README.md

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EPIC Router Family

This repository hosts the public checkpoints for the EPIC router models. Each checkpoint learns to pick the best reasoning configuration (method, aggregator, sample count, etc.) given a natural-language math question.

Available Versions

Subdirectory	File	Notes
0.25/	router_model.pt	Router trained for the cost-accuracy trade-off = 0.25.
0.5/	router_model.pt	Router trained for the cost-accuracy trade-off = 0.5.
0.75/	router_model.pt	Router trained for the cost-accuracy trade-off = 0.75
1.0/	router_model.pt	Router trained for the cost-accuracy trade-off = 1.0

Each checkpoint contains:

• state_dict: PyTorch weights for RouterScoringModel
• model_name: base encoder identifier (defaults to sentence-transformers/all-MiniLM-L6-v2)
• projection_dim: dimension of the projection head
• methods: serialized reasoning configurations; each row corresponds to one column in the router head

Quickstart (Python)

Install the package locally:

git clone https://github.com/nguyenngocbaocmt02/epic.git
cd EPIC
pip install -e .

Load a checkpoint and route a question:

from huggingface_hub import hf_hub_download
import torch
from epic.router.models import RouterScoringModel, MiniLMQuestionEncoder, QuestionProjector
from epic.data_schemas.reasoning import ReasoningConfig

REPO_ID = "baonn/epic"
VERSION = "1.0"  # or 0.5 / 0.75 / 0.25

checkpoint_path = hf_hub_download(
    repo_id=REPO_ID,
    filename="router_model.pt",
    subfolder=VERSION,
)
checkpoint = torch.load(checkpoint_path, map_location="cpu")

encoder = MiniLMQuestionEncoder(
    model_name=checkpoint.get("model_name", "sentence-transformers/all-MiniLM-L6-v2"),
    trainable=False,
)
projector = QuestionProjector(
    input_dim=encoder.transformer.config.hidden_size,
    projection_dim=int(checkpoint["projection_dim"]),
)
model = RouterScoringModel(
    question_encoder=encoder,
    projector=projector,
    num_methods=len(checkpoint["methods"]),
)
model.load_state_dict(checkpoint["state_dict"])
model.eval()

reasoning_configs = [
    ReasoningConfig.deserialize(payload) for payload in checkpoint["methods"]
]

questions = ["x + 20 = 30 then x = ?", "How many positive divisors does 3600 have?"]
with torch.no_grad():
    logits = model(questions)
    method_indices = torch.argmax(logits, dim=1).tolist()

print("Recommended config for question 1:", reasoning_configs[method_indices[0]].serialize(include_samples=True))
print("Recommended config for question 2:", reasoning_configs[method_indices[1]].serialize(include_samples=True))