Meta-Llama-3-70B-Instruct
Maintainer: meta-llama
783
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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-3-70B-Instruct is a large language model (LLM) developed and released by Meta. It is part of the Meta Llama 3 family of models, which includes both 8B and 70B parameter versions in pre-trained and instruction-tuned variants. The Llama 3 instruction-tuned models are optimized for dialogue use cases and outperform many available open-source chat models on common industry benchmarks. Meta took great care in developing these models to optimize for helpfulness and safety.
The Meta-Llama-3-8B-Instruct is a smaller 8 billion parameter version of the instruction-tuned Llama 3 model, while the Llama-2-70b-chat-hf is a 70 billion parameter Llama 2 model tuned specifically for chatbot applications.
Model inputs and outputs
Inputs
- Text input only
Outputs
- Generates text and code
Capabilities
The Meta-Llama-3-70B-Instruct model is a powerful generative text model capable of a wide range of natural language tasks. It can engage in helpful and safe dialogue, generate coherent and relevant text, and even produce code. The model's large size and instruction tuning allow it to outperform many open-source chat models on industry benchmarks.
What can I use it for?
The Meta-Llama-3-70B-Instruct model is well-suited for commercial and research use cases that require an advanced language model for tasks like chatbots, content generation, code generation, and more. Developers can fine-tune the model for specific applications or use the pre-trained version as-is. The model's capabilities make it a valuable tool for businesses looking to enhance their conversational AI offerings or automate content creation.
Things to try
One interesting aspect of the Meta-Llama-3-70B-Instruct model is its strong performance on both language understanding and generation tasks. Developers can experiment with using the model for a variety of natural language applications, from open-ended dialogue to more structured tasks like question answering or summarization. The model's large size and instruction tuning also make it well-suited for few-shot learning, where it can adapt quickly to new tasks with limited training data.
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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