What are the hardware requirements for Llama 2 7B GPTQ?

Llama 2 7B GPTQ requires approximately 3.9 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama 2 7B GPTQ and how large is it?

Llama 2 7B GPTQ 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.

How does Llama 2 7B GPTQ perform on standard benchmarks?

Llama 2 7B GPTQ has the following published scores: MMLU 43.99. Compare against reference models on this page or on the LLM Explorer leaderboards.

Llama 2 7B GPTQ by TheBloke — VRAM 3.9GB, 4K context

Name: Llama 2 7B GPTQ
Author: TheBloke

Llama 2 7B GPTQ is an open-source language model by TheBloke. Features: 7b LLM, VRAM: 3.9GB, Context: 4K, License: llama2, Quantized, LLM Explorer Score: 0.13, Arc: 52.1, HellaSwag: 77.6, MMLU: 44, GSM8K: 5.

Arxiv:2307.09288 4-bit Base model:meta-llama/llama-2-... Base model:quantized:meta-llam... En Facebook Gptq Llama Llama2 Meta Pytorch Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/TheBloke/Llama-2-7B-GPTQ

Llama 2 7B GPTQ Parameters and Internals

Model Type

llama

Use Cases

Primary Use Cases:

Natural language generation tasks., Dialogue use cases for fine-tuned variants.

Limitations:

Tested primarily in English; may not work predictably for other languages., Potential for inaccurate or biased outputs.

Considerations:

Follow Meta’s Responsible Use Guide.

Additional Notes

The model's fine-tuned variants (Llama-2-Chat) are optimized for dialogue applications and exhibit improved safety and helpfulness criteria.

Training Details

Data Volume:

2.0 trillion tokens

Methodology:

Pretraining on publicly available online data and fine-tuning with supervision and reinforcement learning with human feedback.

Context Length:

4096

Training Time:

January 2023 to July 2023

Hardware Used:

Meta's Research Super Cluster, A100-80GB GPUs

Model Architecture:

Auto-regressive language model using an optimized transformer design.

Responsible Ai Considerations

Mitigation Strategies:

Perform safety testing and tuning specific to applications.

LLM Name	Llama 2 7B GPTQ
Repository 🤗	https://huggingface.co/TheBloke/Llama-2-7B-GPTQ
Model Name	Llama 2 7B
Model Creator	Meta
Base Model(s)	Llama 2 7B Hf meta-llama/Llama-2-7b-hf
Model Size	7b
Required VRAM	3.9 GB
Updated	2026-07-30
Maintainer	TheBloke
Model Type	llama
Model Files	3.9 GB
Supported Languages	en
GPTQ Quantization	Yes
Quantization Type	gptq
Model Architecture	LlamaForCausalLM
License	llama2
Context Length	4096
Model Max Length	4096
Transformers Version	4.30.2
Tokenizer Class	LlamaTokenizer
Beginning of Sentence Token	<s>
End of Sentence Token	</s>
Unk Token	<unk>
Vocabulary Size	32000
Torch Data Type	float16

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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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