Qwen 2.5 1.5B

Small but more capable than 0.5B

The 1.5B model is the first Qwen2.5 size where users often expect a more useful general assistant while still staying in a very lightweight VRAM class.

Coding and math uplift

Qwen still frames the family around stronger coding and mathematics than Qwen2, which matters more here because 1.5B is often used as a practical small local model.

Structured-output support

JSON and structured-data handling remain part of the product story rather than being reserved only for the larger checkpoints.

Family release

Qwen2.5 was released as a broad language-model line spanning base and instruction-tuned checkpoints from 0.5B to 72B parameters.

Model architecture

The 1.5B instruct model is a causal language model built as a dense transformer with RoPE, SwiGLU, RMSNorm, attention QKV bias, and tied word embeddings.

1.5B model geometry

The checkpoint has 1.54B total parameters, 1.31B non-embedding parameters, 28 layers, 12 query heads, 2 KV heads, a 32,768-token context window, and up to 8,192 generated tokens.

Training stage

Qwen describes the release as a pretraining plus post-training model rather than a small instruction-only adaptation on top of an older base.

Small general assistant use

Useful when you want a more capable lightweight assistant model than 0.5B without moving all the way to 7B-class memory costs.

Structured outputs

A reasonable fit for lighter JSON, extraction, and formatting workflows on small hardware.

Small coding and math tasks

Good for modest technical and code-oriented tasks when a very small open model is required.