Overview
Llama is a family of large language models that uses publicly available data for training. These models are based on the transformer architecture, which allows it to process input sequences of arbitrary length and generate output sequences of variable length. One of the key features of Llama models is its ability to generate coherent and contextually relevant text. This is achieved through the use of attention mechanisms, which allow the model to focus on different parts of the input sequence as it generates output. Additionally, Llama models use a technique called masked language modeling to pre-train the model on a large corpus of text, which helps it learn to predict missing words in a sentence.
Llama models have shown to perform well on a variety of natural language processing tasks, including language translation, question answering, and text summarization and are also capable of generating human-like text, making Llama models a useful tool for creative writing and other applications where natural language generation is important.
Overall, Llama models are powerful and versatile language models that can be used for a wide range of natural language processing tasks. The models ability to generate coherent and contextually relevant text makes it particularly useful for applications such as chatbots, virtual assistants, and language translation.
Highlights
- Llama 2 pretrained models are trained on 2 trillion tokens, and have a 4k context length. Its fine-tuned chat models have been trained on over 1 million human annotations.
- Llama 2 has undergone internal and external adversarial testing across our fine-tuned models to identify potential toxicity, bias, and other gaps in performance. Our Responsible Use Guide also provides developers with best practices for responsible development and safety evaluations.
- Our model and weights are licensed for both researchers and commercial entities, upholding the principles of openness. Our mission is to empower individuals, and industry through this opportunity, while fostering an environment of discovery and ethical AI advancements.
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Dimension | Cost/unit |
|---|---|
Provisioned Throughput Hourly Price (6 Months Commit) | $13.08 |
Provisioned Throughput Hourly Price (1 Month Commit) | $21.18 |
Model Storage cost per month | $1.95 |
Provisioned Throughput Hourly Price (No Commit) | $23.50 |
Model Storage cost per month | $1.95 |
Provisioned Throughput Hourly Price (1 Month Commit) | $21.18 |
Price per 1 million output tokens | $2.56 |
Price per 1 million input tokens | $1.95 |
Price per 1 million output tokens | $2.56 |
Price per 1 million input tokens | $1.95 |
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