What are the hardware requirements for TinyJLLM?

TinyJLLM requires approximately 0.4 GB of VRAM and supports a context window of 0.5K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed TinyJLLM and how large is it?

TinyJLLM is developed by jaweed123, a model with 102.5m 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 TinyJLLM?

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

TinyJLLM by jaweed123 — VRAM 0.4GB, 0.5K context

Name: TinyJLLM
Author: jaweed123

TinyJLLM is an open-source language model by jaweed123. Features: 102.5m LLM, VRAM: 0.4GB, Context: 0.5K, License: mit, LLM Explorer Score: 0.32.

Base model:jaweed123/tinyjllm Base model:quantized:jaweed123... Bpe En Endpoints compatible Fineweb From-scratch Gguf Learnllm Llama Pytorch Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/jaweed123/TinyJLLM

TinyJLLM Parameters and Internals

LLM Name	TinyJLLM
Repository 🤗	https://huggingface.co/jaweed123/TinyJLLM
Base Model(s)	TinyJLLM jaweed123/TinyJLLM
Model Size	102.5m
Required VRAM	0.4 GB
Updated	2026-08-31
Maintainer	jaweed123
Model Type	llama
Model Files	0.4 GB
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	mit
Context Length	512
Model Max Length	512
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<pad>
Vocabulary Size	32000
Torch Data Type	bfloat16

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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