What are the hardware requirements for SmallLM?

SmallLM requires a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed SmallLM and how large is it?

SmallLM is developed by XsoraS, a model with 14m parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate SmallLM?

SmallLM is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

SmallLM by XsoraS — 2K context

Name: SmallLM
Author: XsoraS

SmallLM is an open-source language model by XsoraS. Features: 14m LLM, Context: 2K, LLM Explorer Score: 0.12.

Arxiv:1910.09700 Endpoints compatible Gpt neox Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/XsoraS/SmallLM

SmallLM Parameters and Internals

LLM Name	SmallLM
Repository 🤗	https://huggingface.co/XsoraS/SmallLM
Model Size	14m
Required VRAM	0 GB
Updated	2026-06-02
Maintainer	XsoraS
Model Type	gpt_neox
Model Files	0.0 GB
Model Architecture	GPTNeoXForCausalLM
Context Length	2048
Model Max Length	2048
Transformers Version	4.39.3
Tokenizer Class	GPTNeoXTokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	50304
Torch Data Type	float16

Rank the SmallLM capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to SmallLM

Best Alternatives	Context / RAM	Downloads	Likes
Pythia 14M	2K / 0 GB	239461	6
Pythia 14m Sft	2K / 0.1 GB	5	0
...ythia 14M Finewebedu Sentences	2K / 0.1 GB	5	0
...t Llm Pythia 14M Pm Gen Ian Nd	2K / 0.1 GB	6	0
Pythia 14M	2K / 0 GB	7	0
Tinyllama Random	2K / 0.1 GB	7	0

Note: green Score (e.g. "73.2") means that the model is better than XsoraS/SmallLM.