pipelinehuggingface.py

from tree_of_thoughts.treeofthoughts import HFPipelineModel, OptimizedTreeofThoughts
from tree_of_thoughts.treeofthoughts import OpenAILanguageModel, GuidanceOpenAILanguageModel, TreeofThoughts, OptimizedOpenAILanguageModel, OptimizedTreeofThoughts, HuggingLanguageModel


model_name="gpt2"
gpt2_pipeline_model = HFPipelineModel(model_name)

tree_of_thoughts = OptimizedTreeofThoughts(gpt2_pipeline_model, search_algorithm="DFS")


# from transformers import AutoModelForCausalLM, AutoTokenizer


# # Initialize the HuggingLanguageModel with the GPT-2 model
# model_name = "gpt2"
# model = HuggingLanguageModel(model_name, 
#                              model_Tokenizer="gpt2", 
#                              verbose=True)
                             



#choose search algorithm('BFS' or 'DFS')
search_algorithm = "BFS"

#cot or propose
strategy="cot"

# value or vote
evaluation_strategy = "value"



input_problem = "use 4 numbers and basic arithmetic operations (+-*/) to obtain 24"
# k = 5
# T = 3
# b = 5
# vth = 0.5
# timeout = 10
# confidence = 0.8 #cmodel is confident on performance
# max_iterations = 40 #tree branh nodes 
# convergence_threshold = 0.01
# convergence_count = 5

    
solution = tree_of_thoughts.solve(input_problem)
                                #   `k, T, b, vth, timeout, confidence_threshold=confidence, max_iterations=max_iterations, convergence_threshold=convergence_threshold, convergence_count=convergence_count)
    
#use the solution in your production environment
print(f"solution: {solution}")