254 lines
10 KiB
Python
Executable File
254 lines
10 KiB
Python
Executable File
#!/usr/bin/env python3
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from __future__ import annotations
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import logging
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import argparse
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import os
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import sys
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from pathlib import Path
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from typing import Any
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import numpy as np
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# Necessary to load the local gguf package
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if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from gguf import GGUFReader, GGUFValueType # noqa: E402
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logger = logging.getLogger("gguf-dump")
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def get_file_host_endian(reader: GGUFReader) -> tuple[str, str]:
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host_endian = 'LITTLE' if np.uint32(1) == np.uint32(1).newbyteorder("<") else 'BIG'
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if reader.byte_order == 'S':
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file_endian = 'BIG' if host_endian == 'LITTLE' else 'LITTLE'
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else:
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file_endian = host_endian
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return (host_endian, file_endian)
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# For more information about what field.parts and field.data represent,
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# please see the comments in the modify_gguf.py example.
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def dump_metadata(reader: GGUFReader, args: argparse.Namespace) -> None:
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host_endian, file_endian = get_file_host_endian(reader)
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print(f'* File is {file_endian} endian, script is running on a {host_endian} endian host.') # noqa: NP100
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print(f'* Dumping {len(reader.fields)} key/value pair(s)') # noqa: NP100
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for n, field in enumerate(reader.fields.values(), 1):
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if not field.types:
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pretty_type = 'N/A'
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elif field.types[0] == GGUFValueType.ARRAY:
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nest_count = len(field.types) - 1
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pretty_type = '[' * nest_count + str(field.types[-1].name) + ']' * nest_count
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else:
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pretty_type = str(field.types[-1].name)
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log_message = f' {n:5}: {pretty_type:10} | {len(field.data):8} | {field.name}'
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if len(field.types) == 1:
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curr_type = field.types[0]
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if curr_type == GGUFValueType.STRING:
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log_message += ' = {0}'.format(repr(str(bytes(field.parts[-1]), encoding='utf-8')[:60]))
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elif field.types[0] in reader.gguf_scalar_to_np:
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log_message += ' = {0}'.format(field.parts[-1][0])
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print(log_message) # noqa: NP100
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if args.no_tensors:
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return
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print(f'* Dumping {len(reader.tensors)} tensor(s)') # noqa: NP100
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for n, tensor in enumerate(reader.tensors, 1):
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prettydims = ', '.join('{0:5}'.format(d) for d in list(tensor.shape) + [1] * (4 - len(tensor.shape)))
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print(f' {n:5}: {tensor.n_elements:10} | {prettydims} | {tensor.tensor_type.name:7} | {tensor.name}') # noqa: NP100
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def dump_metadata_json(reader: GGUFReader, args: argparse.Namespace) -> None:
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import json
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host_endian, file_endian = get_file_host_endian(reader)
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metadata: dict[str, Any] = {}
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tensors: dict[str, Any] = {}
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result = {
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"filename": args.model,
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"endian": file_endian,
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"metadata": metadata,
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"tensors": tensors,
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}
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for idx, field in enumerate(reader.fields.values()):
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curr: dict[str, Any] = {
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"index": idx,
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"type": field.types[0].name if field.types else 'UNKNOWN',
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"offset": field.offset,
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}
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metadata[field.name] = curr
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if field.types[:1] == [GGUFValueType.ARRAY]:
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curr["array_types"] = [t.name for t in field.types][1:]
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if not args.json_array:
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continue
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itype = field.types[-1]
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if itype == GGUFValueType.STRING:
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curr["value"] = [str(bytes(field.parts[idx]), encoding="utf-8") for idx in field.data]
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else:
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curr["value"] = [pv for idx in field.data for pv in field.parts[idx].tolist()]
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elif field.types[0] == GGUFValueType.STRING:
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curr["value"] = str(bytes(field.parts[-1]), encoding="utf-8")
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else:
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curr["value"] = field.parts[-1].tolist()[0]
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if not args.no_tensors:
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for idx, tensor in enumerate(reader.tensors):
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tensors[tensor.name] = {
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"index": idx,
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"shape": tensor.shape.tolist(),
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"type": tensor.tensor_type.name,
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"offset": tensor.field.offset,
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}
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json.dump(result, sys.stdout)
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def element_count_rounded_notation(count: int) -> str:
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if count > 1e15 :
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# Quadrillion
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scaled_amount = count * 1e-15
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scale_suffix = "Q"
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elif count > 1e12 :
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# Trillions
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scaled_amount = count * 1e-12
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scale_suffix = "T"
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elif count > 1e9 :
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# Billions
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scaled_amount = count * 1e-9
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scale_suffix = "B"
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elif count > 1e6 :
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# Millions
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scaled_amount = count * 1e-6
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scale_suffix = "M"
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elif count > 1e3 :
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# Thousands
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scaled_amount = count * 1e-3
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scale_suffix = "K"
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else:
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# Under Thousands
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scaled_amount = count
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scale_suffix = ""
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return f"{'~' if count > 1e3 else ''}{round(scaled_amount)}{scale_suffix}"
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def translate_tensor_name(name):
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import re
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words = re.split(r"[._]", name)
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abbreviation_dictionary = {
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'ffn' : 'Feed Forward',
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'attn' : 'Attention',
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'blk' : 'Block',
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'norm' : 'Normalization',
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'embd' : 'Embedding',
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}
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expanded_words = []
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for word in words:
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word_norm = word.strip().lower()
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if word_norm in abbreviation_dictionary:
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expanded_words.append(abbreviation_dictionary[word_norm])
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else:
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expanded_words.append(word.title())
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return ' '.join(expanded_words)
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def dump_markdown_metadata(reader: GGUFReader, args: argparse.Namespace) -> None:
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host_endian, file_endian = get_file_host_endian(reader)
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print(f'# {args.model} - GGUF Internal File Dump') # noqa: NP100
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print(f'* Endian: {file_endian} endian') # noqa: NP100
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print('') # noqa: NP100
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print('## Key Value Metadata Store') # noqa: NP100
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print(f'There is {len(reader.fields)} key/value pair(s) in this file') # noqa: NP100
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print('') # noqa: NP100
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print('| POS | TYPE | Elements | Key | Value |') # noqa: NP100
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print('|-----|------------|----------|----------------------------------------|--------------------------------------------------------------------------------|') # noqa: NP100
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for n, field in enumerate(reader.fields.values(), 1):
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if not field.types:
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pretty_type = 'N/A'
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elif field.types[0] == GGUFValueType.ARRAY:
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nest_count = len(field.types) - 1
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pretty_type = '[' * nest_count + str(field.types[-1].name) + ']' * nest_count
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else:
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pretty_type = str(field.types[-1].name)
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if len(field.types) == 1:
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curr_type = field.types[0]
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if curr_type == GGUFValueType.STRING:
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value = repr(str(bytes(field.parts[-1]), encoding='utf-8')[:60])
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elif field.types[0] in reader.gguf_scalar_to_np:
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value = field.parts[-1][0]
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print(f'| {n:3} | {pretty_type:10} | {len(field.data):8} | {field.name:38} | {value:<78} |') # noqa: NP100
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print("\n") # noqa: NP100
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if not args.no_tensors:
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# Group tensors by their prefix and maintain order
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tensor_prefix_order = []
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tensor_groups = {}
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total_elements = sum(tensor.n_elements for tensor in reader.tensors)
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for tensor in reader.tensors:
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tensor_name = tensor.name.replace(".weight", "")
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tensor_components = tensor_name.split('.')
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tensor_prefix = tensor_components[0]
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if tensor_prefix == 'blk':
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tensor_prefix = f"{tensor_components[0]}.{tensor_components[1]}"
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if tensor_prefix not in tensor_groups:
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tensor_groups[tensor_prefix] = []
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tensor_prefix_order.append(tensor_prefix)
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tensor_groups[tensor_prefix].append(tensor)
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# Generate Markdown metadata
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for group in tensor_prefix_order:
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tensors = tensor_groups[group]
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group_elements = sum(tensor.n_elements for tensor in tensors)
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group_percentage = group_elements / total_elements * 100
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print(f"## {translate_tensor_name(group)} Tensor Group : {element_count_rounded_notation(group_elements)} Elements") # noqa: NP100
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print("| Tensor Name | Human Friendly Name | Elements | Shape | Type |") # noqa: NP100
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print("|----------------------|-------------------------------------|----------------|---------------------------------|------|") # noqa: NP100
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for tensor in tensors:
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tensor_name = tensor.name.replace(".weight", "")
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human_friendly_name = translate_tensor_name(tensor.name.replace(".weight", ""))
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prettydims = ' x '.join('{0:^5}'.format(d) for d in list(tensor.shape) + [1] * (4 - len(tensor.shape)))
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print(f"| {tensor_name:20} | {human_friendly_name:35} | ({element_count_rounded_notation(tensor.n_elements):>4}) {tensor.n_elements:7} | [{prettydims:29}] | {tensor.tensor_type.name:4} |") # noqa: NP100
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print("") # noqa: NP100
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print(f"- Total elements in {group}: ({element_count_rounded_notation(group_elements):>4}) {group_elements}") # noqa: NP100
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print(f"- Percentage of total elements: {group_percentage:.2f}%") # noqa: NP100
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print("\n") # noqa: NP100
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def main() -> None:
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parser = argparse.ArgumentParser(description="Dump GGUF file metadata")
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parser.add_argument("model", type=str, help="GGUF format model filename")
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parser.add_argument("--no-tensors", action="store_true", help="Don't dump tensor metadata")
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parser.add_argument("--json", action="store_true", help="Produce JSON output")
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parser.add_argument("--json-array", action="store_true", help="Include full array values in JSON output (long)")
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parser.add_argument("--markdown", action="store_true", help="Produce markdown output")
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parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
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args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"])
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logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
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if not args.json and not args.markdown:
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logger.info(f'* Loading: {args.model}')
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reader = GGUFReader(args.model, 'r')
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if args.json:
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dump_metadata_json(reader, args)
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elif args.markdown:
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dump_markdown_metadata(reader, args)
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else:
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dump_metadata(reader, args)
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if __name__ == '__main__':
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main()
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