[Proposal] Weak model proposal

[KennyDizi]

In some cases, e.g., generate PR description, PR update change log, PR add docs, PR generates labels,... the model like: claude-3-5-haiku-20241022, gpt-4o-mini-2024-07-18, ... are intelligent enough to take on the requirements. This is the cheapest and fastest version of its provider. Therefore, I suggest a solution to this idea. It looks like this:

• Define the weak model

class ModelType(str, Enum):
    REGULAR = "regular"
    TURBO = "turbo"
    WEAK = "weak"

• Add model_weak to the configuration section, for example:

[config]
model_weak="anthropic/claude-3-5-haiku-20241022"
model="gpt-4o-2024-08-06"
model_turbo="anthropic/claude-3-5-sonnet-20241022"
fallback_models=["gpt-4o-2024-08-06", "anthropic/claude-3-5-sonnet-20241022"]

• In the flows where we want to use the cheap LLM model, we can point it out when we call the function retry_with_fallback_models, like this: await retry_with_fallback_models(self._prepare_prediction, model_type=ModelType.WEAK)
• Update function _get_all_models to perform get all models

def _get_all_models(model_type: ModelType = ModelType.REGULAR) -> List[str]:
    if model_type == ModelType.TURBO:
        model = get_settings().config.model_turbo
    elif model_type == ModelType.WEAK:
        model = get_settings().config.model_weak
    else:
        model = get_settings().config.model
    fallback_models = get_settings().config.fallback_models
    if not isinstance(fallback_models, list):
        fallback_models = [m.strip() for m in fallback_models.split(",")]
    all_models = [model] + fallback_models
    return all_models

[KennyDizi]

Hi @mrT23, Please take a look at this proposal. If it's reasonable, I would like to open a PR to do so.

[mrT23]

@KennyDizi thanks for the idea.

the original intention of the 'turbo' model was supposed to be like this, but during the process it turned out to be something a bit different.

I am trying to understand the proposed code, and I am not sure I understand it.
Let's imagine we have only 2 models: regular, and weak
shouldn't it be something like:

def _get_all_models(model_type: ModelType = ModelType.REGULAR) -> List[str]:
    if model_type == ModelType.WEAK and get_settings().config.model_weak:
        model = get_settings().config.model_weak
    else:
        model = get_settings().config.model
    fallback_models = get_settings().config.fallback_models
    if not isinstance(fallback_models, list):
        fallback_models = [m.strip() for m in fallback_models.split(",")]
    all_models = [model] + fallback_models
    return all_models

meaning if we define some operation as one that can accept a weak model, we will look first for a weak model, If defined.

what do you think about this logic ?

[KennyDizi]

@mrT23 hi, that's a good one.
In general, my idea is:

• In pr description, pr generate labels, pr update change log, we can use the ast and cheap model like: claude-3-5-haiku-20241022, gpt-4o-mini-2024-07-18
• In the pr review, pr code suggestion, we can use the regular model or turbo model based on our choice.

[KennyDizi]

By the way this PR description has written by model anthropic/claude-3-5-haiku-20241022