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2023-11-06 10:42:00

November 6, 2023

The xAI PromptIDE is an built-in growth surroundings for immediate engineering and interpretability analysis. It accelerates immediate engineering by an SDK that enables implementing advanced prompting strategies and wealthy analytics that visualize the community’s outputs. We use it closely in our steady growth of Grok.

We developed the PromptIDE to offer clear entry to Grok-1, the mannequin that powers Grok, to engineers and researchers locally. The IDE is designed to empower customers and assist them discover the capabilities of our giant language fashions (LLMs) at tempo. On the coronary heart of the IDE is a Python code editor that – mixed with a brand new SDK – permits implementing advanced prompting strategies. Whereas executing prompts within the IDE, customers see useful analytics such because the exact tokenization, sampling possibilities, various tokens, and aggregated consideration masks.

The IDE additionally affords high quality of life options. It mechanically saves all prompts and has built-in versioning. The analytics generated by operating a immediate might be saved completely permitting customers to match the outputs of various prompting strategies. Lastly, customers can add small information equivalent to CSV information and skim them utilizing a single Python operate from the SDK. When mixed with the SDK’s concurrency options, even considerably giant information might be processed shortly.

We additionally hope to construct a neighborhood across the PromptIDE. Any immediate might be shared publicly on the click on of a button. Customers can determine in the event that they solely wish to share a single model of the immediate or your complete tree. It is also attainable to incorporate any saved analytics when sharing a immediate.

The PromptIDE is obtainable to members of our early access program. Beneath, you discover a walkthrough of the primary options of the IDE.

the xAI Staff

Sampling probabilities in the PromptIDE

On the coronary heart of the PromptIDE is a code editor and a Python SDK. The SDK supplies a brand new programming paradigm that enables implementing advanced prompting strategies elegantly. All Python capabilities are executed in an implicit context, which is a sequence of tokens. You’ll be able to manually add tokens to the context utilizing the immediate() operate or you should utilize our fashions to generate tokens primarily based on the context utilizing the pattern() operate. When sampling from the mannequin, you will have numerous configuration choices which might be handed as argument to the operate:

async def pattern(
    max_len: int = 256,
    temperature: float = 1.0,
    nucleus_p: float = 0.7,
    stop_tokens: Optionally available[list[str]] = None,
    stop_strings: Optionally available[list[str]] = None,
    rng_seed: Optionally available[int] = None,
    add_to_context: bool = True,
    return_attention: bool = False,
    allowed_tokens: Optionally available[Sequence[Union[int, str]]] = None,
    disallowed_tokens: Optionally available[Sequence[Union[int, str]]] = None,
    augment_tokens: bool = True,
) -> SampleResult:
    """Generates a mannequin response primarily based on the present immediate.

    The present immediate consists of all textual content that has been added to the immediate both for the reason that
    starting of this system or for the reason that final name to `clear_prompt`.

        max_len: Most variety of tokens to generate.
        temperature: Temperature of the ultimate softmax operation. The decrease the temperature, the
            decrease the variance of the token distribution. Within the restrict, the distribution collapses
            onto the one token with the very best likelihood.
        nucleus_p: Threshold of the High-P sampling approach: We rank all tokens by their
            likelihood after which solely really pattern from the set of tokens that ranks within the
            High-P percentile of the distribution.
        stop_tokens: A listing of strings, every of which might be mapped independently to a single
            token. If a string doesn't map cleanly to 1 token, it will likely be silently ignored.
            If the community samples one in every of these tokens, sampling is stopped and the cease token
            *shouldn't be* included within the response.
        stop_strings: A listing of strings. If any of those strings happens within the community output,
            sampling is stopped however the string that triggered the cease *might be* included within the
            response. Be aware that the response could also be longer than the cease string. For instance, if
            the cease string is "Hel" and the community predicts the single-token response "Whats up",
            sampling might be stopped however the response will nonetheless learn "Whats up".
        rng_seed: See of the random quantity generator used to pattern from the mannequin outputs.
        add_to_context: If true, the generated tokens might be added to the context.
        return_attention: If true, returns the eye masks. Be aware that this will considerably
            improve the response measurement for lengthy sequences.
        allowed_tokens: If set, solely these tokens might be sampled. Invalid enter tokens are
            ignored. Solely one in every of `allowed_tokens` and `disallowed_tokens` have to be set.
        disallowed_tokens: If set, these tokens can't be sampled. Invalid enter tokens are
            ignored. Solely one in every of `allowed_tokens` and `disallowed_tokens` have to be set.
        augment_tokens: If true, strings handed to `stop_tokens`, `allowed_tokens` and
            `disallowed_tokens` might be augmented to incorporate each the handed token and the
            model with main whitespace. That is helpful as a result of most phrases have two
            corresponding vocabulary entries: one with main whitespace and one with out.

        The generated textual content.

The code is executed regionally utilizing an in-browser Python interpreter that runs in a separate net employee. A number of net employees can run on the identical time, which implies you possibly can execute many prompts in parallel.

Sampling probabilities in the PromptIDE

Complicated prompting strategies might be applied utilizing a number of contexts inside the identical program. If a operate is annotated with the @prompt_fn decorator, it’s executed in its personal, recent context. The operate can carry out some operations independently of its mother or father context and go the outcomes again to the caller utilizing the return assertion. This programming paradigm allows recursive and iterative prompts with arbitrarily nested sub-contexts.


The SDK makes use of Python coroutines that allow processing a number of @prompt_fn-annotated Python capabilities concurrently. This may considerably pace up the time to completion – particularly when working with CSV information.

Sampling probabilities in the PromptIDE

Consumer inputs

Prompts might be made interactive by the user_input() operate, which blocks execution till the consumer has entered a string right into a textbox within the UI. The user_input() operate returns the string entered by the consumer, which cen then, for instance, be added to the context by way of the immediate() operate. Utilizing these APIs, a chatbot might be applied in simply 4 strains of code:

await immediate(PREAMBLE)
whereas textual content := await user_input("Write a message"):
    await immediate(f"<|separator|>nnHuman: {textual content}<|separator|>nnAssistant:")
    await pattern(max_len=1024, stop_tokens=["<|separator|>"], return_attention=True)


Builders can add small information to the PromptIDE (as much as 5 MiB per file. At most 50 MiB whole) and use their uploaded information within the immediate. The read_file() operate returns any uploaded file as a byte array. When mixed with the concurrency characteristic talked about above, this can be utilized to implement batch processing prompts to judge a prompting approach on quite a lot of issues. The screenshot under exhibits a immediate that calculates the MMLU analysis rating.

Sampling probabilities in the PromptIDE

See Also

Whereas executing a immediate, customers see detailed per-token analytics to assist them higher perceive the mannequin’s output. The completion window exhibits the exact tokenization of the context alongside the numeric identifiers of every token. When clicking on a token, customers additionally see the top-Okay tokens after making use of top-P thresholding and the aggregated consideration masks on the token.

Sampling probabilities in the PromptIDE

Sampling probabilities in the PromptIDE

When utilizing the user_input() operate, a textbox exhibits up within the window whereas the immediate is operating that customers can enter their response into. The under screenshot exhibits the results of executing the chatbot code snippet listed above.

Sampling probabilities in the PromptIDE

Lastly, the context can be rendered in markdown to enhance legibility when the token visualization options are usually not required.

Sampling probabilities in the PromptIDE

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