What are the hardware requirements for Llama 2 7B Chat Hf?

Llama 2 7B Chat Hf requires approximately 13.5 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 Chat Hf and how large is it?

Llama 2 7B Chat Hf is developed by meta-llama, 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 Chat Hf perform on standard benchmarks?

Llama 2 7B Chat Hf has the following published scores: MMLU 48.32, LMSYS ELO 1052. Compare against reference models on this page or on the LLM Explorer leaderboards.

Llama 2 7B Chat Hf by meta-llama — VRAM 13.5GB, 4K context

Name: Llama 2 7B Chat Hf
Author: meta-llama

Llama 2 7B Chat Hf is an open-source language model by meta-llama. Features: 7b LLM, VRAM: 13.5GB, Context: 4K, License: llama2, LLM Explorer Score: 0.4, ELO: 1052, Arc: 52.9, HellaSwag: 78.6, MMLU: 48.3, GSM8K: 7.4.

Arxiv:2307.09288 Conversational Deploy:sagemaker En Endpoints compatible Facebook Llama Llama2 Meta Pytorch Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf

Llama 2 7B Chat Hf Benchmarks

LMSys ELO 1052 vs 1272 (so35) -17.3%

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Llama 2 7B Chat Hf Parameters and Internals

Model Type

Generative Text Model, Text Generation, Chatbot

Use Cases

Areas:

Commercial, Research

Applications:

Assistant-like chat, Natural language generation tasks

Limitations:

Use in languages other than English., Any manner violating applicable laws.

Additional Notes

Future versions of tuned models will be released to improve model safety.

Supported Languages

English (Primary)

Training Details

Data Sources:

publicly available online data

Data Volume:

2 trillion tokens

Methodology:

Supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF)

Context Length:

4000

Hardware Used:

Meta's Research Super Cluster, third-party cloud compute

Model Architecture:

Auto-regressive language model that uses an optimized transformer architecture

Input Output

Input Format:

text-only

Accepted Modalities:

text

Output Format:

text-only

Performance Tips:

Use specific formatting for chat versions, including special tokens and formatting.

LLM Name	Llama 2 7B Chat Hf
Repository 🤗	https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
Model Size	7b
Required VRAM	13.5 GB
Updated	2026-08-08
Maintainer	meta-llama
Model Type	llama
Model Files	10.0 GB: 1-of-2 3.5 GB: 2-of-2 10.0 GB: 1-of-2 3.5 GB: 2-of-2
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	llama2
Context Length	4096
Model Max Length	4096
Transformers Version	4.32.0.dev0
Tokenizer Class	LlamaTokenizer
Beginning of Sentence Token	<s>
End of Sentence Token	</s>
Unk Token	<unk>
Vocabulary Size	32000
Torch Data Type	float16

Rank the Llama 2 7B Chat Hf 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

Quantized Models of the Llama 2 7B Chat Hf

Model	Likes	Downloads	VRAM
Llama 2 7B Chat GGUF	518	41673	2 GB
Llama 2 7B Chat GPTQ	267	26543	3 GB
Llama 2 7B Chat Hf GGUF	0	308	2 GB
Llama 2 7B Chat GGML	872	157	2 GB
Llama 2 7B Chat AWQ	24	2061	3 GB
OpenHathi 7B Hi V0.1 Base Gptq	3	16	4 GB

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