What are the hardware requirements for Llama2SWA?

Llama2SWA requires approximately 2.5 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama2SWA and how large is it?

Llama2SWA is developed by Maxtimer97, a model with 1.2b 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 Llama2SWA?

Llama2SWA is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama2SWA by Maxtimer97 — VRAM 2.5GB, 128K context

Name: Llama2SWA
Author: Maxtimer97

Llama2SWA is an open-source language model by Maxtimer97. Features: 1.2b LLM, VRAM: 2.5GB, Context: 128K, LLM Explorer Score: 0.19.

Arxiv:1910.09700 Conversational Custom code Hymba Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/Maxtimer97/Llama2SWA

Llama2SWA Parameters and Internals

LLM Name	Llama2SWA
Repository 🤗	https://huggingface.co/Maxtimer97/Llama2SWA
Model Size	1.2b
Required VRAM	2.5 GB
Updated	2026-08-06
Maintainer	Maxtimer97
Model Type	hymba
Model Files	2.5 GB
Model Architecture	HymbaForCausalLM
Context Length	131072
Model Max Length	131072
Transformers Version	4.53.0
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	128256
Torch Data Type	bfloat16

Rank the Llama2SWA 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

Best Alternatives to Llama2SWA

Best Alternatives	Context / RAM	Downloads	Likes
Llama2SWA512	128K / 2.5 GB	5	0

Note: green Score (e.g. "73.2") means that the model is better than Maxtimer97/Llama2SWA.