Given a natural-language service request and a retrieved service catalog context, the model generates a structured workflow DAG containing the required service functions and their execution dependencies.

HieraMind

HieraMind is a hierarchical framework for intent-aware service orchestration across heterogeneous network and computing infrastructures.

Training

The model was fine-tuned using group-based reinforcement learning with teacher-guided workflow evaluation. Semantic retrieval is used to restrict the service catalog provided to the model before workflow generation.

Evaluation

On the HieraMind I2W evaluation set, the model achieves approximately:

Node precision: 96.0%
Node recall: 92.6%

Further end-to-end and ablation results are reported in the HieraMind paper.

Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "mamadzebal/HieraMind-I2W-Qwen3-4B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)