Llama-3.2-1B-Instruct
Maintainer: meta-llama
269
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Property | Value |
---|---|
Run this model | Run 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 Llama-3.2-1B-Instruct
model is part of the Meta Llama 3.2 collection of multilingual large language models (LLMs). It is a 1 billion parameter, pretrained and instruction-tuned generative model optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. The Llama-3.2-3B-Instruct model is a larger 3 billion parameter version in the same model family.
Model inputs and outputs
The Llama-3.2-1B-Instruct
model takes multilingual text as input and generates multilingual text and code as output. It has a context length of 128k tokens.
Inputs
- Multilingual text
Outputs
- Multilingual text and code
Capabilities
The Llama 3.2 models are optimized for multilingual dialogue tasks, including assistive applications like knowledge retrieval and summarization. They outperform many open-source and closed-domain chat models on common industry benchmarks.
What can I use it for?
The Llama-3.2-1B-Instruct
model is intended for commercial and research use in multiple languages. The instruction-tuned version is well-suited for applications like multilingual chatbots, writing assistants, and query/prompt rewriting. The pretrained version can also be adapted for a variety of other natural language generation tasks.
Things to try
Given the model's multilingual capabilities, you could experiment with using it for cross-lingual applications, such as translating between supported languages or generating content in multiple languages. The model's strong performance on tasks like summarization also makes it interesting to try for content creation and analysis use cases.
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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