if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
sys.path.insert(0, str(Path(__file__).parent.parent))
-from gguf import GGUFReader, GGUFValueType # noqa: E402
+from gguf import GGUFReader, GGUFValueType, ReaderTensor # noqa: E402
logger = logging.getLogger("gguf-dump")
json.dump(result, sys.stdout)
+def markdown_table_with_alignment_support(header_map: list[dict[str, str]], data: list[dict[str, Any]]):
+ # JSON to Markdown table formatting: https://stackoverflow.com/a/72983854/2850957
+
+ # Alignment Utility Function
+ def strAlign(padding: int, alignMode: str | None, strVal: str):
+ if alignMode == 'center':
+ return strVal.center(padding)
+ elif alignMode == 'right':
+ return strVal.rjust(padding - 1) + ' '
+ elif alignMode == 'left':
+ return ' ' + strVal.ljust(padding - 1)
+ else: # default left
+ return ' ' + strVal.ljust(padding - 1)
+
+ def dashAlign(padding: int, alignMode: str | None):
+ if alignMode == 'center':
+ return ':' + '-' * (padding - 2) + ':'
+ elif alignMode == 'right':
+ return '-' * (padding - 1) + ':'
+ elif alignMode == 'left':
+ return ':' + '-' * (padding - 1)
+ else: # default left
+ return '-' * (padding)
+
+ # Calculate Padding For Each Column Based On Header and Data Length
+ rowsPadding = {}
+ for index, columnEntry in enumerate(header_map):
+ padCount = max([len(str(v)) for d in data for k, v in d.items() if k == columnEntry['key_name']], default=0) + 2
+ headerPadCount = len(columnEntry['header_name']) + 2
+ rowsPadding[index] = headerPadCount if padCount <= headerPadCount else padCount
+
+ # Render Markdown Header
+ rows = []
+ rows.append('|'.join(strAlign(rowsPadding[index], columnEntry.get('align'), str(columnEntry['header_name'])) for index, columnEntry in enumerate(header_map)))
+ rows.append('|'.join(dashAlign(rowsPadding[index], columnEntry.get('align')) for index, columnEntry in enumerate(header_map)))
+
+ # Render Tabular Data
+ for item in data:
+ rows.append('|'.join(strAlign(rowsPadding[index], columnEntry.get('align'), str(item[columnEntry['key_name']])) for index, columnEntry in enumerate(header_map)))
+
+ # Convert Tabular String Rows Into String
+ tableString = ""
+ for row in rows:
+ tableString += f'|{row}|\n'
+
+ return tableString
+
+
+def element_count_rounded_notation(count: int) -> str:
+ if count > 1e15 :
+ # Quadrillion
+ scaled_amount = count * 1e-15
+ scale_suffix = "Q"
+ elif count > 1e12 :
+ # Trillions
+ scaled_amount = count * 1e-12
+ scale_suffix = "T"
+ elif count > 1e9 :
+ # Billions
+ scaled_amount = count * 1e-9
+ scale_suffix = "B"
+ elif count > 1e6 :
+ # Millions
+ scaled_amount = count * 1e-6
+ scale_suffix = "M"
+ elif count > 1e3 :
+ # Thousands
+ scaled_amount = count * 1e-3
+ scale_suffix = "K"
+ else:
+ # Under Thousands
+ scaled_amount = count
+ scale_suffix = ""
+ return f"{'~' if count > 1e3 else ''}{round(scaled_amount)}{scale_suffix}"
+
+
+def translate_tensor_name(name):
+ words = name.split(".")
+
+ # Source: https://github.com/ggerganov/ggml/blob/master/docs/gguf.md#standardized-tensor-names
+ abbreviation_dictionary = {
+ 'token_embd': 'Token embedding',
+ 'pos_embd': 'Position embedding',
+ 'output_norm': 'Output normalization',
+ 'output': 'Output',
+ 'attn_norm': 'Attention normalization',
+ 'attn_norm_2': 'Attention normalization',
+ 'attn_qkv': 'Attention query-key-value',
+ 'attn_q': 'Attention query',
+ 'attn_k': 'Attention key',
+ 'attn_v': 'Attention value',
+ 'attn_output': 'Attention output',
+ 'ffn_norm': 'Feed-forward network normalization',
+ 'ffn_up': 'Feed-forward network "up"',
+ 'ffn_gate': 'Feed-forward network "gate"',
+ 'ffn_down': 'Feed-forward network "down"',
+ 'ffn_gate_inp': 'Expert-routing layer for the Feed-forward network in Mixture of Expert models',
+ 'ffn_gate_exp': 'Feed-forward network "gate" layer per expert in Mixture of Expert models',
+ 'ffn_down_exp': 'Feed-forward network "down" layer per expert in Mixture of Expert models',
+ 'ffn_up_exp': 'Feed-forward network "up" layer per expert in Mixture of Expert models',
+ 'ssm_in': 'State space model input projections',
+ 'ssm_conv1d': 'State space model rolling/shift',
+ 'ssm_x': 'State space model selective parametrization',
+ 'ssm_a': 'State space model state compression',
+ 'ssm_d': 'State space model skip connection',
+ 'ssm_dt': 'State space model time step',
+ 'ssm_out': 'State space model output projection',
+ 'blk': 'Block'
+ }
+
+ expanded_words = []
+ for word in words:
+ word_norm = word.strip().lower()
+ if word_norm in abbreviation_dictionary:
+ expanded_words.append(abbreviation_dictionary[word_norm].title())
+ else:
+ expanded_words.append(word.title())
+
+ return ' '.join(expanded_words)
+
+
+def dump_markdown_metadata(reader: GGUFReader, args: argparse.Namespace) -> None:
+ host_endian, file_endian = get_file_host_endian(reader)
+ markdown_content = ""
+ markdown_content += f'# {args.model} - GGUF Internal File Dump\n\n'
+ markdown_content += f'- Endian: {file_endian} endian\n'
+ markdown_content += '\n'
+ markdown_content += '## Key Value Metadata Store\n\n'
+ markdown_content += f'There are {len(reader.fields)} key-value pairs in this file\n'
+ markdown_content += '\n'
+
+ kv_dump_table: list[dict[str, str | int]] = []
+ for n, field in enumerate(reader.fields.values(), 1):
+ if not field.types:
+ pretty_type = 'N/A'
+ elif field.types[0] == GGUFValueType.ARRAY:
+ nest_count = len(field.types) - 1
+ pretty_type = '[' * nest_count + str(field.types[-1].name) + ']' * nest_count
+ else:
+ pretty_type = str(field.types[-1].name)
+
+ total_elements = len(field.data)
+ value = ""
+ if len(field.types) == 1:
+ curr_type = field.types[0]
+ if curr_type == GGUFValueType.STRING:
+ value = repr(str(bytes(field.parts[-1]), encoding='utf-8')[:60])
+ elif curr_type in reader.gguf_scalar_to_np:
+ value = str(field.parts[-1][0])
+ else:
+ if field.types[0] == GGUFValueType.ARRAY:
+ curr_type = field.types[1]
+ if curr_type == GGUFValueType.STRING:
+ render_element = min(5, total_elements)
+ for element_pos in range(render_element):
+ value += repr(str(bytes(field.parts[-1 - element_pos]), encoding='utf-8')[:5]) + (", " if total_elements > 1 else "")
+ elif curr_type in reader.gguf_scalar_to_np:
+ render_element = min(7, total_elements)
+ for element_pos in range(render_element):
+ value += str(field.parts[-1 - element_pos][0]) + (", " if total_elements > 1 else "")
+ value = f'[ {value}{" ..." if total_elements > 1 else ""} ]'
+ kv_dump_table.append({"n":n, "pretty_type":pretty_type, "total_elements":total_elements, "field_name":field.name, "value":value})
+
+ kv_dump_table_header_map = [
+ {'key_name':'n', 'header_name':'POS', 'align':'right'},
+ {'key_name':'pretty_type', 'header_name':'TYPE', 'align':'left'},
+ {'key_name':'total_elements', 'header_name':'Count', 'align':'right'},
+ {'key_name':'field_name', 'header_name':'Key', 'align':'left'},
+ {'key_name':'value', 'header_name':'Value', 'align':'left'},
+ ]
+
+ markdown_content += markdown_table_with_alignment_support(kv_dump_table_header_map, kv_dump_table)
+
+ markdown_content += "\n"
+
+ if not args.no_tensors:
+ # Group tensors by their prefix and maintain order
+ tensor_prefix_order: list[str] = []
+ tensor_name_to_key: dict[str, int] = {}
+ tensor_groups: dict[str, list[ReaderTensor]] = {}
+ total_elements = sum(tensor.n_elements for tensor in reader.tensors)
+
+ # Parsing Tensors Record
+ for key, tensor in enumerate(reader.tensors):
+ tensor_components = tensor.name.split('.')
+
+ # Classify Tensor Group
+ tensor_group_name = "base"
+ if tensor_components[0] == 'blk':
+ tensor_group_name = f"{tensor_components[0]}.{tensor_components[1]}"
+
+ # Check if new Tensor Group
+ if tensor_group_name not in tensor_groups:
+ tensor_groups[tensor_group_name] = []
+ tensor_prefix_order.append(tensor_group_name)
+
+ # Record Tensor and Tensor Position
+ tensor_groups[tensor_group_name].append(tensor)
+ tensor_name_to_key[tensor.name] = key
+
+ # Tensors Mapping Dump
+ markdown_content += f'## Tensors Overview {element_count_rounded_notation(total_elements)} Elements\n\n'
+ markdown_content += f'Total number of elements in all tensors: {total_elements} Elements\n'
+ markdown_content += '\n'
+
+ for group in tensor_prefix_order:
+ tensors = tensor_groups[group]
+ group_elements = sum(tensor.n_elements for tensor in tensors)
+ markdown_content += f"- [{translate_tensor_name(group)} Tensor Group - {element_count_rounded_notation(group_elements)} Elements](#{group.replace('.', '_')})\n"
+
+ markdown_content += "\n"
+
+ for group in tensor_prefix_order:
+ tensors = tensor_groups[group]
+ group_elements = sum(tensor.n_elements for tensor in tensors)
+ group_percentage = group_elements / total_elements * 100
+ markdown_content += f"### <a name=\"{group.replace('.', '_')}\">{translate_tensor_name(group)} Tensor Group : {element_count_rounded_notation(group_elements)} Elements</a>\n\n"
+
+ # Precalculate column sizing for visual consistency
+ prettify_element_est_count_size: int = 1
+ prettify_element_count_size: int = 1
+ prettify_dimension_max_widths: dict[int, int] = {}
+ for tensor in tensors:
+ prettify_element_est_count_size = max(prettify_element_est_count_size, len(str(element_count_rounded_notation(tensor.n_elements))))
+ prettify_element_count_size = max(prettify_element_count_size, len(str(tensor.n_elements)))
+ for i, dimension_size in enumerate(list(tensor.shape) + [1] * (4 - len(tensor.shape))):
+ prettify_dimension_max_widths[i] = max(prettify_dimension_max_widths.get(i,1), len(str(dimension_size)))
+
+ # Generate Tensor Layer Table Content
+ tensor_dump_table: list[dict[str, str | int]] = []
+ for tensor in tensors:
+ human_friendly_name = translate_tensor_name(tensor.name.replace(".weight", ".(W)").replace(".bias", ".(B)"))
+ pretty_dimension = ' x '.join(f'{str(d):>{prettify_dimension_max_widths[i]}}' for i, d in enumerate(list(tensor.shape) + [1] * (4 - len(tensor.shape))))
+ element_count_est = f"({element_count_rounded_notation(tensor.n_elements):>{prettify_element_est_count_size}})"
+ element_count_string = f"{element_count_est} {tensor.n_elements:>{prettify_element_count_size}}"
+ type_name_string = f"{tensor.tensor_type.name}"
+ tensor_dump_table.append({"t_id":tensor_name_to_key[tensor.name], "layer_name":tensor.name, "human_layer_name":human_friendly_name, "element_count":element_count_string, "pretty_dimension":pretty_dimension, "tensor_type":type_name_string})
+
+ tensor_dump_table_header_map = [
+ {'key_name':'t_id', 'header_name':'T_ID', 'align':'right'},
+ {'key_name':'layer_name', 'header_name':'Tensor Layer Name', 'align':'left'},
+ {'key_name':'human_layer_name', 'header_name':'Human Friendly Tensor Layer Name', 'align':'left'},
+ {'key_name':'element_count', 'header_name':'Elements', 'align':'left'},
+ {'key_name':'pretty_dimension', 'header_name':'Shape', 'align':'left'},
+ {'key_name':'tensor_type', 'header_name':'Type', 'align':'left'},
+ ]
+
+ markdown_content += markdown_table_with_alignment_support(tensor_dump_table_header_map, tensor_dump_table)
+
+ markdown_content += "\n"
+ markdown_content += f"- Total elements in {group}: ({element_count_rounded_notation(group_elements):>4}) {group_elements}\n"
+ markdown_content += f"- Percentage of total elements: {group_percentage:.2f}%\n"
+ markdown_content += "\n\n"
+
+ print(markdown_content) # noqa: NP100
+
+
def main() -> None:
parser = argparse.ArgumentParser(description="Dump GGUF file metadata")
parser.add_argument("model", type=str, help="GGUF format model filename")
parser.add_argument("--no-tensors", action="store_true", help="Don't dump tensor metadata")
parser.add_argument("--json", action="store_true", help="Produce JSON output")
parser.add_argument("--json-array", action="store_true", help="Include full array values in JSON output (long)")
+ parser.add_argument("--markdown", action="store_true", help="Produce markdown output")
parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"])
logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
- if not args.json:
+ if not args.json and not args.markdown:
logger.info(f'* Loading: {args.model}')
reader = GGUFReader(args.model, 'r')
if args.json:
dump_metadata_json(reader, args)
+ elif args.markdown:
+ dump_markdown_metadata(reader, args)
else:
dump_metadata(reader, args)
overview / pkg / ggml / sources / llama.cpp / commitdiff