tFINE
Collection
pretrained t5 models on high quality data(e.g. fineweb) • 6 items • Updated
YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
tFINE-base-300m
An encoder-decoder (T5 architecture) pretrained with nanoT5:
- tokenizer: sentencepiece BPE w/ byte fallback, 48k vocab (from vocab scaling laws)
- data:
fineweb-edu-dedupsplit ofHuggingFaceTB/smollm-corpus - context length: 1024 ctx
details
Detailed info, including training logs, configs, and checkpoints can be found under checkpoints/ in this repo.
Expand hyperparameter overview
Model:
- Dropout rate: 0.0
- Activations:
silu,gated-silu - torch compile: true
Data processing:
- Input length: 1024
- MLM probability: 0.15
Optimization:
- Optimizer: AdamW with scaling
- Base learning rate: 0.008
- Batch size: 120
- Total training steps: 80,000
- Warmup steps: 10,000
- Learning rate scheduler: Cosine
- Weight decay: 0.0001
- Gradient clipping: 1.0
- Gradient accumulation steps: 24
- Final cosine learning rate: 1e-5
Hardware:
- Device: RTX 4080
- Precision: bfloat16, tf32
plots
training loss
Expand grad and weights L2 norm plots
grad norm
weights norm
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