Meta-Llama-3-8B-Instruct
Maintainer: meta-llama
1.5K
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| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model overview
The Meta-Llama-3-8B-Instruct is a large language model developed and released by Meta. It is part of the Llama 3 family of models, which come in 8 billion and 70 billion parameter sizes, with both pretrained and instruction-tuned variants. The instruction-tuned Llama 3 models are optimized for dialogue use cases and outperform many open-source chat models on common industry benchmarks. Meta has taken care to optimize these models for helpfulness and safety.
The Llama 3 models use an optimized transformer architecture and were trained on a mix of publicly available online data. The 8 billion parameter version uses a context length of 8k tokens and is capable of tasks like commonsense reasoning, world knowledge, reading comprehension, and math. Compared to the earlier Llama 2 models, the Llama 3 models have improved performance across a range of benchmarks.
Model inputs and outputs
Inputs
- Text input only
Outputs
- Generates text and code
Capabilities
The Meta-Llama-3-8B-Instruct model is capable of a variety of natural language generation tasks, including dialogue, summarization, question answering, and code generation. It has shown strong performance on benchmarks evaluating commonsense reasoning, world knowledge, reading comprehension, and math.
What can I use it for?
The Meta-Llama-3-8B-Instruct model is intended for commercial and research use in English. The instruction-tuned variants are well-suited for assistant-like chat applications, while the pretrained models can be further fine-tuned for a range of text generation tasks. Developers should carefully review the Responsible Use Guide before deploying the model in production.
Things to try
Developers may want to experiment with fine-tuning the Meta-Llama-3-8B-Instruct model on domain-specific data to adapt it for specialized applications. The model's strong performance on benchmarks like commonsense reasoning and world knowledge also suggests it could be a valuable foundation for building knowledge-intensive applications.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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