What are the hardware requirements for SmolLM?

SmolLM requires approximately 0.3 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed SmolLM and how large is it?

SmolLM is developed by Sujithanumala, a model with 135m 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 SmolLM?

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

SmolLM by Sujithanumala — VRAM 0.3GB, 8K context

Name: SmolLM
Author: Sujithanumala

SmolLM is an open-source language model by Sujithanumala. Features: 135m LLM, VRAM: 0.3GB, Context: 8K, LLM Explorer Score: 0.16.

Llama Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/Sujithanumala/SmolLM

SmolLM Parameters and Internals

LLM Name	SmolLM
Repository 🤗	https://huggingface.co/Sujithanumala/SmolLM
Model Size	135m
Required VRAM	0.3 GB
Updated	2025-12-25
Maintainer	Sujithanumala
Model Type	llama
Model Files	0.3 GB 0.0 GB
Model Architecture	LlamaForCausalLM
Context Length	8192
Model Max Length	8192
Transformers Version	4.48.3
Vocabulary Size	49152
Torch Data Type	float32

Rank the SmolLM 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 SmolLM

Best Alternatives	Context / RAM	Downloads	Likes
KrynexAI 1.4 150M Mobile Model	8K / 0.3 GB	1324	1
SmolLM2 135M Instruct	8K / 0.3 GB	120	1
SmolLM2 135M Instruct	8K / 0.3 GB	1857297	388
SmolLM2 135M	8K / 0.3 GB	1618289	217
SmolLM2 135M Sukshma	8K / GB	150	1
Digest Sft2	8K / 0.5 GB	178	0
SmolLM2 135M Reasoning 5K	8K / 0.3 GB	276	3
... Subtitle Postprocessor Merged	8K / 0.5 GB	16	1
Bori 2 135M Base	8K / 0 GB	10	0
Nuevamol 135M Base	8K / 0.3 GB	5	0

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