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feat: tweaks and images
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@ -26,7 +26,7 @@
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- local: basic_tutorials/safety
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title: Safety
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- local: basic_tutorials/using_guidance
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title: Using Guidance
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title: Using Guidance, JSON, tools (via outlines)
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- local: basic_tutorials/visual_language_models
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title: Visual Language Models
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title: Tutorials
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@ -46,6 +46,6 @@
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- local: conceptual/speculation
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title: Speculation (Medusa, ngram)
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- local: conceptual/guidance
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title: Guidance, JSON, tools (using outlines)
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title: How Guidance Works
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title: Conceptual Guides
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@ -4,13 +4,12 @@ Text Generation Inference (TGI) now supports [JSON and regex grammars](#grammar-
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These feature are available starting from version `1.4.3`. They are accessible via the [text_generation](https://pypi.org/project/text-generation/) library. The tool support is compatible with OpenAI's client libraries. The following guide will walk you through the new features and how to use them!
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> The Grammar guidance support is currently only available in the TGI API due to lack of support in Open AI API.
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| Endpoint | Grammar | Tools |
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| ------------------- | ------- | ----- |
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| `/generate` | ✅ | ❌ |
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| `/chat/completions` | ❌ | ✅ |
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## Quick Start
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Before we jump into the deep end, ensure your system is using TGI version `1.4.3` or later to access all the features we're about to explore in this guide.
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If you're not up to date, grab the latest version and let's get started!
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_note: guidance is supported as grammar in the `/generate` endpoint and as tools in the `/chat/completions` endpoint._
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## How it works
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@ -37,7 +36,7 @@ If you are interested in the technical details on how outlines is used in TGI, y
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### The Grammar Parameter
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In TGI `1.4.3`, we've introduced the grammar parameter, which allows you to specify the format of the response you want from the AI. This is a game-changer for those who need precise control over the AI's output.
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In TGI `1.4.3`, we've introduced the grammar parameter, which allows you to specify the format of the response you want from the LLM.
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Using curl, you can make a request to TGI's Messages API with the grammar parameter. This is the most primitive way to interact with the API and using [Pydantic](#constrain-with-pydantic) is recommended for ease of use and readability.
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@ -80,14 +79,12 @@ curl localhost:3000/generate \
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```
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A grammar can be defined using Pydantic models, JSON schemas, or regular expressions. The AI will then generate a response that conforms to the specified grammar.
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A grammar can be defined using Pydantic models, JSON schemas, or regular expressions. The LLM will then generate a response that conforms to the specified grammar.
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> Note: A grammar must compile to an intermediate representation to constrain the output. Grammar compilation is a computationally expensive and may take a few seconds to complete on the first request. Subsequent requests will use the cached grammar and will be much faster.
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### Constrain with Pydantic
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Pydantic is a powerful library for data validation and settings management. It's the perfect tool for crafting the a specific response format.
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Using Pydantic models we can define a similar grammar as the previous example in a shorter and more readable way.
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```python
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@ -130,7 +127,7 @@ print(response.json())
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### JSON Schema Integration
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If Pydantic's not your style, go raw with direct JSON Schema integration. It's like having a conversation with the AI in its own language. This is simliar to the first example but with programmatic control.
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If Pydantic's not your style, go raw with direct JSON Schema integration. This is simliar to the first example but with programmatic control.
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```python
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import requests
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@ -228,7 +225,7 @@ if __name__ == "__main__":
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In addition to the grammar parameter, we've also introduced a set of tools and functions to help you get the most out of the Messages API.
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Tools are a set of user defined functions that can be used in tandem with the chat functionality to enhance the AI's capabilities. You can use these tools to perform a variety of tasks, such as data manipulation, formatting, and more.
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Tools are a set of user defined functions that can be used in tandem with the chat functionality to enhance the LLM's capabilities. Functions, similar to grammar are defined as JSON schema and can be passed as part of the parameters to the Messages API.
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Functions, similar to grammar are defined as JSON schema and can be passed as part of the parameters to the Messages API.
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@ -273,8 +270,51 @@ curl localhost:3000/v1/chat/completions \
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// {"id":"","object":"text_completion","created":1709051640,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-native","choices":[{"index":0,"message":{"role":"assistant","tool_calls":{"id":0,"type":"function","function":{"description":null,"name":"tools","parameters":{"format":"celsius","location":"New York"}}}},"logprobs":null,"finish_reason":"eos_token"}],"usage":{"prompt_tokens":157,"completion_tokens":19,"total_tokens":176}}
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```
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### Text Generation Inference Client
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TGI provides a client library to interact with the Messages API and Tool functions. The client library is available in both synchronous and asynchronous versions.
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```python
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from text_generation import AsyncClient
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# NOTE: tools defined above and removed for brevity
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# Define an async function to encapsulate the async operation
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async def main():
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client = AsyncClient(base_url="http://localhost:3000")
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# Use 'await' to wait for the async method 'chat' to complete
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response = await client.chat(
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max_tokens=100,
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seed=1,
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tools=tools,
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presence_penalty=-1.1,
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messages=[
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{
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"role": "system",
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"content": "You're a helpful assistant! Answer the users question best you can.",
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},
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{
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"role": "user",
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"content": "What is the weather like in Brooklyn, New York?",
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},
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],
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)
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# Once the response is received, you can process it
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print(response.choices[0].message.tool_calls)
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# Ensure the main async function is run in the event loop
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if __name__ == "__main__":
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import asyncio
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asyncio.run(main())
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# {"id":"","object":"text_completion","created":1709051942,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-native","choices":[{"index":0,"message":{"role":"assistant","tool_calls":{"id":0,"type":"function","function":{"description":null,"name":"tools","parameters":{"format":"celsius","location":"New York"}}}},"logprobs":null,"finish_reason":"eos_token"}],"usage":{"prompt_tokens":157,"completion_tokens":20,"total_tokens":177}}
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```
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<details>
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<summary>Tools used in example below</summary>
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<summary>Tools used in example above</summary>
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```python
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tools = [
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@ -331,49 +371,6 @@ curl localhost:3000/v1/chat/completions \
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</details>
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### Text Generation Inference Client
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TGI provides a client library to interact with the Messages API and Tool functions. The client library is available in both synchronous and asynchronous versions.
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```python
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from text_generation import AsyncClient
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# NOTE: tools defined above and removed for brevity
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# Define an async function to encapsulate the async operation
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async def main():
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client = AsyncClient(base_url="http://localhost:3000")
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# Use 'await' to wait for the async method 'chat' to complete
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response = await client.chat(
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max_tokens=100,
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seed=1,
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tools=tools,
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presence_penalty=-1.1,
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messages=[
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{
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"role": "system",
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"content": "You're a helpful assistant! Answer the users question best you can.",
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},
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{
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"role": "user",
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"content": "What is the weather like in Brooklyn, New York?",
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},
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],
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)
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# Once the response is received, you can process it
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print(response.choices[0].message.tool_calls)
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# Ensure the main async function is run in the event loop
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if __name__ == "__main__":
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import asyncio
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asyncio.run(main())
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# {"id":"","object":"text_completion","created":1709051942,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-native","choices":[{"index":0,"message":{"role":"assistant","tool_calls":{"id":0,"type":"function","function":{"description":null,"name":"tools","parameters":{"format":"celsius","location":"New York"}}}},"logprobs":null,"finish_reason":"eos_token"}],"usage":{"prompt_tokens":157,"completion_tokens":20,"total_tokens":177}}
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```
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### OpenAI integration
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TGI exposes an OpenAI-compatible API, which means you can use OpenAI's client libraries to interact with TGI's Messages API and Tool functions.
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@ -21,6 +21,13 @@ However these use cases can span a wide range of applications, such as:
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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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<div class="flex justify-center">
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<img
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src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/tgi/multimodal-guidance.png"
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/>
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</div>
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## How it works?
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@ -29,14 +36,15 @@ Diving into the details, guidance is enabled by including a grammar with a gener
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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>placeholder for image 1</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>placeholder for image 2</div>
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<div class="flex justify-center">
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<img
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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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</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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