Content-Type: application/json
Cache-Control: no-cache

    "prompt": "Tell me about llama2"
import urllib.request, json

    url = ""

    hdr ={
    # Request headers
    'Content-Type': 'application/json',
    'Cache-Control': 'no-cache',

    # Request body
    data =  
    data = json.dumps(data)
    req = urllib.request.Request(url, headers=hdr, data = bytes(data.encode("utf-8")))

    req.get_method = lambda: 'POST'
    response = urllib.request.urlopen(req)
    except Exception as e:
// Request body
const body = {
    "prompt": "Tell me about llama2"

fetch('', {
        method: 'POST',
        body: JSON.stringify(body),
        // Request headers
        headers: {
            'Content-Type': 'application/json',
            'Cache-Control': 'no-cache',}
    .then(response => {
    .catch(err => console.error(err));
curl -v -X POST "" -H "Content-Type: application/json" -H "Cache-Control: no-cache" --data-raw "{
    \"prompt\": \"Tell me about llama2\"
import java.util.HashMap;
import java.util.Map;

public class HelloWorld {

  public static void main(String[] args) {
    try {
        String urlString = "";
        URL url = new URL(urlString);
        HttpURLConnection connection = (HttpURLConnection) url.openConnection();

        //Request headers
    connection.setRequestProperty("Content-Type", "application/json");
    connection.setRequestProperty("Cache-Control", "no-cache");

        // Request body
             "{ \"prompt\": \"Tell me about llama2\" }".getBytes()
        int status = connection.getResponseCode();

        BufferedReader in = new BufferedReader(
            new InputStreamReader(connection.getInputStream())
        String inputLine;
        StringBuffer content = new StringBuffer();
        while ((inputLine = in.readLine()) != null) {

    } catch (Exception ex) {
      System.out.print("exception:" + ex.getMessage());
$url = "";
$curl = curl_init($url);

curl_setopt($curl, CURLOPT_CUSTOMREQUEST, "POST");
curl_setopt($curl, CURLOPT_URL, $url);
curl_setopt($curl, CURLOPT_RETURNTRANSFER, true);

# Request headers
$headers = array(
    'Content-Type: application/json',
    'Cache-Control: no-cache',);
curl_setopt($curl, CURLOPT_HTTPHEADER, $headers);

# Request body
$request_body = '{
    "prompt": "Tell me about llama2"
curl_setopt($curl, CURLOPT_POSTFIELDS, $request_body);

$resp = curl_exec($curl);
Meta Llama 2 API
  • Meta Llama 2 API Documentation


    Meta Llama 2 API Documentation The chat model built upon the Meta Llama 3 architecture represents a significant advancement in conversational AI tools. Leveraging Meta’s Llama models, particularly fine-tuned versions, this system can effectively interpret user messages and generate appropriate system responses. Through the chat/completions API call, the model processes incoming messages, generates responses, and maintains the flow of conversation seamlessly.

    Upon receiving a user message, the system utilizes the run Llama 2 capability to analyze the input and formulate a coherent reply. This fine-tuned model not only considers the immediate context but also draws from a vast repository of knowledge embedded within Artificial intelligence algorithms. The model's context length and the context length of the immediate conversation play crucial roles in maintaining coherence over longer interactions. With the increasing sophistication of AI tools, the model size of such systems has become a crucial consideration. Larger models often deliver more accurate and nuanced responses. However, with the advent of open-source LLM (Large Language Models), such as those available through the OpenAI API, developers can access powerful generative AI models without sacrificing performance or efficiency.

    The system prompts generated by these open-source models can be seamlessly integrated into various applications using the Chat API. Whether responding to input prompts in customer service chatbots or engaging users in interactive experiences, the versatility of Llama models empowers developers to create compelling and lifelike conversational interfaces. To optimize the use of these models, developers should consider best practices in prompt engineering. Higher values for certain parameters might yield more creative outputs, while code generation capabilities can be particularly beneficial for technical applications. By experimenting with different models and adjusting API parameters, developers can tailor the conversational AI to meet specific needs.

    For developers looking to get started, using libraries such as import transformers can simplify the process of incorporating the Meta Llama 2 API into their projects. The API is designed to be compatible with a wide range of applications, ensuring that it can meet the needs of different users effectively. The Meta Llama 2 API also features a comprehensive model catalog, showcasing the different versions and capabilities of the Llama models, including the chat version and the latest llava-1.5. This catalog helps developers choose the most appropriate model for their specific use cases.

    By leveraging these features, developers can create robust, reliable, and innovative AI apps that harness the power of the Meta Llama 2 API. Whether for customer service, content generation, or interactive experiences, the API provides the tools and flexibility needed to build state-of-the-art generative AI applications.

  • API Parameters

    The API POST takes the following parameters:


    string, required


    string, optional


    Integration and Implementation

    To utilize the Meta Llama 2 API, developers must send POST requests to the designated endpoint along with the correct headers and request body. The request body should include text inputs, task parameters, and additional settings.

    Base URL

    POST /Get Data

    This endpoint generates text based on the given prompts.

    • URL:
    • Method: POST
    • Headers:
      • Content-Type: application/json
      • Cache-Control: no-cache
      • Ocp-Apim-Subscription-Key: {subscription_key}
    • Body:


        "prompt": "Tell me about Llama2"
  • POST /System Prompt Get Data

    This compatible endpoint also generates text based on the provided prompts.

    • URL:
    • Method: POST
    • Headers:
      • Content-Type: application/json
      • Cache-Control: no-cache
      • Ocp-Apim-Subscription-Key: {subscription_key}
    • Body:


          "prompt": "Tell me about Llama2",
          "system prompt": "You are supposed to be a Llama 2 expert"
    • HTTP Status Codes:
      • 200 OK: The request was successful, and the generated texts are included in the response body.
      • 400 Bad Request: The request must be corrected or include some arguments.
      • 401 Unauthorized: The API key provided in the header is invalid.
      • 500 Internal Server Error: An error occurred on the server while processing the request.
    • Sample Response:


        "status": 200,
        "content-type": "application/json"
    Error Handling

    The Meta Llama 2 API has robust error-handling mechanisms to facilitate seamless operation. Typical status codes encountered include:

    • Error Field Contract:
      • code: An integer that indicates the HTTP status code (e.g., 400, 401, 500).
      • message: A clear and concise description of what the error is about.
      • traceId:A unique identifier that can be used to trace the request in case of issues.
    • AI Model: Refers to the underlying machine learning model used to interpret the text prompts and generate corresponding texts.
    • Changelog: Document detailing any updates, bug fixes, or improvements made to the API in each version.

    Use Cases of Meta Llama 2 API

    Meta Llama 2, an open-source large language model (LLM) developed by Meta and Microsoft, is designed to be versatile and can be used in various applications. Its compatibility with different endpoints and the ability to handle a maximum number of tokens makes it an invaluable tool for numerous scenarios:

    • Chatbots: Meta Llama 2 can be fine-tuned for tasks such as summarization, translation, and dialogue generation. Using the Meta Llama 2 API, it can be integrated into chat interfaces, providing coherent and nuanced responses. The model output can be optimized for different prompts and user input, ensuring high-quality interactions in customer support and other dialogue-based applications.
    • Text Generation: The Meta Llama 2 API is capable of generating text, creating summaries, translations, and even entire texts. The response_format can be tailored to meet the needs of various applications, enhancing content creation processes.
    • Summarization: The Meta Llama 2 API excels at summarizing long texts, articles, or documents into concise and meaningful summaries. This feature is particularly useful for businesses and researchers needing quick insights from extensive information.
    • Translation: Fine-tuned for translation tasks, the Meta Llama 2 API can seamlessly translate text from one language to another. This capability is essential for global businesses and content creators aiming to reach a broader audience.
    • Dialogue Generation: In dialogue applications, the Meta Llama 2 API, integrated with Azure AI Studio, generates coherent and nuanced responses. This feature is vital for pushing the boundaries of conversational AI, enabling more natural and engaging interactions in chatbots and virtual assistants.
    • Content Creation: The Meta Llama 2 API can generate various forms of content, including articles, blog posts, or even entire books. By leveraging advanced AI models, it simplifies the content creation process, allowing creators to focus on their creative vision.
    • Education: The Meta Llama 2 API is a valuable tool for educational purposes, capable of creating interactive mindmaps and summaries from any website or PDF. This feature helps students and educators organize information efficiently.
    • Accessibility: Assisting people with disabilities, the Meta Llama 2 API offers text-to-speech functionality and other accessibility features. It enables blind users to access written content and enhances overall inclusivity.
    • Healthcare: In the healthcare sector, the Meta Llama 2 API can analyze medical texts and generate summaries, aiding healthcare professionals in quickly extracting crucial information from extensive documents.
    • Business: The Meta Llama 2 API is instrumental in generating reports, summaries, and other business documents. Streamlining document creation saves time and enhances productivity.
    • Policy Analysis: For policymakers and analysts, the Meta Llama 2 API can analyze policy documents and generate summaries. This capability provides clear insights, facilitating informed decision-making.
    • Customer Support: The Meta Llama 2 API enhances customer support by generating responses to customer inquiries. Its ability to handle different prompts and provide accurate answers improves the customer experience.
    • Marketing: In marketing, the Meta Llama 2 API can generate content such as social media posts, blog articles, and product descriptions. This feature helps marketers produce engaging content efficiently.
    • Research: Researchers can utilize the Meta Llama 2 API to analyze research papers and generate summaries. This functionality accelerates the research process by highlighting key findings and insights.
    • Entertainment: For creative projects, the Meta Llama 2 API can generate stories, jokes, and even entire scripts. This capability opens new avenues for entertainment and creative expression.

    Advanced Features of the Meta Llama 2 API

    The Meta Llama 2 API offers several advanced features that make it a powerful tool for building generative AI applications. Here are some of the key features:

    • Fine-Tuning: The API supports fine-tuning of the Llama 2 model for specific tasks, such as chatbots, summarization, and translation. Developers can adjust the model to create highly specialized applications by leveraging the fine-tuned generative text models.
    • Reinforcement Learning: The API uses reinforcement learning from human feedback (RLHF) for fine-tuning the model, which involves techniques such as rejection sampling, proximal policy optimization (PPO), and iterative refinement. These methods help in improving the accuracy and relevance of the responses generated by the model. The default value for RLHF parameters ensures a balance between exploration and exploitation, optimizing the model's performance in generating high-quality responses.
    • Open-Source: The model and its model weights are available for download under a community license, allowing for open-source development and integration with internal data. This openness promotes innovation and flexibility in developing AI applications.
    • Free: The model is free to use for both research and commercial purposes, making it an economical option for organizations looking to implement sophisticated AI solutions without incurring high costs.
    • Versatile: The model offers a range of sizes to fit different use cases and platforms, indicating flexibility and adaptability to various requirements. This versatility is enhanced by the availability of different sampling temperature settings, allowing developers to control the creativity of the generated tokens.
    • Safety: The API has been tested for safety and includes measures to mitigate issues such as toxicity and bias, ensuring that the results produced by the system are accurate and trustworthy. Safety protocols are crucial for maintaining the integrity of AI applications in sensitive areas.
    • Responsible Use Guide: The API comes with a comprehensive Responsible Use Guide that provides developers with best practices for safe and responsible AI development and evaluation. This guide is essential for ensuring that AI applications are developed and deployed ethically.
    • Acceptable Use Policy: The API has an acceptable use policy that defines prohibited use cases and promotes fairness and responsible AI applications. Adhering to this policy helps in maintaining the ethical standards of AI usage.
    • Red-Teaming Exercises: The API includes red-teaming exercises to ensure safety and address potential vulnerabilities. These exercises involve exposing fine-tuned models to adversarial prompts to test their robustness and security.
    • Transparency Schematic: The API provides a transparency schematic that outlines the fine-tuning and evaluation methods, known challenges, and shortcomings. This transparency offers valuable information to the AI community and fosters trust and collaboration.

    Key Technical Specifications of the Meta Llama 2 API

    The Meta Llama 2 API, a cutting-edge large language model (LLM) developed by Meta and Microsoft, offers a range of technical features and specifications that cater to diverse use cases and computing capabilities. Here are the detailed specifications:

    • Model Sizes: Llama 2 comes in a range of sizes, from 7 billion to 70 billion parameters. This flexibility allows users to select a model size that best fits their specific use cases and computing capabilities, ensuring efficient performance across different applications.
    • Pre-Training: The models are pre-trained on a large corpus of publicly available online data, including books, articles, and other written content. This extensive pre-training allows Llama 2 to learn general language patterns and effectively generate coherent and contextually accurate text.
    • Fine-Tuning: Llama 2 undergoes supervised fine-tuning and reinforcement learning from human feedback (RLHF) to enhance performance. This process includes techniques like rejection sampling, proximal policy optimization (PPO), and iterative refinement. Human annotations are crucial in this phase, ensuring the model's responses align with expected outcomes and positive values.
    • Tokenizer: The Llama 2 API utilizes the AutoTokenizer from the Transformers library to load a pre-trained tokenizer from the Hugging Face model hub. This tokenizer efficiently processes input tokens and prepares them for the model, optimizing the text generation process.
    • Text Generation Pipeline: A sophisticated text generation pipeline is built using the Transformers library with the Llama 2 model. This pipeline can be configured with torch_dtype for optimizing memory usage and device_map for automatic device selection. The pipeline ensures smooth and efficient text generation, handling both input length and output tokens effectively.
    • Inference API: The Llama 2 API provides an Inference API, which allows users to generate new tokens based on the provided input tokens. This API facilitates seamless integration and real-time text generation for various applications.
    • Licensing: Llama 2 is available under a community license that permits commercial use, with one small exception. If the user count exceeds 700 million per month, additional licensing may be required. This licensing model offers significant cost savings for most users, enabling broad adoption and utilization of the model.
    • Ecosystem Integration: Llama 2 is compatible with various toolkits and libraries such as llama-recipes from Meta for fine-tuning scripts, LangChain, and the Hugging Face ecosystem. This compatibility ensures that developers can leverage existing tools and resources to enhance their projects.
    • Responsible Use: Meta provides a Responsible Use Guide with Llama 2, equipping developers with best practices for safe and responsible AI development and evaluation. This guide ensures that the API is used ethically and responsibly, promoting positive values in AI applications.
    • Model Card: Each version of the Llama 2 model is accompanied by a model card, which details its specifications, training data sources, and intended use cases. The model card provides transparency and helps developers understand the capabilities and limitations of the model.
    • API URL and Access Token: To access the Llama 2 API, users must use the provided API URL and an access token. This secure method of access ensures that only authorized users can utilize the API, maintaining the integrity and security of the service.
    • System Message and Response Message: The API supports a system message to initialize the context and a response message to generate coherent outputs based on the user input. This feature is essential for creating engaging and relevant interactions in applications such as chatbots and virtual assistants.
    • Model Performance: The Llama 2 API is designed for optimal model performance, handling large context sizes and complex input tokens with ease. This performance is crucial for applications that require high-quality text generation and real-time responses.
    • Integration: The Meta Llama 2 API supports integration with Amazon Web Services and other API providers, enabling seamless deployment on virtual machines and other infrastructures. The REST API facilitates easy access to various API endpoints, making it simple to implement the model in diverse environments.

    What are the benefits of the Meta Llama 2 API?

    The Meta Llama 2 API offers several benefits that make it a powerful tool for building generative AI applications. Here are some of the key advantages:

    • Safety: Open-source: As an open-source model, Llama 2 allows users to adjust the weights and fine-tune the model for specific use cases. This flexibility empowers developers to customize the model according to their unique requirements, ensuring optimal performance for various applications.
    • Commercial Use: The model is licensed for commercial use in English, except for companies with over 700 million users per month, which require permission from Meta. This licensing arrangement allows businesses to integrate the model into their products and services with significant cost savings, promoting innovation and widespread adoption.
    • Hardware Efficiency: Fine-tuning the model is quick and can be done on consumer-level hardware with minimal GPUs. This hardware efficiency makes it accessible to a broader range of users and reduces the costs associated with high-performance computing resources.
    • Versatility: Trained on a wide range of data sources, the model is versatile and applicable to various downstream tasks. This versatility is enhanced by its integration with other tools and platforms, such as Azure Machine Learning and the DeepInfra API, which provide additional functionality and support.
    • Easy Customization: The model can be prompt-tuned, which is a cost-effective and convenient way to adapt the model to new AI applications without resource-heavy fine-tuning and model retraining. This allows for quick adjustments to meet specific needs, saving time and resources.
    • Advanced Features: The model includes advanced features such as reinforcement learning from human feedback (RLHF), ensuring that the model provides accurate and helpful responses. Other advanced features include nucleus sampling and function choice, which enhance the model's ability to generate diverse and high-quality outputs.
    • Real-time Insights: The model can provide real-time insights and transparent recommendations for effective governance and decision-making. By analyzing data and generating actionable insights, it supports enhanced decision-making processes in various domains.
    • Accessibility: accessible to the research community, the model ensures continuous development and improvement for better results. This collaborative approach promotes innovation and the refinement of AI capabilities.
    • Cost Savings: The model can provide cost savings by automating tasks and reducing the need for human intervention. This efficiency allows businesses to allocate resources more effectively and improve operational productivity.
    • Improved User Experience: The model can enhance the user experience by providing personalized and relevant responses to user queries. This is particularly beneficial in customer support scenarios, where accurate and timely responses improve customer satisfaction.
    • Enhanced Decision-Making: By providing actionable insights and recommendations based on data analysis, the model supports enhanced decision-making. This is crucial for businesses and organizations that rely on data-driven strategies.
    • Increased Productivity: The model can increase productivity by automating tasks and reducing the need for manual processing. This allows employees to focus on higher-value activities and achieve better outcomes.
    • Better Customer Support: The model can provide better customer support by delivering accurate and helpful responses to customer inquiries. This improves the efficiency of customer service teams and enhances the overall customer experience.
    • Improved Content Generation: Capable of generating high-quality content, such as articles, scripts, and social media posts, the model tailors outputs to specific audiences. This capability is valuable for the marketing, media, and entertainment industries.
    • Integration and Compatibility: The Meta Llama 2 API can seamlessly integrate with various model providers and supports streaming requests for real-time applications. The API's compatibility with the json_body format allows for easy data exchange and processing.
    • API Management: Users can manage their access to the Meta Llama 2 API through secure endpoints, utilizing access tokens and maintaining control over their API usage. This management is facilitated by a comprehensive API URL, which ensures secure and efficient interactions.
    • Collaboration and Support: Developers can access a wealth of resources and support through the Llama 2 community, including email addresses for direct support and collaboration. This network of support enhances the development experience and ensures the successful implementation of AI applications.

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