What are the hardware requirements for Pure 7B 210726?

Pure 7B 210726 requires approximately 13.5 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 Pure 7B 210726 and how large is it?

Pure 7B 210726 is developed by breitburg, a model with 7b 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 Pure 7B 210726?

Pure 7B 210726 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Pure 7B 210726 by breitburg — VRAM 13.5GB, 2K context

Name: Pure 7B 210726
Author: breitburg

Pure 7B 210726 is an open-source language model by breitburg. Features: 7b LLM, VRAM: 13.5GB, Context: 2K, License: apache-2.0, LLM Explorer Score: 0.27.

Base model:finetune:huggyllama... Base model:huggyllama/llama-7b Conversational En Endpoints compatible Llama Region:us Safetensors Sharded Tensorflow Unsloth

Model Card on HF 🤗: https://huggingface.co/breitburg/pure-7b-210726

Pure 7B 210726 Parameters and Internals

LLM Name	Pure 7B 210726
Repository 🤗	https://huggingface.co/breitburg/pure-7b-210726
Base Model(s)	Llama 7B huggyllama/llama-7b
Model Size	7b
Required VRAM	13.5 GB
Updated	2026-08-03
Maintainer	breitburg
Model Type	llama
Model Files	10.0 GB: 1-of-2 3.5 GB: 2-of-2
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	2048
Model Max Length	2048
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	32002
Torch Data Type	bfloat16

Rank the Pure 7B 210726 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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