Instructions to use Prasanna85/laya-issue-triage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Laya
How to use Prasanna85/laya-issue-triage with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Download run_meta.json from Prasanna85/laya-issue-triage: direct link, hf CLI and curl.
- Browser
- Download file 2.25 kB
-
https://huggingface.co/Prasanna85/laya-issue-triage/resolve/main/run_meta.json
- Command line
-
hf download hf://Prasanna85/laya-issue-triage/run_meta.json
-
curl -L -o run_meta.json https://huggingface.co/Prasanna85/laya-issue-triage/resolve/main/run_meta.json
2.25 kB
| { | |
| "project": "laya-triage", | |
| "created_utc": "2026-10-02T08:33:21+00:00", | |
| "smoke": false, | |
| "upstream_notebook": { | |
| "repo": "NandhaKishorM/laya", | |
| "tag": "v0.3.23", | |
| "commit": "d8a2e59781ca135169a36095056132e273cd9938", | |
| "tag_object": "ae3222b3fcdf424254a2c726d72161f671a86d95" | |
| }, | |
| "base": { | |
| "repo": "convaiinnovations/laya", | |
| "revision": "55cf4c4ebb4ebe31b2550e8bdf3bd21b99753851" | |
| }, | |
| "versions": { | |
| "python": "3.12.13", | |
| "torch": "2.10.0+cu128", | |
| "cuda": "12.8", | |
| "laya": "0.3.23", | |
| "transformers": "5.18.0", | |
| "tokenizers": "0.23.2", | |
| "huggingface_hub": "1.33.0", | |
| "safetensors": "0.8.0" | |
| }, | |
| "gpus": [ | |
| "Tesla T4", | |
| "Tesla T4" | |
| ], | |
| "constants": { | |
| "max_len": 1024, | |
| "head_max_len": 256, | |
| "seed": 42, | |
| "epochs": 4, | |
| "grad_accum": 4, | |
| "label_smoothing": 0.0, | |
| "model_name": "laya-issue-triage" | |
| }, | |
| "data": { | |
| "train.jsonl": { | |
| "sha256": "f57427a3b9d32ad76eab5d469eeca50561c75db39873bcf6c0d61cffc59c5113", | |
| "bytes": 2505537, | |
| "rows": 1196 | |
| }, | |
| "val.jsonl": { | |
| "sha256": "330bab114a6ff83cf508ef948603b53a6425f012f4e9df14e69490cc02d60d6b", | |
| "bytes": 618432, | |
| "rows": 300 | |
| } | |
| }, | |
| "items": { | |
| "sha256": "e1ad1d4d2810d90a307af53c0e1be821ccb72684b0fbd2f93f9ebd0e495e88ab", | |
| "items": 1196, | |
| "label_counts": { | |
| "bug": 398, | |
| "feature": 398, | |
| "question": 400 | |
| }, | |
| "token_length": { | |
| "p50": 410.0, | |
| "p90": 1024.0, | |
| "p99": 1024.0, | |
| "max": 1024 | |
| }, | |
| "share_longer_than_512": 0.3796 | |
| }, | |
| "plan": { | |
| "items": 1196, | |
| "calib_items": 119, | |
| "train_items": 1076, | |
| "items_per_rank": 538, | |
| "micro_batches_per_epoch": 68, | |
| "optimizer_steps_per_epoch": 17, | |
| "epochs": 4, | |
| "optimizer_steps": 68, | |
| "effective_batch": 64 | |
| }, | |
| "train": { | |
| "calib_seed": 20260922, | |
| "calib_items": 119, | |
| "train_items": 1076, | |
| "items_per_rank": 538, | |
| "micro_batch": 8, | |
| "grad_accum": 4, | |
| "epochs": 4, | |
| "optimizer_steps": 68, | |
| "scheduler_t_max": 68, | |
| "train_seconds": 712.0, | |
| "fitted_temperatures": [ | |
| 4.018789291381836, | |
| 1.2, | |
| 1.2 | |
| ] | |
| }, | |
| "temperature": [ | |
| 4.018789291381836, | |
| 1.2, | |
| 1.2 | |
| ], | |
| "gpu_smoke_predict": { | |
| "rows": 30, | |
| "accuracy": 0.8667, | |
| "pred_counts": { | |
| "bug": 9, | |
| "feature": 10, | |
| "question": 11 | |
| }, | |
| "gold_counts": { | |
| "bug": 10, | |
| "feature": 10, | |
| "question": 10 | |
| } | |
| } | |
| } |