What are the hardware requirements for Yarn Llama 2 7B 64K GGUF?

Yarn Llama 2 7B 64K GGUF requires approximately 2.8 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Yarn Llama 2 7B 64K GGUF and how large is it?

Yarn Llama 2 7B 64K GGUF is developed by TheBloke, 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 Yarn Llama 2 7B 64K GGUF?

Yarn Llama 2 7B 64K GGUF is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Yarn Llama 2 7B 64K GGUF by TheBloke — VRAM 2.8GB

Name: Yarn Llama 2 7B 64K GGUF
Author: TheBloke

Yarn Llama 2 7B 64K GGUF is an open-source language model by TheBloke. Features: 7b LLM, VRAM: 2.8GB, License: llama2, Quantized, LLM Explorer Score: 0.09.

Arxiv:2309.00071 Base model:nousresearch/yarn-l... Base model:quantized:nousresea... Dataset:pg19 Gguf Llama Quantized Region:us Yarn

Model Card on HF 🤗: https://huggingface.co/TheBloke/Yarn-Llama-2-7B-64K-GGUF

Yarn Llama 2 7B 64K GGUF Parameters and Internals

Model Type

llama

Additional Notes

Further pretrained on long context data for 400 steps.

Training Details

Data Sources:

PG19

Context Length:

64000

Model Architecture:

Flash Attention 2 patched

Input Output

Input Format:

{prompt}

LLM Name	Yarn Llama 2 7B 64K GGUF
Repository 🤗	https://huggingface.co/TheBloke/Yarn-Llama-2-7B-64K-GGUF
Model Name	Yarn Llama 2 7B 64K
Model Creator	NousResearch
Base Model(s)	Yarn Llama 2 7B 64K NousResearch/Yarn-Llama-2-7b-64k
Model Size	7b
Required VRAM	2.8 GB
Updated	2026-07-16
Maintainer	TheBloke
Model Type	llama
Model Files	2.8 GB 3.6 GB 3.3 GB 3.0 GB 3.8 GB 4.1 GB 3.9 GB 4.7 GB 4.8 GB 4.7 GB 5.5 GB 7.2 GB
GGUF Quantization	Yes
Quantization Type	gguf
Model Architecture	AutoModel
License	llama2

Rank the Yarn Llama 2 7B 64K GGUF 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

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/Yarn-Llama-2-7B-64K-GGUF.