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.

__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:

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.

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.

nip.code_validation.agents.OpenAiSharedModelGroup