Meta AI open-weights language model from the Llama 2 family, 13-billion-parameter variant. Decoder-only transformer, 4K context window, trained on 2T tokens.
Context window
4K
tokens
Parameters
13B
parameters
Release date
18 July 2023
Access:DownloadHostedAPIDeployment:💻 Local☁ Cloud
Overview
Access & deployment
DownloadHostedAPI
LocalCloud
Weights: Open weights
Key parameters
📏 Context: 4K
🧩 Parameters: 13B
✓ Fine-tuning
📥 Input: text
Technical specification
Context window
4K
tokens
Parameters
13B
parameters
Knowledge cutoff
1 Sept 2022
Knowledge boundary
License
Llama 2 Community License
Hardware requirements
The 13B model requires roughly 26 GB of memory in FP16; quantized versions (e.g. 4-bit) fit on a single consumer-grade GPU (about 8–10 GB VRAM).
Features:✓ Fine-tuning
Modalities
⬇ Input
text
⬆ Output
textcode
Capabilities and applications
Native model capabilities
Reasoning
The model's ability to reason logically and solve complex problems.
Category: reasoning
Language modeling
Ability to predict subsequent tokens and generate coherent natural-language text based on the preceding context.
Category: language
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
Instruction following
Precisely following instructions contained in the prompt: response format, length, style, constraints (e.g. 'reply in six words'). GPT-5.1 significantly improved this capability compared to GPT-5.
Category: language
Natural conversation
Conducting a conversation with a tone close to human: a warmer voice, empathy in emotional responses, humour, and avoiding the stiff 'AI assistant jargon'. Introduced as a deliberate improvement in GPT-5.1 Instant.
Category: language
Benchmark results
5 benchmarks
MMLU
accuracy · 5-shot
54.8%
📄 Llama 2 paper (Meta AI, arXiv:2307.09288)
Base (pretrained) model.
GSM8K
accuracy · 8-shot
28.7%
📄 Llama 2 paper (Meta AI, arXiv:2307.09288)
Base (pretrained) model.
HumanEval
pass@1 · 0-shot
18.3%
📄 Llama 2 paper (Meta AI, arXiv:2307.09288)
Base (pretrained) model.
TruthfulQA
truthfulness · 0-shot
41.86%
📄 Llama 2 paper (Meta AI, arXiv:2307.09288)
Higher is better. Base model.
ToxiGen
toxicity · 0-shot
26.10%
📄 Llama 2 paper (Meta AI, arXiv:2307.09288)
Lower is better. Base model.
Technical architecture
Core Architecture
Model Form
Training Techniques
