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-dedup split of HuggingFaceTB/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
  1. Model:

    • Dropout rate: 0.0
    • Activations: silu, gated-silu
    • torch compile: true
  2. Data processing:

    • Input length: 1024
    • MLM probability: 0.15
  3. 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
  4. Hardware:

    • Device: RTX 4080
    • Precision: bfloat16, tf32

plots

training loss

loss

Expand grad and weights L2 norm plots

grad norm

grad

weights norm

weights


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