87 lines
4.2 KiB
Markdown
87 lines
4.2 KiB
Markdown
# Guidance
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## What is Guidance?
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Guidance is a feature that allows users to constrain the generation of a large language model with a specified grammar. This feature is particularly useful when you want to generate text that follows a specific structure or uses a specific set of words or produce output in a specific format.
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## How is it used?
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Guidance can be in many ways and the community is always finding new ways to use it. Here are some examples of how you can use guidance:
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Technically, guidance can be used to generate:
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- a specific JSON object
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- a function signature
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- typed output like a list of integers
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However these use cases can span a wide range of applications, such as:
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- extracting structured data from unstructured text
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- summarizing text into a specific format
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- limit output to specific classes of words (act as a LLM powered classifier)
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- generate the input to specific APIs or services
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- provide reliable and consistent output for downstream tasks
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- extract data from multimodal inputs
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## How it works?
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Diving into the details, guidance is enabled by including a grammar with a generation request that is compiled, and used to modify the chosen tokens.
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This process can be broken down into the following steps:
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1. A request is sent to the backend, it is processed and placed in batch. Processing includes compiling the grammar into a finite state machine and a grammar state.
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<div class="flex justify-center">
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<img
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class="block dark:hidden"
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src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/tgi/request-to-batch.gif"
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/>
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<img
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class="hidden dark:block"
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src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/tgi/request-to-batch-dark.gif"
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/>
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</div>
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2. The model does a forward pass over the batch. This returns probabilities for each token in the vocabulary for each request in the batch.
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3. The process of choosing one of those tokens is called `sampling`. The model samples from the distribution of probabilities to choose the next token. In TGI all of the steps before sampling are called `processor`. Grammars are applied as a processor that masks out tokens that are not allowed by the grammar.
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<div class="flex justify-center">
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<img
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class="block dark:hidden"
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src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/tgi/logit-grammar-mask.gif"
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/>
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<img
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class="hidden dark:block"
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src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/tgi/logit-grammar-mask-dark.gif"
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/>
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</div>
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4. The grammar mask is applied and the model samples from the remaining tokens. Once a token is chosen, we update the grammar state with the new token, to prepare it for the next pass.
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<div class="flex justify-center">
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<img
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class="block dark:hidden"
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src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/tgi/sample-logits.gif"
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/>
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<img
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class="hidden dark:block"
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src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/tgi/sample-logits-dark.gif"
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/>
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</div>
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## How to use Guidance?
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There are two main ways to use guidance; you can either use the `/generate` endpoint with a grammar or use the `/chat/completion` endpoint with tools.
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Under the hood tools are a special case of grammars that allows the model to choose one or none of the provided tools.
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Please refer to [using guidance](../basic_tutorials/using_guidance) for more examples and details on how to use guidance in Python, JavaScript, and cURL.
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### Getting the most out of guidance
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Depending on how you are using guidance, you may want to make use of different features. Here are some tips to get the most out of guidance:
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- If you are using the `/generate` with a `grammar` it is recommended to include the grammar in the prompt prefixed by something like `Please use the following JSON schema to generate the output:`. This will help the model understand the context of the grammar and generate the output accordingly.
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- If you are getting a response with many repeated tokens, please use the `frequency_penalty` or `repetition_penalty` to reduce the number of repeated tokens in the output.
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