Generations API NEW ​

The Generations API is a lightweight library with the goal of providing high-level access to LMQL features, such as its inference backends, (constrained) generation, and scoring. The API was designed to be easy to use and does not require users to write any LMQL themselves.

Overview ​

To illustrate the Generations API, let's look at a simple example of generating and scoring text using the openai/text-davinci-003 model:

import lmql

# obtain a model instance
m: lmql.LLM = lmql.model("openai/text-davinci-003")

# simple generation
m.generate_sync("Hello", max_tokens=10)
# -> Hello, I am a 23 year old female.

# sequence scoring
m.score_sync("Hello", ["World", "Apples", "Oranges"])
# lmql.ScoringResult(model='openai/text-davinci-003')
# -World: -3.9417848587036133
# -Apples: -15.26676321029663
# -Oranges: -16.22640037536621

The snippet above demonstrates the different components of the Generations API:

• lmql.LLM At the core of the Generations API are lmql.LLM objects. Using the lmql.model(...) constructor, you can access a wide range of different models, as described in the Models chapter. This includes support for models running in the same process, in a separate worker process or cloud-based models available via a API endpoint.

• lmql.LLM.generate(...) is a simple function to generating text completions based on a given prompt. This can be helpful to quickly obtain single-step completions, or to generate a list of completions for a given prompt.

• lmql.LLM.score(...) allows you to directly access the scores, your model assigns to the tokenized representation of your input prompt and continuations. This can be helpful for tasks such as classification or ranking.

  The result is an lmql.ScoringResult object, which contains the scores for each continuation, as well as the prompt and continuations used for scoring. It provides a convenient interface for score aggregation, normalization and maximum selection.

Compatibility For more advanced use cases, the Generation API seamlessly blends with standard LMQL, allowing users to gradually adopt the full language runtime over time, if their use cases require it.

Implementation Internally, the Generations API is implemented as a thin wrapper around LMQL, and thus benefits from all the features of LMQL, such as caching, parallelization, and more. The API is fully asynchronous, and should be used with asyncio. Alternatively, all API funcationality is also available synchronously, using the *_sync variants of the functions.

lmql.LLM Objects ​

At the core, lmql.LLM objects represent a specific language model and provide methods for generation and scoring. An lmql.LLM is instantiated using lmql.model(...) and can be passed as-is to LMQL query programs or to the top-level lmql.generate and lmql.score functions.

LLM.generate(...) ​

async def generate(
    self,
    prompt: str, 
    max_tokens: Optional[int] = None, 
    decoder: str,
    **kwargs
) -> Union[str, List[str]]

Generates a text completion based on a given prompt. Returns the full prompt + completion as one string.

Arguments

• prompt: str: The prompt to generate from.
• max_tokens: Optional[int]: The maximum number of tokens to generate. If None, text is generated until the model returns an end-of-sequence token.
• decoder: str: The decoding algorithm to use for generation. Defaults to "argmax".
• **kwargs: Additional keyword arguments that are passed to the underlying LMQL query program. These can be useful to specify options like chunksize, decoder arguments like n, or any other model or decoder-specific arguments.

Return Value The function returns a string or a list of strings, depending on the decoder in use (decoder=argmax yields a single sequence, decoder="sample", n=2 yields two sequences, etc.).

Asynchronous The function is asynchronous and should be used with asyncio and with await. When run in parallel, multiple generations will be batched and parallelized across multiple calls to the same model. For synchronous use, you can rely on LLM.generate_sync, note however, that in this case, the function will block the current thread until generation is complete.

LLM.generate_sync(...) ​

def generate_sync(self, *args, **kwargs):

Synchronous version of lmql.LLM.generate.

LLM.score(...) ​

async def score(
    self,
    prompt: str,
    values: Union[str, List[str]]
) -> lmql.ScoringResult

Scores different continuation values for a given prompt.

For instance await m.score("Hello", ["World", "Apples", "Oranges"]) would score the continuations "Hello World", "Hello Apples" and "Hello Oranges".

Arguments

• prompt: The prompt to use as a common prefix for all continuations.
• values: The continuation values to score. This can be a single string or a list of strings.

Return Value The result is an lmql.ScoringResult object, which contains the scores for each continuation, as well as the prompt and continuations used for scoring. It provides a convenient interface for score aggregation, normalization and maximum selection.

Asynchronous The function is asynchronous and should be used with asyncio and with await. When run in parallel, multiple generations will be batched and parallelized across multiple calls to the same model. For synchronous use, you can rely on LLM.score_sync.

LLM.score_sync(...) ​

def score_sync(self, *args, **kwargs)

Synchronous version of lmql.LLM.score.

Top-Level Functions ​

The Generation API is also available directly in the top-level namespace of the lmql module. This allows for direct generation and scoring, without the need to instantiate an lmql.LLM object first.

lmql.generate(...) ​

async def lmql.generate(
    prompt: str, 
    max_tokens: Optional[int] = None, 
    model: Optional[Union[LLM, str]] = None, 
    **kwargs
) -> Union[str, List[str]]

lmql.generate generates text completions based on a given prompt and behaves just like LLM.generate, with the provided model instance or model name.

If no model is provided, the default model is used. See lmql.set_default_model for more information.

lmql.generate_sync(...) ​

Synchronous version of lmql.generate.

lmql.score(...) ​

async def score(
    prompt: str,
    values: Union[str, List[str]],
    model: Optional[Union[str, LLM]] = None, 
    **kwargs
) -> lmql.ScoringResult

lmql.score scores different continuation values for a given prompt and behaves just like LLM.score, with the provided model instance or model name.

If no model is provided, the default model is used. See lmql.set_default_model for more information.

lmql.score_sync(...) ​

Synchronous version of lmql.score.

lmql.set_default_model(...) ​

def set_default_model(model: Union[str, LLM])

Sets the model to be used when no from clause or @lmql.query(model=<model>) are specified in LMQL. The default model applies globally in the current process and affects both LMQL queries and Generation API methods like lmql.generate and lmql.score functions.

You can also specify the environment variable LMQL_DEFAULT_MODEL to set the default model.