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Jongbin-kr/llama-3.1-8b-instruct_LBox-category-prior-8x1-lora-moe
llama-3.1-8b-instruct_LBox-category-prior-8x1-lora-moe is a machine learning model from Jongbin-kr. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as llama3.1.
should probably proofread and complete it, then remove this comment. --
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Updated Sep 5, 2026
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From the Hugging Face model README
This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Supervised Loss | Load Balance Loss | Z Loss |
|---|---|---|---|---|---|---|
| 9.9457 | 0.1830 | 500 | 1.2682 | 1.0739 | 2.7332 | 5.4282 |
| 6.7027 | 0.3660 | 1000 | 0.8868 | 1.1596 | 2.6544 | 7.1435 |
| 5.8974 | 0.5489 | 1500 | 0.8349 | 0.9402 | 2.9654 | 6.0732 |
| 5.8900 | 0.7319 | 2000 | 0.8071 | 1.3818 | 2.9513 | 5.3320 |
| 6.5693 | 0.9149 | 2500 | 0.7883 | 0.9038 | 2.9441 | 3.6248 |
| 5.8019 | 1.0977 | 3000 | 0.7796 | 0.9469 | 2.8268 | 2.8833 |
| 5.7989 | 1.2807 | 3500 | 0.7592 | 1.6139 | 3.5352 | 2.9172 |
| 5.6415 | 1.4637 | 4000 | 0.7490 | 1.2382 | 2.8955 | 1.9796 |
| 5.2636 | 1.6467 | 4500 | 0.7454 | 0.8895 | 3.0758 | 1.6344 |
| 5.7957 | 1.8296 | 5000 | 0.7356 | 1.1778 | 2.8222 | 1.5490 |
| 5.8623 | 2.0124 | 5500 | 0.7326 | 0.9802 | 2.8715 | 1.4671 |
| 5.1178 | 2.1954 | 6000 | 0.7344 | 0.9339 | 2.9024 | 1.5378 |
| 6.0174 | 2.3784 | 6500 | 0.7272 | 1.1811 | 2.9990 | 1.4460 |
| 5.5216 | 2.5614 | 7000 | 0.7250 | 1.0457 | 2.7300 | 1.6281 |
| 4.8988 | 2.7444 | 7500 | 0.7231 | 0.9169 | 2.9111 | 1.6735 |
| 5.4509 | 2.9274 | 8000 | 0.7195 | 1.3227 | 3.0895 | 1.4725 |
| 4.8223 | 3.1102 | 8500 | 0.7198 | 1.0807 | 2.7534 | 1.5616 |
| 5.0465 | 3.2931 | 9000 | 0.7202 | 1.2287 | 2.9593 | 1.5258 |
| 5.8587 | 3.4761 | 9500 | 0.7170 | 1.1436 | 2.8002 | 1.5982 |
| 5.7854 | 3.6591 | 10000 | 0.7163 | 1.4184 | 3.2401 | 1.5266 |
| 5.1895 | 3.8421 | 10500 | 0.7156 | 1.3455 | 3.0909 | 1.6244 |
| 5.6037 | 4.0249 | 11000 | 0.7150 | 1.2034 | 2.8747 | 1.6527 |
| 5.1878 | 4.2079 | 11500 | 0.7153 | 1.2657 | 2.9666 | 1.6544 |
| 5.1715 | 4.3909 | 12000 | 0.7149 | 1.2851 | 3.0077 | 1.6401 |
| 5.5300 | 4.5738 | 12500 | 0.7148 | 1.3079 | 3.0496 | 1.6346 |
| 5.3011 | 4.7568 | 13000 | 0.7146 | 1.2713 | 2.9880 | 1.6303 |
| 5.1349 | 4.9398 | 13500 | 0.7145 | 1.2590 | 2.9670 | 1.6471 |
| 5.3102 | 5.0 | 13665 | 0.7146 | 1.2566 | 2.9630 | 1.6500 |