ffront/qwen1_5_moe_pruned

Pruned Qwen1.5 MoE (thin wrapper)

This repository is a thin wrapper that loads base weights from Qwen/Qwen1.5-MoE-A2.7B and masks selected experts per layer during inference routing.

Usage

from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "ffront/qwen1_5_moe_pruned",
    trust_remote_code=True,
    pruned_experts_by_layer={
        "*": [0],
        5: [1, 2, 3],
    },
)

Behavior

• Pruned experts are masked before softmax/top-k in sparse MoE routing.
• Masked experts do not occupy top-k slots.
• Base model weights are loaded from Qwen/Qwen1.5-MoE-A2.7B at load time.
• This wrapper does not zero expert weights.

Publish to Hugging Face Hub

1. Create repository: https://huggingface.co/new?name=qwen1_5_moe_pruned&organization=Ffr0nt
2. Add files from this folder to repo root.
3. Push with git or upload in web UI.
4. Validate loading using the usage snippet above.