Instructions to use AlexWortega/moe100m-physics-tinybpe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlexWortega/moe100m-physics-tinybpe with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AlexWortega/moe100m-physics-tinybpe", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload VERIFY.json with huggingface_hub
Browse files- VERIFY.json +29 -0
VERIFY.json
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{
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"all_pass": false,
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"gates": {
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"1_generation_sanity": {
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"pass": true,
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"detail": "parsed 1 obj from greedy continuation; sample='Frame 5: in motion\\n obj_0: pos=(94.7987, 92.7295), vel=(223.8441, 234.7186)\\nFrame 6:'"
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},
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"2_loss_sanity": {
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"pass": true,
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"detail": "train_loss(last50 avg)=1.2204 (raw last-step=1.7107) in band [0.62,6.24]"
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},
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"3_eval_tracks_train": {
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"pass": true,
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"detail": "|best_eval 1.6926 - train_loss(last50 avg) 1.2204| = 0.4722 (<0.5)"
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},
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"4_data_consumption": {
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"pass": false,
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"detail": "consumed 1.4e+08/7e+08 = 19.9% (>=70%)"
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},
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"5_stderr_scan": {
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"pass": false,
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"detail": "stderr hits=[]; aborted=True. (NaN-skip+recover is tolerated)"
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},
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"6_param_count": {
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"pass": true,
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"detail": "active=92.83M (target 100M +/-15% -> [85,115])"
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}
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}
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}
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