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Llama 2 7B

Llama 2 7B

7B · Family: Llama
Meta's base, pretrained 7-billion-parameter language model from the Llama 2 family. Non-fine-tuned variant without RLHF, intended for further fine-tuning.
✓ Active✓ Public access⚖ Open weightsLLM📁 Llama
Context window
4K
tokens
Parameters
7B
parameters
Release date
18 July 2023
Access:DownloadDeployment:💻 Local☁ Cloud

Overview

Llama 2 7B is the base (pretrained) language model from Meta's Llama 2 family, released on 18 July 2023. It is the smallest variant in the family, with 7 billion parameters, trained on 2 trillion tokens of publicly available data. The model uses an auto-regressive decoder-only transformer architecture with RMSNorm, RoPE positional encoding and the SwiGLU activation. The 7B variant uses standard multi-head attention (MHA) — grouped-query attention (GQA) is only used in the larger 34B and 70B variants.

This is the base variant, without instruction fine-tuning (SFT) or reinforcement learning from human feedback (RLHF) — those techniques were applied only to the Llama 2 Chat versions. The base model is intended primarily for text completion and as a starting point for custom fine-tuning. The context window is 4096 tokens and the knowledge cutoff is September 2022. Weights are distributed under the Llama 2 Community License (open weights, commercial use permitted with restrictions).

Classification
LLM
Family: Llama
Access & deployment
Download
LocalCloud
Weights: Open weights
Key parameters
📏 Context: 4K
🧩 Parameters: 7B
✓ Fine-tuning
📥 Input: text

Technical specification

Context window
4K
tokens
Parameters
7B
parameters
Knowledge cutoff
1 Sept 2022
Knowledge boundary
License
Llama 2 Community License
Hardware requirements
In FP16 precision the model requires roughly 14 GB of memory, allowing it to run on a single GPU (e.g. 16 GB). After 4-bit quantization the requirement drops to about 4-5 GB.
Features:✓ Fine-tuning
Modalities
⬇ Input
text
⬆ Output
textcode

Capabilities and applications

Native model capabilities
Language modeling
Ability to predict subsequent tokens and generate coherent natural-language text based on the preceding context.
Category: language
Reasoning
The model's ability to reason logically and solve complex problems.
Category: reasoning
Coding
Generating, analysing and modifying code in many programming languages. Covers writing functions, debugging, refactoring, code review, and creating tests. Measured by benchmarks such as HumanEval and SWE-bench.
Category: coding

Benchmark results

3 benchmarks
MMLU
accuracy · 5-shot
45.3%
📄 Llama 2 paper (arXiv:2307.09288)
HumanEval
pass@1 · 0-shot
12.8%
📄 Llama 2 paper (arXiv:2307.09288)
GSM8K
accuracy · 8-shot
14.6%
📄 Llama 2 paper (arXiv:2307.09288)

Technical architecture

Model Form
Training Techniques