aribornstein

from autogen_core import CancellationToken
from pydantic import BaseModel
from autogen_core.models import ChatCompletionClient, CreateResult, SystemMessage, UserMessage, AssistantMessage
from autogen_core.tools import Tool
from llama_cpp import Llama
from typing import List, Dict, Any, Literal, Optional, Sequence, Union, AsyncGenerator
import json

class ComponentModel(BaseModel):
    provider: str

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# Export To Onnx
model.to_onnx(filepath, input_sample, export_params=True)

# Export to Torch Script
torch.jit.save(model.to_torchscript(), "model.pt")

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Trainer(num_gpus=32).predict(millions_of_reviews)

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prediction = model.predict("This movie is great!")

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# Save Checkpoint 
trainer.save_checkpoint("text_class_model.pt")

# Load Model From Checkpoint 
model = TextClassifier.load_from_checkpoint("text_class_model.pt")

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trainer.test()

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trainer = flash.Trainer(gpus=8)
trainer = flash.Trainer(gpus=8, num_nodes=32)
trainer = flash.Trainer(tpu_cores=1)

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trainer = flash.Trainer(max_epochs = 1) 
trainer.finetune(model, datamodule = datamodule)

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model = TextClassifier(num_classes = 2, backbone = 'roberta-base')

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datamodule = TextClassificationData.from_files(
                train_file="data/imdb/train.csv",
                valid_file="data/imdb/valid.csv",
                test_file="data/imdb/test.csv",
                input="review",
                target="sentiment"
)