C2: best epoch 100, val loss 0.0993
Browse files- C2_best.pt +3 -0
- README.md +86 -0
- config.json +113 -0
- summary.json +1964 -0
- tokenizer_cipher.json +0 -0
- tokenizer_plain.json +0 -0
C2_best.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:fde9061a389146483bea353f589f1d293c484b3ccf0acb46d2f9761442968db5
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size 33282087
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README.md
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---
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library_name: pytorch
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pipeline_tag: translation
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tags:
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- transformer
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- seq2seq
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- cryptanalysis
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- from-scratch
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- anlp
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---
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# C2 -- RoPE instead of sinusoidal absolute
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Encoder-decoder transformer written from scratch -- no `nn.Transformer`, no
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pretrained weights, tokenizer fitted in-repo -- trained to decipher a binary
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cipher into English plaintext. One row of a five-configuration ablation in
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which exactly one architectural choice moves at a time.
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| | |
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| --- | --- |
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| configuration | `C2` -- RoPE instead of sinusoidal absolute |
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| positional encoding | rope |
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| attention | mha |
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| normalisation | layernorm |
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| tokenisation | unigram LM subword, fitted on the training split |
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| depth | 2 encoder / 2 decoder layers |
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| width | dim_model 128, 4 heads, dim_ff 512 |
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| parameters | 2,763,264 |
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| windowing | 1024-character source windows at stride 768 (25,654 training windows) |
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| best epoch | 100 (val loss 0.0993) |
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## Test metrics
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| metric | test | test_full | gen_test_win | gen_test | gen_test_full |
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| --- | --- | --- | --- | --- | --- |
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| loss | 0.1198 | 5.9206 | -- | -- | -- |
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| perplexity | 1.1273 | 372.6371 | -- | -- | -- |
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| token acc | 0.9747 | 0.3038 | 0.8211 | 0.4405 | 0.0425 |
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| bit acc | -- | -- | 0.9561 | 0.8182 | 0.3518 |
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| seq acc | 0.5632 | 0.0528 | 0.6598 | 0.2040 | 0.0540 |
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| levenshtein | -- | -- | 1.5377 | 22.9500 | 505.6000 |
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| lev / len | -- | -- | 0.0103 | 0.0342 | 0.6313 |
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| BLEU | -- | -- | 0.9453 | 0.9246 | 0.1027 |
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| ROUGE-1 | -- | -- | 0.9753 | 0.9620 | 0.3624 |
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| ROUGE-2 | -- | -- | 0.9555 | 0.9412 | 0.2560 |
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| ROUGE-L | -- | -- | 0.9753 | 0.9619 | 0.3113 |
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- `test` -- teacher-forced, per window
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- `test_full` -- teacher-forced, per whole line
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- `gen_test_win` -- greedy, per window
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- `gen_test` -- greedy, windowed and stitched
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- `gen_test_full` -- greedy, whole line in one pass
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## Files
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- `C2_best.pt` -- weights, optimiser state, config and vocabulary sizes,
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from the best epoch.
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- `config.json` -- the full ExperimentConfig this was trained under.
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- `summary.json` -- the run record: the metrics above plus per-epoch history.
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- `tokenizer_cipher.json`, `tokenizer_plain.json` -- the fitted unigram
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vocabularies, when the config uses them. Byte-level configurations have none
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by construction.
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## Loading it
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The checkpoint is a plain `torch.save` dict, not a `transformers` model. The
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vocabulary sizes travel with it because they are not recoverable from the
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config -- the tokenizer lands wherever pruning leaves it:
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```python
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import torch
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from huggingface_hub import hf_hub_download
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from src.config import get_config
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from src.models.base import build_model
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path = hf_hub_download("winterdewdev/anlp-a1-transformers-C2", "C2_best.pt")
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state = torch.load(path, map_location="cpu", weights_only=False)
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cfg = get_config(state["config_name"])
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model = build_model(cfg, state["src_vocab_size"], state["tgt_vocab_size"])
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model.load_state_dict(state["model"])
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model.eval()
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```
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Trained with `python -m src.train --config C2`.
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config.json
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{
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"name": "C2",
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"description": "RoPE instead of sinusoidal absolute",
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"model": {
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"dim_model": 128,
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"num_heads": 4,
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"num_encoder_layers": 2,
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"num_decoder_layers": 2,
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"dim_ff": 512,
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"dropout": 0.1,
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"norm_eps": 1e-06,
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"num_kv_heads": 2,
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"rope_base": 10000.0,
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"pos_encoding": "rope",
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"attention": "mha",
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"norm": "layernorm",
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"tokenization": "subword",
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"tie_output_embedding": false
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},
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"data": {
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"cipher_path": "/home2/winterdew/ANLP_A1/ANLP-M26-Assignment-1/dataset/brown_cipher.txt",
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"plain_path": "/home2/winterdew/ANLP_A1/ANLP-M26-Assignment-1/dataset/brown_plain.txt",
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"split_ratios": [
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0.8,
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0.1,
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0.1
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],
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"split_seed": 1234,
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"max_src_len_subword": 768,
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"max_tgt_len_subword": 256,
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"max_src_len_byte": 3072,
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"max_tgt_len_byte": 1152,
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"length_policy": "drop",
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"effective_batch_size": 64,
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"micro_batch_size_subword": 64,
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"micro_batch_size_byte": 64,
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"num_workers": 0,
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"verbose": true,
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"tokenizer": {
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"src_vocab_size": 4096,
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"tgt_vocab_size": 4096,
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"src_seed_size": 20000,
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"tgt_seed_size": 50000,
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"src_max_segment": 16,
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"tgt_max_segment": 32,
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"max_piece_len": 16,
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"shrink_factor": 0.75,
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"em_iters": 2,
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"min_freq": 2,
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"sample_seed": 1234,
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"cache_dir": "/home2/winterdew/ANLP_A1/ANLP-M26-Assignment-1/outputs/tokenizers",
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"retrain": false
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},
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"window": {
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"enabled": true,
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"src_window": 1024,
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"src_stride": 768,
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"min_tgt_chars": 16,
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"eval_full_sentences": true
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}
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},
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"train": {
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"epochs": 100,
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"lr": 0.0007,
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"betas": [
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0.9,
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0.98
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],
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"adam_eps": 1e-09,
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"weight_decay": 0.01,
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"grad_clip": 1.0,
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"label_smoothing": 0.05,
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"scheduler": "cosine",
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"warmup_steps": 400,
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"min_lr": 1e-05,
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"seed": 26,
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"device": "auto",
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"amp": true,
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"eval_every_epochs": 1,
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"generate_every_epochs": 5,
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"max_eval_batches": null,
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"length_slack": 16,
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"gen_eval_examples": 128,
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"gen_batch_size": 32,
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"test_gen_examples": null,
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"log_every_steps": 50,
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"checkpoint_dir": "/home2/winterdew/ANLP_A1/ANLP-M26-Assignment-1/outputs/checkpoints",
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"keep_best_only": true,
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"early_stopping_patience": null,
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"wandb_project": "anlp-a1-transformers",
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"wandb_entity": null,
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"wandb_mode": "online"
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},
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"blt": {
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"patch_size": 8,
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"local_dim": 128,
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"local_heads": 4,
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"local_dim_ff": 512,
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"local_encoder_layers": 1,
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"local_decoder_layers": 1,
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"local_dropout": 0.1
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},
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"derived": {
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"token_free": false,
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"max_src_len": 1026,
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"max_tgt_len": 1026,
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"micro_batch_size": 64,
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"windowed": true,
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"grad_accum_steps": 1,
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"pe_max_len": 1042,
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"head_dim": 32
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}
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}
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summary.json
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|
| 1 |
+
{
|
| 2 |
+
"config": "C2",
|
| 3 |
+
"description": "RoPE instead of sinusoidal absolute",
|
| 4 |
+
"parameters": 2763264,
|
| 5 |
+
"windowed": true,
|
| 6 |
+
"window": {
|
| 7 |
+
"src_window": 1024,
|
| 8 |
+
"src_stride": 768,
|
| 9 |
+
"train_examples": 25654
|
| 10 |
+
},
|
| 11 |
+
"best_epoch": 100,
|
| 12 |
+
"best_val_loss": 0.09927769922586938,
|
| 13 |
+
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| 1964 |
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|
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