SmolLM2-360M-Instruct quantized with GLQ using the QTIP-style trellis (TCQ) codebook at 4 bits/weight, lookup-free 3INST variant, uniform rate across all layers.

All four rungs were quantized, smoke-tested and measured in one session on one RTX PRO 6000 Blackwell (vLLM 0.27.1, glq 0.8.8, full CUDA graphs, 256-token decode, chat-template sample):

TTFT was 16 ms at B=1 on every rung. The lower rungs decode faster (less memory traffic; 5-6 bpw also pay a second stacked-RVQ stage). Every rung produced coherent chat-template output in the smoke test.

Perplexity is teacher-forced wikitext-2 (seqlen 2048, 128 non-overlapping windows), measured through vLLM, with the bf16 reference run the same way in the same session — so the deltas are internally comparable, but do not mix these values into HF-measured PPL series. 6 bpw's +0.02 is within the measurement's character for bf16 itself; 3 bpw pays a real ~11%. No task evals were run at this model size — this table is the trade-off: footprint and speed against perplexity.

(trellis and 3inst are the defaults since glq 0.8.8; kept explicit for reproducibility.)

Validated on vLLM with glq >= 0.8.8 — the numbers above. The HF transformers path is untested for this checkpoint.

SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device. More details in our paper: https://arxiv.org/abs/2502.02737

SmolLM2 demonstrates significant advances over its predecessor SmolLM1, particularly in instruction following, knowledge, reasoning. The 360M model was trained on 4 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new filtered datasets we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using UltraFeedback.

The instruct model additionally supports tasks such as text rewriting, summarization and function calling (for the 1.7B) thanks to datasets developed by Argilla such as Synth-APIGen-v0.1. You can find the SFT dataset here: https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk and finetuning code in the alignement handbook

For more details refer to: https://github.com/huggingface/smollm. You will find pre-training, post-training, evaluation and local inference code.

You can also use the TRL CLI to chat with the model from the terminal:

In this section, we report the evaluation results of SmolLM2. All evaluations are zero-shot unless stated otherwise, and we use lighteval to run them.

SmolLM2 models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content.