#instructions on how to get gpt o1 API.

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spark crystal
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instructions on how to get gpt o1 API.

naive willowBOT
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Hi @spark crystal. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:

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To access the OpenAI o1-preview and o1-mini APIs, you need to be a developer who qualifies for API usage Tier 4 or 5. The rate limits for these tiers are as follows:

Tier 5:
o1-preview: 1000 requests per minute
o1-mini: 5000 requests per minute

Tier 4:
o1-preview: 100 requests per minute
o1-mini: 100 requests per minute

Developers can access the model in the Chat Completions API. OpenAI plans to progressively expand access over the weeks following launch.

The context windows for o1-preview and o1-mini is 32k. The OpenAI o1-preview and o1-mini models both have a 128k context window. The OpenAI o1-preview model has an output limit of 32k, and the OpenAI o1-mini model has an output limit of 64k.

Please note that the OpenAI o1 API currently does not support function calling, structured outputs, streaming, support for system messages, and some other features. OpenAI is working to add these functionalities in future updates.

For more information, you can refer to the OpenAI o1 models FAQ (https://help.openai.com/en/articles/9855712-openai-o1-models-faq-chatgpt-enterprise-and-edu#h_4db0449a78).

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@spark crystal kapa.ai is still learning and improving, please let us know how it did by reacting below

spark crystal
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@naive willow Explain how to proceed through the steps to gain that level of API access.

naive willowBOT
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Let me check the knowledge base for you. One moment! :books:

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To gain access to the OpenAI o1-preview and o1-mini APIs, you need to be a developer who qualifies for API usage Tier 4 or 5. However, the specific steps to qualify for these tiers are not detailed in the provided knowledge sources.

It's also important to note that the o1 models are currently in beta, and access is limited with low rate limits. OpenAI is working on adding more features, increasing rate limits, and expanding access to more developers in the coming weeks.

For more information on how to qualify for these tiers and gain access to the o1-preview and o1-mini APIs, I recommend checking the OpenAI API documentation (https://platform.openai.com/docs/) or reaching out to OpenAI support directly.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

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@spark crystal kapa.ai is still learning and improving, please let us know how it did by reacting below

sour brook
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@naive willow help me to build custom gpt for Theory of Computation. That would make specific solutions with the logic of customs steps in answer, for the specific question. The answer, should consist the answer with the specific pattern with high understanding the logic of the process of getting answer. The GPT would have the contextual information as tokens for training custom GPT on the specific data for Theory of Computation, And would be concentrating mainly on definitions and examples to use as a pattern structure of an answer.

naive willowBOT
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Let me check the knowledge base for you again. One moment! :books:

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Building a custom GPT for the Theory of Computation involves several steps, including prompt engineering, defining schemas, and configuring the GPT. Here's a general guide on how you can proceed:

  1. Prompt Engineering: This is the process of crafting prompts to get the right output from a model. You can provide the model with precise instructions, examples, and necessary context information. For example, you can ask the model to explain a specific concept in the Theory of Computation or solve a specific problem. More details on prompt engineering can be found in the OpenAI guide (https://platform.openai.com/docs/guides/text-generation).

  2. Defining Schemas: You can use definitions to define subschemas which are referenced throughout your schema. This can be useful for structuring the output of your GPT. For example, you can define a schema for a step-by-step solution to a problem in the Theory of Computation. More details on defining schemas can be found in the OpenAI guide (https://platform.openai.com/docs/guides/structured-outputs/handling-user-generated-input).

  3. Configuring the GPT: You can configure your GPT to focus on definitions and examples, which can be useful for understanding the Theory of Computation. You can also provide the GPT with contextual information as tokens for training on specific data. More details on configuring GPTs can be found in the OpenAI guide (https://help.openai.com/en/articles/9260256-chatgpt-capabilities-overview).

Remember, the effectiveness of your custom GPT will depend on the quality of your instructions and the data you provide for training. It's also important to note that building a custom GPT requires a good understanding of the GPT technology and the specific domain (in this case, the Theory of Computation).

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@sour brook Kapa.ai is still learning and improving, please let me know how I did by reacting below.

sour brook
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@naive willow Best parameter for Top P in chatgpt for coding solution?

naive willowBOT
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Let me check the knowledge base for you again. One moment! :books:

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The optimal value for the top_p parameter in ChatGPT for coding solutions isn't explicitly mentioned in the provided knowledge sources. However, top_p is a parameter that controls the randomness of the model's responses. A lower value (closer to 0) makes the output more deterministic, while a higher value (closer to 1) makes it more diverse.

In the context of coding solutions, you might want a balance between deterministic and diverse responses. Therefore, a top_p value around 0.5 could be a good starting point. However, the optimal value can vary depending on the specific use case and may require some experimentation to find.

For more information on how to use the top_p parameter, you can refer to the OpenAI API documentation (https://platform.openai.com/docs/).

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@sour brook Kapa.ai is still learning and improving, please let me know how I did by reacting below.

sour brook
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Mistakes in chatGPT since GPT 3.5 until GPT o1-preview.

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@naive willow Mistakes in chatGPT since GPT 3.5 until GPT o1-preview.

naive willowBOT
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Let me check the knowledge base for you again. One moment! :books:

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ChatGPT, from GPT-3.5 to GPT o1-preview, has exhibited several types of mistakes and limitations. Here are some key points based on the provided knowledge sources:

  1. Hallucinations: ChatGPT can sometimes provide convincing but incorrect or misleading information. This is often referred to as "hallucination" in AI literature. It might make up facts, quotes, or citations, which is why it's advised not to use it as the sole source for research. Source (https://help.openai.com/en/articles/8313428-does-chatgpt-tell-the-truth)

  2. Knowledge Limitations: The model's knowledge is not always up-to-date, particularly regarding current events or trends. This is because the model is trained on data available up to a certain point and does not have real-time internet access unless specific plugins are enabled. Source (https://help.openai.com/en/articles/8313428-does-chatgpt-tell-the-truth)

  3. Lack of Tool Access: Models like the OpenAI o1-preview and o1-mini do not have access to advanced tools and features such as memory, custom instructions, data analysis, file uploads, web browsing, vision, and voice. This limits their ability to perform certain tasks that require these capabilities. Source (https://help.openai.com/en/articles/9824965-using-openai-o1-models-and-gpt-4o-models-on-chatgpt)

  4. Confidence in Incorrect Assertions: There are instances where ChatGPT might express confidence in incorrect assertions, and it is not always clear when and why this happens. Source (https://help.openai.com/en/articles/8313428-does-chatgpt-tell-the-truth)

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  1. Rate Limits and Usage Restrictions: During the preview phases, models like o1-preview and o1-mini have tight rate limits, which can affect their availability and performance. These limits are expected to be adjusted over time. Source (https://help.openai.com/en/articles/9824962-openai-o1-preview-and-o1-mini-usage-limits-on-chatgpt-and-the-api)

These points highlight some of the challenges and limitations faced by ChatGPT models from GPT-3.5 to the o1-preview. For more detailed information, you can refer to the respective OpenAI help articles (https://help.openai.com/en/articles/8313428-does-chatgpt-tell-the-truth) and usage guidelines (https://help.openai.com/en/articles/9824965-using-openai-o1-models-and-gpt-4o-models-on-chatgpt).

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

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@sour brook Kapa.ai is still learning and improving, please let me know how I did by reacting below.

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Let me check the knowledge base for you again. One moment! :books:

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Here are some examples of mistakes and limitations that ChatGPT models, from GPT-3.5 to GPT o1-preview, might exhibit:

Common Mistakes

  1. Counting Errors:

    • Example: When asked how many 'r's are in "strawberry," ChatGPT might incorrectly say there are 2, when there are actually 3.

  2. Factual Inaccuracies:

    • ChatGPT might provide incorrect information about historical events, scientific facts, or other data due to outdated training data or hallucinations.

  3. Misinterpretation of Questions:

    • The model might misunderstand the context or intent of a question, leading to irrelevant or incorrect answers.

Policy-Related Mistakes

  1. Harmful Content:

    • ChatGPT might inadvertently generate content that could be interpreted as harmful or inappropriate, such as discussing sensitive topics like self-harm or suicide without proper context or support.

  2. Illegal Activities:

    • There might be instances where the model provides information that could be used to engage in illegal activities, such as making weapons or drugs, despite safeguards in place to prevent this.

  3. Privacy Violations:

    • The model could potentially generate outputs that violate privacy, such as sharing personal data or generating content that could be used for identity theft.

  4. Voice Cloning and Impersonation:

    • While not directly related to text-based models, concerns about AI technologies include the potential misuse for voice cloning or impersonation, which could lead to privacy and security issues.

Mitigation and Reporting

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OpenAI actively works to mitigate these issues by implementing safety measures, refining models, and updating usage policies. Users are encouraged to report any harmful or illegal content they encounter. For more information on reporting, you can refer to the OpenAI help articles (https://help.openai.com/en/articles/7943618-how-do-i-report-harmful-or-illegal-content-in-a-shared-link).

These examples illustrate some of the challenges and areas for improvement in AI models like ChatGPT. OpenAI continues to refine its models to reduce these errors and enhance safety.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

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@sour brook Kapa.ai is still learning and improving, please let me know how I did by reacting below.

sour brook
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@naive willow Also provide links for that. Give me as many links related to chatGPT mistakes, i guess you already understood the concept from our chat.

naive willowBOT
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Let me check the knowledge base for you again. One moment! :books:

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Here are some relevant links and information related to mistakes and limitations of ChatGPT models, as discussed in our conversation:

  1. Hallucinations and Factual Inaccuracies:

  2. Knowledge Limitations:

  3. Policy-Related Mistakes:

  4. Bias and Stereotypes:

  5. Reporting Harmful or Illegal Content:

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These links and sources provide insights into the types of mistakes and limitations that ChatGPT models might exhibit, as well as OpenAI's efforts to address these issues.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon: