Llama 3 8b
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks.
API
If you're looking for an API, you can choose from your desired programming language.
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const axios = require('axios');
const api_key = "YOUR API-KEY";
const url = "https://api.segmind.com/v1/llama-v3-8b-instruct";
const data = {
"messages": [
{
"role": "user",
"content": "tell me a joke on cats"
},
{
"role": "assistant",
"content": "here is a joke about cats..."
},
{
"role": "user",
"content": "now a joke on dogs"
}
]
};
(async function() {
try {
const response = await axios.post(url, data, { headers: { 'x-api-key': api_key } });
console.log(response.data);
} catch (error) {
console.error('Error:', error.response.data);
}
})();Attributes
An array of objects containing the role and content
Could be "user", "assistant" or "system".
A string containing the user's query or the assistant's response.
To keep track of your credit usage, you can inspect the response headers of each API call. The x-remaining-credits property will indicate the number of remaining credits in your account. Ensure you monitor this value to avoid any disruptions in your API usage.
Llama 3 8b
Meta Llama 3 8b, is a game-changer in the world of large language models (LLMs). Developed by Meta AI, it's designed to be open-source and accessible, making it a valuable tool for developers, researchers, and businesses alike. Meta Llama 3 is a foundational system, meaning it serves as a base for building even more advanced AI applications.
Focus on Accessibility
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Open-source: Unlike many powerful LLMs, Meta Llama 3 is freely available for anyone to use and modify. This fosters innovation and collaboration within the AI community.
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Scalability: Llama 3 comes in two sizes: 8B and 70B parameters. This allows users to choose the version that best suits their needs and computational resources.
Enhanced Capabilities
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Efficient Tokenizer: Meta Llama 3 uses a tokenizer with a vocabulary of 128,000 tokens. This allows it to encode language effectively, leading to improved performance compared to previous models.
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Grouped Query Attention (GQA): This technique improves the efficiency of the model during the inference stage, making it faster to process information and generate responses.
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