Activefastllm Open Source

Llama 3 8B

by Meta· Released April 2024· Cutoff March 2024

Llama 3 8B is a lightweight, open-source large language model from Meta, designed for efficient text generation and understanding. It offers strong performance for its size, rivaling larger models in many tasks, and is part of the Llama 3 family that includes 8B and 70B parameter variants. The model is optimized for a wide range of natural language processing applications with a focus on accessibility and customization.

Official Site API Docs 🤗 Hugging Face 📄 Research Paper

Input cost

Free (open source)

Output cost

Free (open source)

Context window

8K tokens

Max output

—

Modalities

text

Parameters

License

Llama 3 Community License

Capabilities

Text GenerationCode GenerationInstruction FollowingSummarizationQuestion AnsweringTranslationReasoningStreaming

Best For

Efficient, on-device or self-hosted text generation and understanding tasks requiring a balance of performance and resource usage.

Strengths

Strong performance for its size, competitive with larger models
Open-source and freely available for research and commercial use
Efficient inference, suitable for deployment on consumer hardware
Good multilingual capabilities and instruction following

Limitations

Smaller context window (8K tokens) compared to some competitors
No native vision or multimodal support
May struggle with complex reasoning tasks compared to larger models
Requires fine-tuning for specialized domains

Use Cases

Chatbots and virtual assistants

Code generation and assistance

Content summarization and rewriting

Educational tutoring and Q&A

Data extraction and classification

Language translation

Creative writing and brainstorming

Improvements Over Previous Model

Improved performance over Llama 2 7B on benchmarks like MMLU (68.4% vs 63.9%)
Larger vocabulary (128K tokens vs 32K) for better token efficiency
Enhanced instruction following and safety with RLHF
Support for grouped query attention (GQA) for faster inference
Trained on 15 trillion tokens vs 2 trillion for Llama 2

Back to all models

Improvements Over Previous Model

Improved performance over Llama 2 7B on benchmarks like MMLU (68.4% vs 63.9%)

Larger vocabulary (128K tokens vs 32K) for better token efficiency

Enhanced instruction following and safety with RLHF

Support for grouped query attention (GQA) for faster inference

Trained on 15 trillion tokens vs 2 trillion for Llama 2