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Practical guides to fine-tuning, distillation, and deploying small language models.
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.
Hidden Costs of Running Your Own Models
The line items that never appear in a per-token comparison: idle GPU time, serving expertise, monitoring, retraining cadence, and the security work that self-hosting moves onto your side of the ledger.
Latency as a Cost
Treating response time as a line item rather than a quality metric, using measured brain-stage latencies from a voice assistant where a 0.6B student answered in ~40ms against a ~700ms cloud median.
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.