Run Msa Meetingbank Bs8 E20 Fp32 19
Custom PyTorch Transformer checkpoint trained on MeetingBank for meeting summarization research. This repository is part of the transformer-lab collection.
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 233 MB
- Versión
- vRevision 020a130
- Actualizado
- 14 sept 2026
- Publicador
- @Pradheep1647
- Descargas
- 12
- Fuente
- Hugging Face
Descripción de la fuente
Run Msa
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_msa-meetingbank-bs8-e20-fp32-19 |
| Attention | msa |
| Dataset | meetingbank |
| Layers | 6 |
| Hidden size | 512 |
| Heads | 8 |
| Batch size | 8 |
| Epochs | 20 |
| Precision | fp32 |
| Checkpoint | run_msa-meetingbank-bs8-e20-fp32-1919.pt |
Architecture
Architecture diagram
Static architecture diagram generated from this run's config.json, including model width, depth, sequence dimensions, and attention-specific settings.
Training Loss
Training loss
Raw curve data is available in loss_curve.csv.
Available Models
Files
| File | Purpose |
|---|---|
run_msa-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.
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_msa-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_msa-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.jsonas the source of truth for architecture parameters.
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 10 ago
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactorun_msa-meetingbank-bs8-e20-fp32-1919.pt
pt · 233 MB · SHA-256 4f692c9dfc3c…1ca4 · Hugging Face
DescargarExtractos de fuentes
3 extractos
Extractos de fuentes
3 extractos--- library_name: pytorch license: apache-2.0 datasets: - huuuyeah/meetingbank tags: - pytorch - transformer - meeting-summarization - custom-code - attention-variant --- # Run Msa Custom PyTorch Transformer checkpoint trained on MeetingBank for meeting summarization research. This repository is part of the [`transformer-lab`](https://huggingface.co/collections/Pradheep1647/transformer-lab-6a07fe3185f5728e217997e0) collection. ## Model Details | Field | Value | |---|---| | Repository | `Pradheep1647/run_msa-meetingbank-bs8-e20-fp32-19` | | Attention | `msa` | | Dataset | `meetingbank` | | Layers | `6` | | Hidden size | `512` | | Heads | `8` | | Batch size | `8` | | Epochs | `20` | | Precision | `fp32` | | Checkpoint | `run_msa-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`](loss_curve.csv). ## Available Models | Variant | Repository | |---|---| | `gqa_rope` | [`Pradheep1647/run_gqa_rope-meetingbank-bs8-e20-fp32-19`](https://huggingface.co/Pradheep1647/run_gqa_rope-meetingbank-bs8-e20-fp32-19) | | `mqa` | [`Pradheep1647/run_mqa-meetingbank-bs8-e20-fp32-19`](https://huggingface.co/Pradheep1647/run_mqa-meetingbank-bs8-e20-fp32-19) | | `gqa` | [`Pradheep1647/run_gqa-meetingbank-bs8-e20-fp32-19`](https://huggingface.co/Pradheep1647/run_gqa-meetingbank-bs8-e20-fp32-19) | | `mha` | [`Pradheep1647/run_mha-meetingbank-bs8-e20-fp32-19`](https://huggingface.co/Pradheep1647/run_mha-meetingbank-bs8-e20-fp32-19) | | `sliding_gqa` | [`Pradheep1647/run_sliding_gqa-meetingbank-bs8-e20-fp32-19`](https://huggingface.co/Pradheep1647/run_sliding_gqa-meetingbank-bs8-e20-fp32-19) | | `csa` | [`Pradheep1647/run_csa-meetingbank-bs8-e20-fp32-19`](https://huggingface.co/Pradheep1647/run_csa-meetingbank-bs8-e20-fp32-19) | | `hca` | [`Pradheep1647/run_hca-meetingbank-bs8-e20-fp32-19`](https://huggingface.co/Pradheep1647/run_hca-meetingbank-bs8-e20-fp32-19) | | `msa` | [`Pradheep1647/run_msa-meetingbank-bs8-e20-fp32-19`](https://huggingface.co/Pradheep1647/run_msa-meetingbank-bs8-e20-fp32-19) | ## Files | File | Purpose | |---|---| | `run_msa-meetingbank-bs8-e20-fp32-1919.pt` |...
Source context: 12 downloads · 0 likes · Library pytorch · Repo Pradheep1647/run_msa-meetingbank-bs8-e20-fp32-19
Source context: 1 downloads · 0 likes · Library pytorch · Repo Pradheep1647/run_msa-meetingbank-bs8-e20-fp32-19
