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Llama 3 8B Instruct

Llama 3 8B Instruct

3 (8B, Instruct) · Family: Llama
Meta's open-weights 8B-parameter instruction-tuned LLM aligned with SFT and RLHF. 8K context, 128K-token tokenizer, pretrained on 15T+ tokens.
✓ Active✓ Public access⚖ Open weightsLLM📁 Llama
Context window
8K
tokens
Parameters
8B
parameters
Release date
18 April 2024
Access:DownloadAPIHostedDeployment:💻 Local☁ Cloud📱 On-device

Overview

Llama 3 8B Instruct is Meta's instruction-tuned language model released on 18 April 2024. It belongs to the first Llama 3 generation and is an auto-regressive model built on an optimized Transformer architecture. The Instruct variant was tuned for dialogue using supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF).

The model has 8 billion parameters, supports an 8,192-token context window and uses Grouped Query Attention (GQA) together with a tokenizer that has a vocabulary of 128,000 tokens. It was pretrained on over 15 trillion tokens from publicly available sources, with a knowledge cutoff of March 2023.

The weights are distributed under the Meta Llama 3 Community License, allowing local execution, fine-tuning and commercial deployment within the license terms.

Classification
LLM
Family: Llama
Access & deployment
DownloadAPIHosted
LocalCloudOn-device
Weights: Open weights
Key parameters
📏 Context: 8K
🧩 Parameters: 8B
✓ Fine-tuning
📥 Input: text

Technical specification

Context window
8K
tokens
Parameters
8B
parameters
Knowledge cutoff
1 Mar 2023
Knowledge boundary
License
Meta Llama 3 Community License
Hardware requirements
The 8B variant needs about 16 GB of memory in FP16/BF16; with quantization (e.g. 4-bit) it can run on a single consumer GPU.
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
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
Reasoning
The model's ability to reason logically and solve complex problems.
Category: reasoning

Benchmark results

5 benchmarks
MMLU
accuracy · 5-shot
68.4%
📄 Hugging Face model card
GPQA
accuracy · 0-shot
34.2%
📄 Hugging Face model card
HumanEval
pass@1 · 0-shot
62.2%
📄 Hugging Face model card
GSM8K
accuracy · 8-shot, CoT
79.6%
📄 Hugging Face model card
MATH
accuracy · 4-shot, CoT
30.0%
📄 Hugging Face model card

Technical architecture