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"""Build a tiny, deterministic random-init glm5_next TEXT-stack fixture (stdlib only).
Purpose: zai-org/GLM-5.3-Flash (released 2026-08-25) is a large multimodal
Glm5NextForConditionalGeneration MoE, so nobody can instantiate it in CI or on a
laptop. This fixture ships a ~0.3 MB random-init TEXT checkpoint plus a reduced,
nested config (model_type glm5_next, text_config with the GLM MoE schema) so
loaders, quant planners, and CI jobs can exercise the new architecture's config
parsing, expert-table sizing, and safetensors load path without the real weights.
Random-init: NOT trained and NOT a quality claim. Weight names are a reduced
text-only convention (see README omissions); a full multimodal loader must supply
vision/projector tensors and remap names. Geometry is documented in the README.
"""
import hashlib
import json
import math
import os
import struct
M64 = (1 << 64) - 1
SEED = 20260902
SCALE = 0.02
# ---- tiny geometry (reduced from the real text_config, documented in README) ----
VOCAB = 256
HIDDEN = 64
LAYERS = 4
HEADS = 4
KV_HEADS = 4
HEAD_DIM = 16
DENSE_INTER = 128
MOE_INTER = 32
N_ROUTED = 8
TOPK = 2
N_SHARED = 1
FIRST_DENSE = 1
N_GROUP = 1
class SplitMix64:
"""SplitMix64 + Box-Muller, identical to the llama/t5/glm_moe_dsa fixtures."""
def __init__(self, seed):
self.state = seed & M64
self._spare = None
def next_u64(self):
self.state = (self.state + 0x9E3779B97F4A7C15) & M64
z = self.state
z = ((z ^ (z >> 30)) * 0xBF584A7F17C119E3) & M64
z = ((z ^ (z >> 27)) * 0x94D049BB133111EB) & M64
return z ^ (z >> 31)
def uniform(self):
return (self.next_u64() >> 11) / float(1 << 53)
def gauss(self):
if self._spare is not None:
value, self._spare = self._spare, None
return value
u1 = 1.0 - self.uniform()
u2 = self.uniform()
radius = math.sqrt(-2.0 * math.log(u1))
theta = 2.0 * math.pi * u2
self._spare = radius * math.sin(theta)
return radius * math.cos(theta)
def build_tensors():
shapes = {
"model.embed_tokens.weight": (VOCAB, HIDDEN),
"model.norm.weight": (HIDDEN,),
}
ones = {"model.norm.weight"}
for layer in range(LAYERS):
p = "model.layers.%d." % layer
shapes[p + "input_layernorm.weight"] = (HIDDEN,)
shapes[p + "post_attention_layernorm.weight"] = (HIDDEN,)
ones.add(p + "input_layernorm.weight")
ones.add(p + "post_attention_layernorm.weight")
shapes[p + "self_attn.q_proj.weight"] = (HEADS * HEAD_DIM, HIDDEN)
shapes[p + "self_attn.k_proj.weight"] = (KV_HEADS * HEAD_DIM, HIDDEN)
shapes[p + "self_attn.v_proj.weight"] = (KV_HEADS * HEAD_DIM, HIDDEN)
shapes[p + "self_attn.o_proj.weight"] = (HIDDEN, HEADS * HEAD_DIM)
if layer < FIRST_DENSE:
shapes[p + "mlp.gate_proj.weight"] = (DENSE_INTER, HIDDEN)
shapes[p + "mlp.up_proj.weight"] = (DENSE_INTER, HIDDEN)
shapes[p + "mlp.down_proj.weight"] = (HIDDEN, DENSE_INTER)
else:
shapes[p + "mlp.gate.weight"] = (N_ROUTED, HIDDEN)
for expert in range(N_ROUTED):
e = p + "mlp.experts.%d." % expert
shapes[e + "gate_proj.weight"] = (MOE_INTER, HIDDEN)
shapes[e + "up_proj.weight"] = (MOE_INTER, HIDDEN)
shapes[e + "down_proj.weight"] = (HIDDEN, MOE_INTER)
shapes[p + "mlp.shared_experts.gate_proj.weight"] = (MOE_INTER, HIDDEN)
shapes[p + "mlp.shared_experts.up_proj.weight"] = (MOE_INTER, HIDDEN)
shapes[p + "mlp.shared_experts.down_proj.weight"] = (HIDDEN, MOE_INTER)
rng = SplitMix64(SEED)
out = {}
for name in sorted(shapes):
shape = shapes[name]
count = 1
for dim in shape:
count *= dim
if name in ones:
values = [1.0] * count
else:
values = [rng.gauss() * SCALE for _ in range(count)]
blob = b"".join(
struct.pack("<f", struct.unpack("<f", struct.pack("<f", v))[0]) for v in values
)
out[name] = (list(shape), "F32", blob)
return out
def write_safetensors(path, tensors, metadata):
header = {"__metadata__": metadata}
offset = 0
blobs = []
for name in sorted(tensors):
shape, dtype, blob = tensors[name]
header[name] = {"dtype": dtype, "shape": shape,
"data_offsets": [offset, offset + len(blob)]}
offset += len(blob)
blobs.append(blob)
raw = json.dumps(header, separators=(",", ":")).encode("utf-8")
raw += b" " * ((-len(raw)) % 8)
with open(path, "wb") as handle:
handle.write(struct.pack("<Q", len(raw)))
handle.write(raw)
for blob in blobs:
handle.write(blob)
return len(raw), offset
def main():
out_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"glm5_next_tiny_fixture")
os.makedirs(out_dir, exist_ok=True)
tensors = build_tensors()
metadata = {
"format": "pt",
"source": "usefulHuggingface",
"generator": "SplitMix64 seed=%d Box-Muller scale=%s float32 row-major" % (SEED, SCALE),
}
header_len, data_len = write_safetensors(
os.path.join(out_dir, "model.safetensors"), tensors, metadata)
text_config = {
"attention_bias": False,
"attention_dropout": 0.0,
"dtype": "float32",
"first_k_dense_replace": FIRST_DENSE,
"head_dim": HEAD_DIM,
"hidden_act": "silu",
"hidden_size": HIDDEN,
"intermediate_size": DENSE_INTER,
"max_position_embeddings": 256,
"model_type": "glm_moe_dsa",
"moe_intermediate_size": MOE_INTER,
"moe_layer_freq": 1,
"moe_router_dtype": "float32",
"n_group": N_GROUP,
"n_routed_experts": N_ROUTED,
"n_shared_experts": N_SHARED,
"norm_topk_prob": True,
"num_attention_heads": HEADS,
"num_experts_per_tok": TOPK,
"num_hidden_layers": LAYERS,
"num_key_value_heads": KV_HEADS,
"num_nextn_predict_layers": 0,
"pad_token_id": 0,
"rms_norm_eps": 1e-5,
"rope_theta": 10000.0,
"tie_word_embeddings": False,
"vocab_size": VOCAB,
}
config = {
"architectures": ["Glm5NextForConditionalGeneration"],
"image_token_id": 248056,
"language_model_only": True,
"model_type": "glm5_next",
"text_config": text_config,
"vision_config": {
"model_type": "glm5_next_vision",
"_note": "placeholder; this fixture ships NO vision/projector tensors",
},
}
with open(os.path.join(out_dir, "config.json"), "w") as handle:
json.dump(config, handle, indent=2, sort_keys=True)
handle.write("\n")
with open(os.path.join(out_dir, "generation_config.json"), "w") as handle:
json.dump({"bos_token_id": 1, "eos_token_id": 2, "pad_token_id": 0,
"no_repeat_ngram_size": 4, "seed": SEED},
handle, indent=2, sort_keys=True)
handle.write("\n")
with open(os.path.join(out_dir, "tokenizer_config.json"), "w") as handle:
json.dump({"model_max_length": 256, "bos_token": "<s>", "eos_token": "</s>",
"unk_token": "<unk>", "pad_token": "<pad>",
"model_input_names": ["input_ids"]},
handle, indent=2, sort_keys=True)
handle.write("\n")
with open(os.path.join(out_dir, "special_tokens_map.json"), "w") as handle:
json.dump({"additional_special_tokens": ["<pad>", "<unk>"],
"bos_token": "<s>", "eos_token": "</s>",
"pad_token": "<pad>", "unk_token": "<unk>"},
handle, indent=2, sort_keys=True)
handle.write("\n")
lines = []
for name in sorted(tensors):
shape, dtype, blob = tensors[name]
lines.append("%s %s %s %d %s" % (name, dtype, "x".join(map(str, shape)),
len(blob), hashlib.sha256(blob).hexdigest()))
with open(os.path.join(out_dir, "checksums.txt"), "w") as handle:
handle.write("\n".join(lines) + "\n")
print("header_len=%d data_len=%d tensors=%d" % (header_len, data_len, len(tensors)))
print("total_params=%d" % (data_len // 4))
if __name__ == "__main__":
main()
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