dr-hf 0.1.0

HuggingFace utilities for repository management, dataset operations, and model analysis

dr-hf

HuggingFace utilities for repository management, dataset operations, and model analysis.

Installation

uv add dr-hf

For model weight analysis (requires PyTorch):

uv add dr-hf[weights]

For DuckDB query support:

uv add dr-hf[duckdb]

Quick Start

from dr_hf import (
    get_checkpoint_branches,
    parse_branch_name,
    HFLocation,
    download_dataset,
)

# Parse checkpoint branches from a repo
branches = get_checkpoint_branches("org/model-checkpoints")
for branch in branches:
    info = parse_branch_name(branch)
    print(f"Step {info.step}, Seed: {info.seed}")

# Create a location reference for HF datasets
loc = HFLocation(org="allenai", repo_name="my-dataset")
print(loc.repo_uri)  # hf://datasets/allenai/my-dataset

# Download a dataset to local parquet
from pathlib import Path
download_dataset(Path("./data/squad_train.parquet"), repo_id="squad", split="train")

Module Overview

Module	Purpose	Key Exports
branches	Branch discovery & parsing	get_checkpoint_branches, parse_branch_name, create_branch_metadata
configs	Model config analysis	download_config_file, analyze_model_config, estimate_parameter_count
weights	Model weight analysis	analyze_model_weights, calculate_weight_statistics ⚡
checkpoints	Checkpoint orchestration	analyze_complete_checkpoint, process_all_checkpoints ⚡
datasets	Dataset loading & caching	load_or_download_dataset, download_dataset
io	HfApi upload/download	upload_file_to_hf, cached_download_tables_from_hf
location	HF resource URIs	HFLocation, HFRepoID, HFResource
paths	Environment paths	get_data_dir, get_repo_dir
models	Pydantic data models	BranchInfo, ConfigAnalysis, WeightsAnalysis, ...

⚡ = Requires [weights] optional dependency

Documentation

• Full API Reference
• Module guides: branches | configs | weights | checkpoints | datasets | io | location | paths
• Pydantic Models
• Recipes & Patterns

Auto-generated API Docs

# Serve interactive docs locally
uv run pdoc dr_hf

# Generate static HTML
uv run pdoc dr_hf -o docs/api_html

Quick Reference

Branch Operations

from dr_hf import (
    get_all_repo_branches,    # list all branches in repo
    get_checkpoint_branches,  # filter to stepN-seed-* branches
    is_checkpoint_branch,     # check if branch matches pattern
    parse_branch_name,        # extract step/seed -> BranchInfo
    extract_step_from_branch, # get step number
    extract_seed_from_branch, # get seed string
    sort_branches_by_step,    # sort branches by step
    group_branches_by_seed,   # group branches by seed
    create_branch_metadata,   # full repo metadata -> BranchMetadata
)

Config Analysis

from dr_hf import (
    download_config_file,           # download config.json
    analyze_model_config,           # parse config -> ConfigAnalysis
    extract_model_architecture_info,# extract architecture -> ArchitectureInfo
    estimate_parameter_count,       # estimate params -> ParameterEstimate
)

Weight Analysis (requires [weights])

from dr_hf import (
    discover_model_weight_files,  # find weight files in repo
    download_model_weights,       # download specific weights
    calculate_weight_statistics,  # analyze weights -> WeightFileStatistics
    calculate_tensor_stats,       # per-tensor stats -> TensorStats
    analyze_layer_structure,      # categorize layers -> LayerAnalysis
    calculate_global_weight_stats,# global stats -> GlobalWeightStats
    analyze_model_weights,        # full workflow -> WeightsAnalysis
)

Checkpoint Analysis (requires [weights])

from dr_hf import (
    download_optimizer_checkpoint, # download optim.pt
    analyze_optimizer_checkpoint,  # parse optimizer -> OptimizerAnalysis
    analyze_complete_checkpoint,   # full analysis -> CheckpointAnalysis
    process_single_checkpoint,     # single branch analysis
    process_all_checkpoints,       # parallel multi-branch
    create_comprehensive_summary,  # DataFrame summary
    create_learning_rate_summary,  # LR-focused summary
    save_checkpoint_analysis,      # save to JSON
    save_all_analyses_outputs,     # save CSVs + JSON
)

Dataset Operations

from dr_hf import (
    load_or_download_dataset, # load from cache or download
    download_dataset,         # download HF dataset to parquet
    sanitize_repo_name,       # convert repo ID to safe filename
)

HfApi I/O

from dr_hf import (
    upload_file_to_hf,            # upload file to HF repo
    cached_download_tables_from_hf,# download parquet with caching
    get_tables_from_cache,        # read cached parquet files
    read_local_parquet_paths,     # list local parquet files
    query_hf_with_duckdb,         # query HF with DuckDB (requires [duckdb])
)

Location Management

from dr_hf import (
    HFLocation,   # Pydantic model for HF dataset locations
    HFRepoID,     # Type alias: "org/repo-name"
    HFResource,   # Type alias: "hf://datasets/org/repo"
)

loc = HFLocation(org="allenai", repo_name="c4")
loc.repo_id        # "allenai/c4"
loc.repo_uri       # "hf://datasets/allenai/c4"
loc.repo_link      # HttpUrl to HF page

# Parse from URI
loc = HFLocation.from_uri("hf://datasets/squad/squad")

Environment Paths

from dr_hf import (
    get_data_dir,  # get DATA_DIR from env
    get_repo_dir,  # get REPO_DIR from env
)

Pydantic Models

from dr_hf import (
    # Branch models
    BranchInfo,           # parsed branch (step, seed, valid)
    SeedBranchInfo,       # step + branch name
    SeedConfiguration,    # seed group metadata
    BranchMetadata,       # full repo branch info

    # Config models
    ConfigAnalysis,       # config.json analysis result
    ArchitectureInfo,     # model architecture details
    ParameterEstimate,    # estimated parameter counts

    # Weight models
    WeightsAnalysis,      # full weight analysis result
    WeightsSummary,       # aggregated weight stats
    WeightFileStatistics, # per-file statistics
    TensorInfo,           # per-tensor metadata
    TensorStats,          # tensor statistics
    LayerAnalysis,        # layer categorization
    LayerCategorization,  # layers by type
    LayerCounts,          # layer count summary
    GlobalWeightStats,    # global weight statistics
    ParameterStats,       # parameter counts

    # Checkpoint models
    CheckpointAnalysis,   # full checkpoint analysis
    CheckpointComponents, # optimizer + config + weights
    CheckpointSummaryRow, # DataFrame row model
    OptimizerAnalysis,    # optimizer state analysis
    OptimizerComponentInfo,# optimizer component details
    LearningRateInfo,     # learning rate extraction
    ParamGroupInfo,       # param group details
)

License

MIT