What are the hardware requirements for Smollm Fw Darija?

Smollm Fw Darija requires approximately 0.5 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Smollm Fw Darija and how large is it?

Smollm Fw Darija is developed by sawalni-ai, 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 Fw Darija?

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

Smollm Fw Darija by sawalni-ai — VRAM 0.5GB, 2K context

Name: Smollm Fw Darija
Author: sawalni-ai

Smollm Fw Darija is an open-source language model by sawalni-ai. Features: 135m LLM, VRAM: 0.5GB, Context: 2K, LLM Explorer Score: 0.15.

Ar Base model:finetune:huggingfac... Base model:huggingfacetb/smoll... Dataset:sawalni-ai/fw-darija Llama Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/sawalni-ai/smollm-fw-darija

Smollm Fw Darija Parameters and Internals

LLM Name	Smollm Fw Darija
Repository 🤗	https://huggingface.co/sawalni-ai/smollm-fw-darija
Base Model(s)	SmolLM 135M HuggingFaceTB/SmolLM-135M
Model Size	135m
Required VRAM	0.5 GB
Updated	2026-07-23
Maintainer	sawalni-ai
Model Type	llama
Model Files	0.5 GB
Supported Languages	ar
Model Architecture	LlamaForCausalLM
Context Length	2048
Model Max Length	2048
Transformers Version	4.46.3
Tokenizer Class	GPT2Tokenizer
Vocabulary Size	49152
Torch Data Type	float32

Rank the Smollm Fw Darija capabilities

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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

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Note: green Score (e.g. "73.2") means that the model is better than sawalni-ai/smollm-fw-darija.