Choosing a language model: large or small?

E5-Small is a smaller baseline; Large-Instruct scores higher in this Spanish MIRACL report. The Qwen results here are multilingual aggregates, not Spanish-specific scores.

When queries and documents are both Spanish; identify cross-language retrieval separately.

The 51.2-to-53.7 difference establishes neither final-answer quality nor coverage of every Spanish variety.

Treat Large and Large-Instruct as separate variants: Large scores higher on English MIRACL in this report. For a reranking step, use Qwen’s comparison with a common candidate pool.

When target-language evidence matters more than a multilingual average.

The Instruct label does not guarantee an improvement on every task.

Start the size comparison with 0.6B. Consider 4B and 8B when their task-specific gains justify a second-stage resource budget; 8B does not lead on every metric.

When relevant documents reach the candidate pool but rank poorly; candidate count is part of the design.

Reranking cannot recover a document absent from its input pool.

For an instruction-following assistant, distinguish repository editing from coding problems. StarCoder2-3B serves code completion/FIM; HumanEval does not measure chat or editing-agent quality.

Once the interface is known: editor completion, multi-file changes, or solving a programming problem.

MoE active parameters do not determine resident weight memory.

LFM is a small candidate for extraction and bounded workflows; MiniCPM and Granite also have reasoning and tool-use evidence. Actual weight size can differ from the rounded size in a model name.

When task scope, output format and memory constraints are defined.

A strong small-model score establishes neither lower latency nor laptop feasibility at maximum context.

Salamandra supplies direct evidence for several Spanish tasks. Keep it alongside general multilingual candidates; the available data does not identify a best Spanish chatbot.

When Spanish evidence must be distinguished from a general multilingual claim.

Language coverage is not evidence for every region or professional domain.

Use MassiveIntent and MTOP evidence for intent classification. An embedding model is one component of the classifier; the result also depends on the classifier and its training data.

For a defined set of labels; the published evidence supports an initial shortlist.

An embedding model or bare ParsBERT is not a ready-to-use chatbot.