Fix: expert weights are FP4 (E2M1), not INT4 - verified with nibble analysis
Nibble index 0 vs 8 ratio = 0.996 (FP4 -0.0 ≈ +0.0), NOT INT4 where -8 would be rare. FP4 dequant uses E2M1 LUT lookup × E8M0 scale (MXFP4 microscaling). Also adds model_opt_nvfp4_full.py for full model NVFP4 quantization.
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@@ -8,11 +8,13 @@ Handles ALL compressed tensor types found in the mixed-precision model:
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- weight × scale_expanded → BF16
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- 128×128 block quantization
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2. INT4 expert weights (int8 packed + float8_e8m0fnu block scales)
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- Unpack 2 int4 values per int8 byte (lower nibble first, upper second)
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- Dequantize: int4_signed × scale_expanded → BF16
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- Per-row, 32-column block scaling
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2. FP4 (E2M1) expert weights (int8 packed + float8_e8m0fnu block scales)
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- Unpack 2 FP4 values per int8 byte (lower nibble first, upper second)
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- Dequantize via E2M1 LUT lookup × scale_expanded → BF16
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- Per-row, 32-column block scaling (MXFP4 microscaling format)
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- Output dimensions are 2× the stored dimensions
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- Verified: nibble index 0 vs 8 ratio = 0.996 (FP4 -0.0 vs +0.0),
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NOT INT4 where index 8 = -8 would be rare
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3. FP8 shared expert weights (float8_e4m3fn + float8_e8m0fnu block scales)
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- Same as FP8 attention dequantization
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@@ -31,7 +33,16 @@ import torch
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FP8_WEIGHT_DTYPE = torch.float8_e4m3fn
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FP8_SCALE_DTYPE = torch.float8_e8m0fnu
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BLOCK_SIZE_FP8 = (128, 128)
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INT4_BLOCK_SIZE = 32 # columns per scale value for INT4 expert weights
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FP4_BLOCK_SIZE = 32 # columns per scale value for MXFP4 expert weights
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# E2M1 FP4 lookup table (MXFP4 microscaling format)
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# Index 0-7: positive values (sign=0, 2-bit exp, 1-bit mantissa)
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# Index 8-15: negative values (sign=1)
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# Mapping: 0→0, 1→0.5, 2→1, 3→1.5, 4→2, 5→3, 6→4, 7→6
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FP4_E2M1_LUT = torch.tensor([
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0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0,
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-0.0, -0.5, -1.0, -1.5, -2.0, -3.0, -4.0, -6.0,
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], dtype=torch.float32)
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def dequantize_fp8_weight(fp8_weight: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
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@@ -48,42 +59,44 @@ def dequantize_fp8_weight(fp8_weight: torch.Tensor, scale: torch.Tensor) -> torc
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return weight_bf16.to(torch.bfloat16)
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def dequantize_int4_weight(int8_packed: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
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"""Dequantize INT4-packed expert weight to BF16.
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def dequantize_fp4_weight(int8_packed: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
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"""Dequantize MXFP4 (E2M1) expert weight to BF16.
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INT4 values are packed 2-per-byte into int8 tensors.
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FP4 values are packed 2-per-byte into int8 tensors.
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Lower nibble (bits 0-3) is the first value, upper nibble (bits 4-7) is the second.
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Signed int4 range: -8 to 7.
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E2M1 format: 1 sign + 2 exponent + 1 mantissa bit.
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Scale is per-row with 32-column blocks (float8_e8m0fnu).
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Scale is per-row with 32-column blocks (float8_e8m0fnu, MX microscaling).
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Output dimensions are 2× the stored dimensions.
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int8_packed: (out_features, in_features//2) int8
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scale: (out_features, in_features//32) float8_e8m0fnu
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returns: (out_features, in_features) bfloat16
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"""
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# Unpack int4 from int8
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lower = (int8_packed & 0x0F).to(torch.int8) # 0-15
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upper = ((int8_packed >> 4) & 0x0F).to(torch.int8) # 0-15
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lut = FP4_E2M1_LUT.to(int8_packed.device)
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# Convert unsigned to signed int4: 0-7 stay, 8-15 → -8 to -1
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lower_signed = torch.where(lower > 7, lower - 16, lower).float()
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upper_signed = torch.where(upper > 7, upper - 16, upper).float()
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# Unpack nibble indices
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lower_idx = (int8_packed & 0x0F).long() # 0-15
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upper_idx = ((int8_packed >> 4) & 0x0F).long() # 0-15
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# LUT lookup
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lower = lut[lower_idx] # float32
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upper = lut[upper_idx] # float32
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out_features = int8_packed.shape[0]
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in_features_full = int8_packed.shape[1] * 2 # 2× expansion
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# Expand scale: (out_features, in_features//32) → (out_features, in_features)
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scale_f32 = scale.float()
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scale_expanded = scale_f32.repeat_interleave(INT4_BLOCK_SIZE, dim=1)
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scale_expanded = scale_f32.repeat_interleave(FP4_BLOCK_SIZE, dim=1)
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scale_expanded = scale_expanded[:, :in_features_full]
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# Interleave lower and upper nibbles
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unpacked = torch.zeros(out_features, in_features_full, dtype=torch.float32)
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unpacked[:, 0::2] = lower_signed
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unpacked[:, 1::2] = upper_signed
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unpacked = torch.empty(out_features, in_features_full, dtype=torch.float32, device=int8_packed.device)
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unpacked[:, 0::2] = lower
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unpacked[:, 1::2] = upper
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# Dequantize
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# Dequantize: FP4 value × E8M0 scale
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bf16_weight = (unpacked * scale_expanded).to(torch.bfloat16)
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return bf16_weight
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@@ -104,9 +117,8 @@ def dequantize_model(model_dir: str, out_dir: str):
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print(f"Found {total_shards} shards")
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# First pass: build scale-key → weight-key mapping
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# Pattern: *.scale → *.weight
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print("\nScanning for weight+scale pairs...")
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scale_to_weight = {} # scale_key → weight_key
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scale_to_weight = {}
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for f in safetensor_files:
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with safe_open(f, framework="pt") as sf:
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for key in sf.keys():
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@@ -114,37 +126,35 @@ def dequantize_model(model_dir: str, out_dir: str):
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weight_key = key[:-len(".scale")] + ".weight"
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scale_to_weight[key] = weight_key
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# Also find weight → scale mapping
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weight_to_scale = {v: k for k, v in scale_to_weight.items()}
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print(f"Found {len(scale_to_weight)} weight+scale pairs")
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# Classify weights by type
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int4_weight_keys = set()
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# Classify weights by type (sample first 2 shards)
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fp4_weight_keys = set()
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fp8_weight_keys = set()
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scale_keys = set(scale_to_weight.keys())
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for f in safetensor_files[:2]: # Sample to classify
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for f in safetensor_files[:2]:
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with safe_open(f, framework="pt") as sf:
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for key in sf.keys():
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if key in weight_to_scale:
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t = sf.get_tensor(key)
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if t.dtype == torch.int8:
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int4_weight_keys.add(key)
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fp4_weight_keys.add(key)
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elif t.dtype == FP8_WEIGHT_DTYPE:
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fp8_weight_keys.add(key)
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print(f" INT4 expert weights (packed): ~{len(int4_weight_keys)} per shard")
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print(f" FP4 (E2M1) expert weights (packed): ~{len(fp4_weight_keys)} per shard")
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print(f" FP8 attention/shared-expert weights: ~{len(fp8_weight_keys)} per shard")
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# Second pass: dequantize and save
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stats = {"int4_dequantized": 0, "fp8_dequantized": 0, "scales_removed": 0, "unchanged": 0}
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stats = {"fp4_dequantized": 0, "fp8_dequantized": 0, "scales_removed": 0, "unchanged": 0}
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start_time = time.time()
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for i, f in enumerate(safetensor_files):
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shard_start = time.time()
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tensors = {}
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scales_in_shard = {}
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weights_to_dequant = {}
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with safe_open(f, framework="pt") as sf:
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keys = list(sf.keys())
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@@ -163,16 +173,16 @@ def dequantize_model(model_dir: str, out_dir: str):
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t = sf.get_tensor(key)
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if key in weight_to_scale and t.dtype == torch.int8:
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# INT4 packed expert weight
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# FP4 (E2M1) packed expert weight (MXFP4 microscaling)
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scale_key = weight_to_scale[key]
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scale = scales_in_shard.get(scale_key)
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if scale is None:
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print(f" WARNING: scale {scale_key} not in same shard as {key}")
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tensors[key] = t # keep as-is
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continue
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bf16 = dequantize_int4_weight(t, scale)
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bf16 = dequantize_fp4_weight(t, scale)
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tensors[key] = bf16
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stats["int4_dequantized"] += 1
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stats["fp4_dequantized"] += 1
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del scales_in_shard[scale_key]
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stats["scales_removed"] += 1
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@@ -208,8 +218,8 @@ def dequantize_model(model_dir: str, out_dir: str):
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eta = (total_shards - i - 1) / rate if rate > 0 else 0
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print(f"[{i+1}/{total_shards}] {os.path.basename(f)} "
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f"({stats['int4_dequantized']} int4, {stats['fp8_dequantized']} fp8, "
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f"{stats['scales_removed']} scales removed) "
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f"({stats['fp4_dequantized']} fp4, {stats['fp8_dequantized']} fp8, "
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f"{stats['scales_removed']} scales rm) "
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f"[{shard_time:.1f}s, ETA: {eta/60:.0f}min]")
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del tensors, scales_in_shard
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@@ -227,7 +237,7 @@ def dequantize_model(model_dir: str, out_dir: str):
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total_time = time.time() - start_time
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print(f"\nDone in {total_time/60:.1f} minutes!")
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print(f" INT4 expert weights dequantized: {stats['int4_dequantized']}")
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print(f" FP4 expert weights dequantized: {stats['fp4_dequantized']}")
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print(f" FP8 weights dequantized: {stats['fp8_dequantized']}")
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print(f" Scale tensors removed: {stats['scales_removed']}")
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print(f" Unchanged tensors: {stats['unchanged']}")
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@@ -244,7 +254,7 @@ def dequantize_model(model_dir: str, out_dir: str):
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if remaining_compressed <= 5:
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print(f" REMAINING: {key} {t.dtype} {t.shape}")
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if remaining_compressed == 0:
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print(" ✅ No compressed tensors remaining - model is pure BF16!")
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print(" ✅ No compressed tensors remaining — model is pure BF16!")
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else:
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print(f" ⚠️ {remaining_compressed} compressed tensors still present")
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