def create_model(source_model_id: str, source_revision: str, save_folder: str) -> str:
    import copy
    import json
    from pathlib import Path

    import torch
    from huggingface_hub import hf_hub_download
    from safetensors.torch import save_file
    from transformers import AutoConfig, AutoModelForImageTextToText, set_seed

    folder = Path(save_folder)
    folder.mkdir(parents=True, exist_ok=True)
    # Transformers 5.18 loads this custom Loop/mHC wrapper around the native Qwen3.5 backbone; ship it pinned.
    for name in (
        "modeling_sensenova_loop.py",
        "chat_template.jinja",
        "processor_config.json",
        "preprocessor_config.json",
        "video_preprocessor_config.json",
        "tokenizer.json",
        "tokenizer_config.json",
    ):
        (folder / name).write_bytes(Path(hf_hub_download(source_model_id, name, revision=source_revision)).read_bytes())

    config = json.loads(Path(hf_hub_download(source_model_id, "config.json", revision=source_revision)).read_text())
    # Pre-loop 0-3, looped block 4-7 executed twice, post-loop 8-11: twelve physical layers / sixteen executions,
    # four complete linear/linear/linear/full cycles. Residual width 8, head dim 32 with eight Q / four KV heads.
    text_config = config["text_config"]
    text_config.update(
        head_dim=32,
        hidden_size=8,
        intermediate_size=32,
        num_hidden_layers=12,
        num_attention_heads=8,
        num_key_value_heads=4,
        linear_key_head_dim=32,
        linear_num_key_heads=4,
        linear_num_value_heads=8,
        linear_value_head_dim=32,
    )
    text_config["layer_types"] = ["linear_attention", "linear_attention", "linear_attention", "full_attention"] * 3
    text_config["rope_parameters"]["mrope_section"] = [1, 1, 2]
    # Two-stream mHC spans exactly the looped block [4, 8), matching the source [4, 28) cadence.
    config["loop_settings"] = {"counts": [2], "end": 8, "eval_count": 2, "start": 4}
    config["mhc"] = {
        "end": 8,
        "fixed_identity": False,
        "iterations": 20,
        "pre_scale": 1.0,
        "start": 4,
        "streams": 2,
    }
    config["hidden_size"] = 8
    # Four vision blocks keep the native Qwen3.5 tower and the fixed patch/temporal/merge and position contracts.
    config["vision_config"].update(
        hidden_size=64,
        intermediate_size=128,
        num_heads=2,
        out_hidden_size=8,
        depth=4,
    )
    (folder / "config.json").write_text(json.dumps(config, indent=2) + "\n")

    set_seed(42)
    model = AutoModelForImageTextToText.from_config(
        AutoConfig.from_pretrained(folder, trust_remote_code=True),
        trust_remote_code=True,
        dtype=torch.bfloat16,
    )
    # The source keeps the hybrid linear-attention SSM parameters FP32 even under a BF16 load.
    from transformers.models.qwen3_5.modeling_qwen3_5 import Qwen3_5GatedDeltaNet

    for layer in model.model.language_model.layers:
        linear = getattr(layer, "linear_attn", None)
        if isinstance(linear, Qwen3_5GatedDeltaNet):
            with torch.no_grad():
                linear.A_log = torch.nn.Parameter(linear.A_log.float())
                linear.dt_bias = torch.nn.Parameter(linear.dt_bias.float())
                linear.norm.weight = torch.nn.Parameter(linear.norm.weight.float())
                linear.conv1d.weight = torch.nn.Parameter(linear.conv1d.weight.float())
    # One BF16 MTP draft layer reusing a full-attention backbone layer outside the mHC range.
    full_layer = model.model.language_model.layers[11]
    model.mtp = torch.nn.ModuleDict(
        {
            "pre_fc_norm_embedding": torch.nn.RMSNorm(8),
            "fc": torch.nn.Linear(16, 8, bias=False),
            "layers": torch.nn.ModuleList([copy.deepcopy(full_layer)]),
            "norm": torch.nn.RMSNorm(8),
            "pre_fc_norm_hidden": torch.nn.RMSNorm(8),
        }
    )
    model = model.cpu()
    representation = str(model)

    state = model.state_dict()
    backbone, draft = {}, {}
    for name, tensor in state.items():
        if name.startswith("mtp."):
            draft[name] = tensor.to(torch.bfloat16).contiguous()
        else:
            # The shipped checkpoint stores the complete backbone FP32, mHC routers included.
            backbone[name] = tensor.to(torch.float32).contiguous()
    save_file(backbone, str(folder / "model.safetensors"), metadata={"format": "pt"})
    save_file(draft, str(folder / "mtp.safetensors"), metadata={"format": "pt"})
    # Rewrite the edited config (save was manual) and pin the source generation config bytes.
    (folder / "config.json").write_text(json.dumps(config, indent=2) + "\n")
    (folder / "generation_config.json").write_bytes(
        Path(hf_hub_download(source_model_id, "generation_config.json", revision=source_revision)).read_bytes()
    )

    expected_draft = {
        "mtp.fc.weight",
        "mtp.pre_fc_norm_embedding.weight",
        "mtp.pre_fc_norm_hidden.weight",
        "mtp.norm.weight",
        "mtp.layers.0.input_layernorm.weight",
        "mtp.layers.0.post_attention_layernorm.weight",
        "mtp.layers.0.self_attn.q_proj.weight",
        "mtp.layers.0.self_attn.k_proj.weight",
        "mtp.layers.0.self_attn.v_proj.weight",
        "mtp.layers.0.self_attn.o_proj.weight",
        "mtp.layers.0.self_attn.q_norm.weight",
        "mtp.layers.0.self_attn.k_norm.weight",
        "mtp.layers.0.mlp.gate_proj.weight",
        "mtp.layers.0.mlp.up_proj.weight",
        "mtp.layers.0.mlp.down_proj.weight",
    }
    assert set(draft) == expected_draft, sorted(set(draft) ^ expected_draft)
    assert all(
        name.count("mhc_attn") + name.count("mhc_ffn") == 0 or backbone[name].dtype == torch.float32
        for name in backbone
    )
    return representation


create_model(
    source_model_id="sensenova/SenseNova-Loop-9B-Preview",
    source_revision="9937e91c7301b7dcbb5bf17e3e4e95c499a11e99",
    save_folder="/tmp/sensenova-loop",
)