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Llama 2 70B

Llama 2 70B

70B · Family: Llama
Meta AI open large language model with about 70B parameters, released July 2023 under the Llama 2 Community License. 4K context window, trained on 2T tokens.
✓ Active✓ Public access⚖ Open weightsLLM📁 Llama
Context window
4K
tokens
Parameters
70B
parameters
Release date
18 July 2023
Access:DownloadHostedAPIDeployment:💻 Local☁ Cloud

Overview

Llama 2 70B is an open large language model (LLM) developed by Meta AI — the largest variant of the Llama 2 family, with about 70 billion parameters. It was released on 18 July 2023 alongside the smaller 7B and 13B variants.

The model uses a Transformer decoder architecture with RMSNorm pre-normalization, the SwiGLU activation function and rotary positional embeddings (RoPE). The 34B and 70B variants additionally use Grouped-Query Attention (GQA) for improved inference scalability. The context window is 4096 tokens, and the model was pretrained on 2 trillion tokens of publicly available data.

The Llama 2-Chat 70B variant, optimized for dialogue, was fine-tuned using supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). The model weights are available for download under the Llama 2 Community License, which permits commercial use with restrictions for services exceeding 700 million monthly active users.

Classification
LLM
Family: Llama
Access & deployment
DownloadHostedAPI
LocalCloud
Weights: Open weights
Key parameters
📏 Context: 4K
🧩 Parameters: 70B
✓ Fine-tuning
📥 Input: text

Technical specification

Context window
4K
tokens
Parameters
70B
parameters
Knowledge cutoff
1 Sept 2022
Knowledge boundary
License
Llama 2 Community License
Hardware requirements
The FP16 variant requires roughly 140 GB of memory (e.g. two 80 GB GPUs). Running on a single GPU is possible after quantization (e.g. 4-bit).
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

3 benchmarks
MMLU
accuracy · 5-shot
68.9%
📄 paper
Llama 2 70B base model.
HumanEval
pass@1 · 0-shot
29.9%
📄 paper
Llama 2 70B base model.
TriviaQA
exact match · 1-shot
85.0%
📄 paper
Llama 2 70B base model.

Technical architecture