is what that means for memory planning, download size, and picking between the two.

Liquid LFM2.5 350M for Tool Calling

Liquid AI's LFM2.5-350M reaches 96-98% tool call equivalence after fine-tuning on three multi-turn benchmarks, matching a 120B teacher — here is the data, the architecture behind it, and where the model still fails.

Multi-Turn Tool Calling Explained

Multi-turn tool calling trains a model to emit the next function call given a whole conversation, not a single request — the data format, the turn-expansion setting, and why per-call accuracy compounds.

Qwen3 1.7B Fine-Tuning Guide

Specs, benchmark rank, failure modes, and configuration for fine-tuning Qwen3-1.7B — the default recommendation in the distil labs catalog when you have no reason to pick anything else.

Qwen3.5 2B Fine-Tuning Guide

What changed between Qwen3 and Qwen3.5 at the 2B tier, what a 262K context window is and is not good for, and why no published distil labs benchmark covers this model yet.

The Six Task Types for Training a Small Language Model

The six task types distil labs supports — classification, question answering, open-book QA, closed-book QA, tool calling, multi-turn tool calling — and what each one changes about your data, your metric, and your model choice.

SmolLM2 135M: The Smallest Useful Student

At 135M parameters SmolLM2 is the floor of the distil labs catalog — what a model this size can still learn, the three hard constraints that come with it, and when picking it is the right call.