nip.scenario_base.agents.PureTextSharedModelGroup#

class nip.scenario_base.agents.PureTextSharedModelGroup(hyper_params: HyperParameters, settings: ExperimentSettings, protocol_handler: ProtocolHandler, agent_wholes: Iterable[PureTextWholeAgent], group_name: str)[source]#

A class representing a group of pure text agents which share the same model.

The shared model is fine-tuned on the data from all agents in the group.

Parameters:

hyper_params (HyperParameters) – The parameters of the experiment.
settings (ExperimentSettings) – The settings of the experiment.
protocol_handler (ProtocolHandler) – The protocol handler for the experiment.
agent_wholes (Iterable[PureTextWholeAgent]) – The agents in the shared model group.
group_name (str) – The name of the shared model group.

Methods Summary

`__init__`(hyper_params, settings, ...)
`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 group given sampled rollouts.
`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 of the shared model group as a dict.
`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

`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.

Methods

__init__(hyper_params: HyperParameters, settings: ExperimentSettings, protocol_handler: ProtocolHandler, agent_wholes: Iterable[PureTextWholeAgent], group_name: str)[source]#

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

Get an iterable of agent IDs and names.

Yields:

abstract 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.

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.

abstract 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 group given sampled rollouts.

This method is used to do supervised fine-tuning (as opposed to other methods of fine-tuning, like reinforcement learning).

Parameters:

rollouts_per_agent (dict[str, NestedArrayDict]) – The data for each agent in the group, sampled from the environment.
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.
job_name (str, optional) – A name for the job, to make it more easily identifiable.

abstract get_fine_tune_job_error_repr() → str[source]#: Get a string representation of the error for the fine-tune job.

abstract 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 of the shared model group as a dict.

This method should be implemented by subclasses capable of saving their state.

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

This method should be overridden by subclasses to restore the state of the shared model group from a checkpoint.

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

abstract switch_to_next_model()[source]#: Switch to the next model after fine-tuning.

nip.scenario_base.agents.PureTextSharedModelGroup