nip.code_validation.agents.OpenAiSharedModelGroup#

class nip.code_validation.agents.OpenAiSharedModelGroup(hyper_params: HyperParameters, settings: ExperimentSettings, protocol_handler: ProtocolHandler | CodeValidationProtocolHandler, agent_wholes: dict[str, OpenAiWholeAgent], group_name: str)[source]#

A class representing a group of code validation OpenAI agents sharing a model.

Methods Summary

__getstate__()

Get the state of the object for pickling.

__init__(hyper_params, settings, ...)

_get_fine_tune_job()

Get the fine-tune job from the OpenAI API.

_make_fine_tune_api_call(fine_tune_dataset, ...)

Make the API call to fine-tune the model.

agent_ids_and_names()

Get an iterable of agent IDs and names.

create_dpo_fine_tune_job(...[, job_name])

Create a DPO fine-tune job for the agent group given sampled timesteps.

create_supervised_fine_tune_job(...[, ...])

Create a supervised fine-tune job for the agent.

get_fine_tune_job_error_repr()

Get a string representation of the error for the fine-tune job.

get_fine_tune_job_status()

Get the status of the fine-tune job.

get_state()

Get the state of the shared model group.

get_state_dict()

Get the state dictionary of the agent.

set_state(checkpoint)

Set the state of the shared model group from a checkpoint.

switch_to_next_model()

Switch to the next model after fine-tuning.

Attributes

client

The OpenAI client to use for interacting with the OpenAI API.

max_message_rounds

The maximum number of message rounds in the protocol.

model_name

The current model name, which may be the base model or a fine-tuned model.

agent_wholes

Methods

__getstate__() dict[str, Any][source]#

Get the state of the object for pickling.

We don’t pickle the OpenAI client, as it is not picklable.

Returns:

state (dict[str, any]) – The state of the object.

__init__(hyper_params: HyperParameters, settings: ExperimentSettings, protocol_handler: ProtocolHandler | CodeValidationProtocolHandler, agent_wholes: dict[str, OpenAiWholeAgent], group_name: str)[source]#
_get_fine_tune_job() FineTuningJob[source]#

Get the fine-tune job from the OpenAI API.

_make_fine_tune_api_call(fine_tune_dataset: list[dict], method: Literal['supervised', 'dpo'], job_name: str | None = None)[source]#

Make the API call to fine-tune the model.

Parameters:
  • fine_tune_dataset (list[dict]) – The dataset of examples to fine-tune the model with.

  • method (Literal["supervised", "dpo"]) – The fine-tuning method to use.

  • job_name (str, optional) – A name for the job, to make it more easily identifiable.

agent_ids_and_names() Iterable[tuple[int, str]][source]#

Get an iterable of agent IDs and names.

Yields:
  • agent_id (int) – The ID of the agent.

  • agent_name (str) – The name of the agent.

create_dpo_fine_tune_job(timesteps_per_agent: dict[str, NestedArrayDict], positive_examples_per_agent: dict[str, NestedArrayDict], negative_examples_per_agent: dict[str, NestedArrayDict], job_name: str | None = None)[source]#

Create a DPO fine-tune job for the agent group given sampled timesteps.

This method generates a dataset of examples ready to pass to the fine-tune API.

Parameters:
  • timesteps_per_agent (dict[str, NestedArrayDict]) – The data for each agent in the group. Each agent’s data is a nested dictionary of arrays, which are timesteps selected from the rollouts.

  • positive_examples_per_agent (dict[str, NestedArrayDict]) – The next timestep in the preferred response for each of the timesteps in timesteps_per_agent.

  • negative_examples_per_agent (dict[str, NestedArrayDict]) – The next timestep in the non-preferred response for each of the timesteps in timesteps_per_agent.

  • job_name (str, optional) – A name for the job, to make it more easily identifiable.

create_supervised_fine_tune_job(rollouts_per_agent: dict[str, NestedArrayDict], guess_replaced_rollouts: dict[str, NestedArrayDict] = {}, job_name: str | None = None)[source]#

Create a supervised fine-tune job for the agent.

This method generates a dataset of examples ready to pass to the fine-tune API.

Parameters:
  • rollouts_per_agent (dict[str, NestedArrayDict]) –

    The sampled rollouts for each agent. Each is a nested dictionary of arrays with keys:

    • ”round” (batch round): The current round number.

    • ”message_history” (batch round round channel): The history of messages exchanged between the agents in each channel.

    • ”message_agent_id” (batch round round channel): The id of the agent who messaged at a round-channel pair.

    • ”raw_message_history” (batch round round agent): The raw message generated by each model in each timestep.

    • ”question” (batch round): The problem text.

    • ”solution” (batch round): The proposed solution text.

    • ”y” (batch round): The true label (0 for incorrect, 1 for correct).

    • ”prover_stance” (batch round): When randomizing the prover stance, the verdict that the prover is arguing for, where 0 means “reject” and 1 means “accept”.

  • guess_replaced_rollouts (dict[str, NestedArrayDict], default={}) – Additional rollouts for the verifier agents where the verifier’s guess is to be replaced with the true label. In these the verifier’s guess will be replaced with either ‘Decision: accept’ or ‘Decision: reject’ based on the true label.

  • job_name (str, optional) – A name for the job, to make it more easily identifiable.

get_fine_tune_job_error_repr() str[source]#

Get a string representation of the error for the fine-tune job.

get_fine_tune_job_status() Literal['pending', 'running', 'succeeded', 'failed', 'cancelled'][source]#

Get the status of the fine-tune job.

get_state() PureTextSharedModelGroupState[source]#

Get the state of the shared model group.

get_state_dict() dict[source]#

Get the state dictionary of the agent.

Returns:

state_dict (dict) – The state dictionary of the agent.

set_state(checkpoint: OpenAiSharedModelGroupState | dict[str, Any])[source]#

Set the state of the shared model group from a checkpoint.

Parameters:

checkpoint (AgentCheckpoint) – The checkpoint to restore the state from.

switch_to_next_model()[source]#

Switch to the next model after fine-tuning.