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Pradheep1647/
run_hca-meetingbank-bs8-e20-fp32-19

Name: run_hca-meetingbank-bs8-e20-fp32-19
Availability: InStock

pytorch

transformer

meeting-summarization

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Run Hca

Custom PyTorch Transformer checkpoint trained on MeetingBank for meeting summarization research. This repository is part of the transformer-lab collection.

Model Details

Field	Value
Repository	`Pradheep1647/run_hca-meetingbank-bs8-e20-fp32-19`
Attention	`hca`
Dataset	`meetingbank`
Layers	`6`
Hidden size	`512`
Heads	`8`
Batch size	`8`
Epochs	`20`
Precision	`fp32`
Checkpoint	`run_hca-meetingbank-bs8-e20-fp32-1919.pt`

Architecture

Static architecture diagram generated from this run's config.json, including model width, depth, sequence dimensions, and attention-specific settings.

Training Loss

Raw curve data is available in loss_curve.csv.

Available Models

Variant	Repository
`gqa_rope`	`Pradheep1647/run_gqa_rope-meetingbank-bs8-e20-fp32-19`
`mqa`	`Pradheep1647/run_mqa-meetingbank-bs8-e20-fp32-19`
`gqa`	`Pradheep1647/run_gqa-meetingbank-bs8-e20-fp32-19`
`mha`	`Pradheep1647/run_mha-meetingbank-bs8-e20-fp32-19`
`sliding_gqa`	`Pradheep1647/run_sliding_gqa-meetingbank-bs8-e20-fp32-19`
`csa`	`Pradheep1647/run_csa-meetingbank-bs8-e20-fp32-19`
`hca`	`Pradheep1647/run_hca-meetingbank-bs8-e20-fp32-19`

Files

File	Purpose
`run_hca-meetingbank-bs8-e20-fp32-1919.pt`	PyTorch checkpoint containing `model_state_dict`, optimizer states, epoch, and global step.
`config.json`	Training and architecture config converted from the Hydra run config.
`architecture.png`	Architecture diagram generated from the saved model config, with block shapes and dimensions.
`tokenizer.json`	MeetingBank transcript tokenizer alias for source inputs.
`transcript_tokenizer.json`	Explicit MeetingBank transcript tokenizer.
`summary_tokenizer.json`	MeetingBank summary tokenizer for target text.
`loss_curve.csv`	TensorBoard `train/loss` scalar export.
`loss_curve.svg`	Static training-loss plot generated from `loss_curve.csv`.

Usage

These checkpoints are from a custom PyTorch codebase, not a transformers.AutoModel checkpoint. Use the repo-native builder to instantiate the architecture, then load the checkpoint state dict.

python

from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from omegaconf import OmegaConf

import src  # registers components
from src.model.builder import build_causal_lm

repo_id = "Pradheep1647/run_hca-meetingbank-bs8-e20-fp32-19"

config_path = hf_hub_download(repo_id=repo_id, filename="config.json")
checkpoint_path = hf_hub_download(repo_id=repo_id, filename="run_hca-meetingbank-bs8-e20-fp32-1919.pt")

cfg = OmegaConf.load(config_path)
model = build_causal_lm(cfg)

state = torch.load(checkpoint_path, map_location="cpu")
model.load_state_dict(state["model_state_dict"])
model.eval()

print(f"Loaded {repo_id} from {Path(checkpoint_path).name}")

Notes

This is a research checkpoint for comparing attention variants under the same MeetingBank setup.
The config and tokenizers are included so future runs can reproduce the architecture and preprocessing assumptions.
Use config.json as the source of truth for architecture parameters.