File size: 3,379 Bytes
c99f13f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | import gguf
import numpy as np
from constants import ARCH_FEATURES
def detect_architecture(tensors: dict) -> str:
"""Detect model architecture from tensor names."""
names = list(tensors.keys())
has_ssm = any("ssm_" in n for n in names)
has_qkv = any("attn_qkv" in n for n in names)
has_nextn = any("nextn" in n for n in names)
has_moe = any("exps" in n for n in names)
has_separate_qkv = any("attn_q.weight" in n for n in names)
has_gemma_specific = any(
t in n for t in ("layer_output_scale", "post_attention_norm", "post_ffw_norm")
for n in names
)
if has_ssm and has_qkv:
return "qwen35"
if has_moe:
return "mellum2"
# Исправление: заменяем неопределённую has_blk_attn на уже существующий признак
if has_separate_qkv or has_gemma_specific:
return "gemma4"
return "unknown"
def _detect_prefix(tensors: dict) -> str:
for name in tensors:
if name.startswith("BLK."):
return "BLK"
return "blk"
def _estimate_layers(tensors: dict) -> int:
max_layer = 0
for name in tensors:
parts = name.split(".")
if len(parts) >= 2 and parts[0] in ("blk", "BLK"):
try:
layer = int(parts[1])
if layer > max_layer:
max_layer = layer
except ValueError:
pass
return max_layer + 1 # layers are 0-indexed
def read_model(path: str) -> dict:
"""Parse BF16 GGUF, return model info."""
r = gguf.GGUFReader(path)
tensors = {}
meta = {}
for k, v in r.fields.items():
# Безопасное извлечение данных (список/число, а не raw numpy)
try:
data = v.data
if isinstance(data, np.ndarray):
data = data.tolist()
elif isinstance(data, (np.generic,)):
data = data.item()
meta[k] = data
except Exception:
meta[k] = str(v)
for t in r.tensors:
shape = list(t.shape)
name = t.name
n_elements = int(np.prod(shape))
tensors[name] = {
"shape": shape,
"n_elements": n_elements,
"size_mib": n_elements * 2 / 1024 / 1024,
}
arch = detect_architecture(tensors)
arch_features = ARCH_FEATURES.get(arch, {}).copy()
prefix = _detect_prefix(tensors)
n_layers = _estimate_layers(tensors)
if arch == "mellum2" and arch_features.get("moe_intermediate_size", 0) == 0:
arch_features["moe_intermediate_size"] = 896
arch_features["prefix"] = prefix
if n_layers > 0:
arch_features["n_layers"] = n_layers
has_moe = any("exps" in n for n in tensors)
if has_moe:
arch_features["has_moe"] = True
has_nextn = any("nextn" in n for n in tensors)
# blk.32 is MTP only in ~32-layer models (Qwen). Skip for deeper models (Gemma4, 48 layers).
has_blk32 = n_layers <= 33 and any(
n.startswith("blk.32.") or n.startswith("BLK.32.") for n in tensors
)
arch_features["has_mtp"] = has_nextn or has_blk32
return {
"path": path,
"architecture": arch,
"features": arch_features,
"tensors": tensors,
"n_tensors": len(tensors),
"meta": meta,
}
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