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.
Context window
4K
tokens
Parameters
7B
parameters
Release date
18 July 2023
Access:DownloadDeployment:💻 Local☁ Cloud
Overview
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
Core Architecture
Model Form
Training Techniques
