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--- |
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license: apache-2.0 |
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base_model: mistralai/Mistral-7B-Instruct-v0.1 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- openwebtext |
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model-index: |
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- name: Mistral_Sparse_pretraining_80_percent_10000 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Mistral_Sparse_pretraining_80_percent_10000 |
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This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the openwebtext dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6872 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 32 |
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- seed: 0 |
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- distributed_type: multi-GPU |
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- num_devices: 6 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 96 |
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- total_eval_batch_size: 192 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- training_steps: 10000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 1.7461 | 0.05 | 50 | 1.7009 | |
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| 1.4034 | 0.1 | 100 | 1.3910 | |
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| 1.2302 | 0.15 | 150 | 1.2330 | |
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| 1.1363 | 0.19 | 200 | 1.1354 | |
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| 1.0699 | 0.24 | 250 | 1.0723 | |
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| 1.0316 | 0.29 | 300 | 1.0284 | |
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| 1.0044 | 0.34 | 350 | 0.9943 | |
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| 0.9719 | 0.39 | 400 | 0.9668 | |
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| 0.9391 | 0.44 | 450 | 0.9430 | |
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| 0.9194 | 0.48 | 500 | 0.9249 | |
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| 0.9131 | 0.53 | 550 | 0.9092 | |
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| 0.877 | 0.58 | 600 | 0.8953 | |
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| 0.8757 | 0.63 | 650 | 0.8852 | |
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| 0.8644 | 0.68 | 700 | 0.8749 | |
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| 0.8625 | 0.73 | 750 | 0.8679 | |
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| 0.867 | 0.78 | 800 | 0.8594 | |
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| 0.852 | 0.82 | 850 | 0.8529 | |
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| 0.8482 | 0.87 | 900 | 0.8473 | |
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| 0.8372 | 0.92 | 950 | 0.8421 | |
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| 0.8391 | 0.97 | 1000 | 0.8366 | |
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| 0.8209 | 1.02 | 1050 | 0.8327 | |
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| 0.8172 | 1.07 | 1100 | 0.8275 | |
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| 0.8094 | 1.11 | 1150 | 0.8247 | |
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| 0.8107 | 1.16 | 1200 | 0.8210 | |
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| 0.8137 | 1.21 | 1250 | 0.8168 | |
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| 0.8122 | 1.26 | 1300 | 0.8143 | |
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| 0.8047 | 1.31 | 1350 | 0.8115 | |
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| 0.804 | 1.36 | 1400 | 0.8083 | |
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| 0.7955 | 1.41 | 1450 | 0.8062 | |
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| 0.7939 | 1.45 | 1500 | 0.8040 | |
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| 0.7835 | 1.5 | 1550 | 0.8019 | |
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| 0.7983 | 1.55 | 1600 | 0.8001 | |
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| 0.7953 | 1.6 | 1650 | 0.7975 | |
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| 0.7903 | 1.65 | 1700 | 0.7945 | |
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| 0.7864 | 1.7 | 1750 | 0.7938 | |
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| 0.7972 | 1.75 | 1800 | 0.7914 | |
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| 0.7855 | 1.79 | 1850 | 0.7905 | |
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| 0.7834 | 1.84 | 1900 | 0.7878 | |
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| 0.7812 | 1.89 | 1950 | 0.7854 | |
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| 0.7865 | 1.94 | 2000 | 0.7847 | |
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| 0.7875 | 1.99 | 2050 | 0.7837 | |
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| 0.7764 | 2.04 | 2100 | 0.7815 | |
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| 0.7676 | 2.08 | 2150 | 0.7807 | |
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| 0.7716 | 2.13 | 2200 | 0.7796 | |
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| 0.777 | 2.18 | 2250 | 0.7781 | |
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| 0.7706 | 2.23 | 2300 | 0.7769 | |
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| 0.7669 | 2.28 | 2350 | 0.7748 | |
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| 0.771 | 2.33 | 2400 | 0.7742 | |
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| 0.7501 | 2.38 | 2450 | 0.7728 | |
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| 0.7653 | 2.42 | 2500 | 0.7713 | |
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| 0.7715 | 2.47 | 2550 | 0.7699 | |
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| 0.7588 | 2.52 | 2600 | 0.7694 | |
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| 0.7665 | 2.57 | 2650 | 0.7676 | |
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| 0.7616 | 2.62 | 2700 | 0.7658 | |
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| 0.7597 | 2.67 | 2750 | 0.7654 | |
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| 0.756 | 2.71 | 2800 | 0.7644 | |
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| 0.7517 | 2.76 | 2850 | 0.7628 | |
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| 0.7561 | 2.81 | 2900 | 0.7628 | |
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| 0.7413 | 2.86 | 2950 | 0.7620 | |
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| 0.7545 | 2.91 | 3000 | 0.7603 | |
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| 0.7442 | 2.96 | 3050 | 0.7592 | |
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| 0.7454 | 3.01 | 3100 | 0.7589 | |
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| 0.7575 | 3.05 | 3150 | 0.7583 | |
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| 0.739 | 3.1 | 3200 | 0.7571 | |
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| 0.7446 | 3.15 | 3250 | 0.7558 | |
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| 0.7428 | 3.2 | 3300 | 0.7557 | |
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| 0.737 | 3.25 | 3350 | 0.7553 | |
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| 0.7512 | 3.3 | 3400 | 0.7536 | |
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| 0.7447 | 3.34 | 3450 | 0.7525 | |
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| 0.7417 | 3.39 | 3500 | 0.7525 | |
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| 0.7403 | 3.44 | 3550 | 0.7512 | |
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| 0.761 | 3.49 | 3600 | 0.7502 | |
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| 0.7475 | 3.54 | 3650 | 0.7498 | |
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| 0.7535 | 3.59 | 3700 | 0.7486 | |
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| 0.733 | 3.64 | 3750 | 0.7483 | |
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| 0.7347 | 3.68 | 3800 | 0.7470 | |
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| 0.7439 | 3.73 | 3850 | 0.7470 | |
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| 0.7417 | 3.78 | 3900 | 0.7460 | |
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| 0.7383 | 3.83 | 3950 | 0.7460 | |
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| 0.7316 | 3.88 | 4000 | 0.7450 | |
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| 0.7273 | 3.93 | 4050 | 0.7442 | |
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| 0.7376 | 3.97 | 4100 | 0.7440 | |
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| 0.73 | 4.02 | 4150 | 0.7424 | |
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| 0.732 | 4.07 | 4200 | 0.7429 | |
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| 0.7278 | 4.12 | 4250 | 0.7419 | |
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| 0.721 | 4.17 | 4300 | 0.7416 | |
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| 0.7309 | 4.22 | 4350 | 0.7410 | |
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| 0.7273 | 4.27 | 4400 | 0.7400 | |
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| 0.7297 | 4.31 | 4450 | 0.7395 | |
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| 0.7321 | 4.36 | 4500 | 0.7385 | |
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| 0.7348 | 4.41 | 4550 | 0.7381 | |
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| 0.7251 | 4.46 | 4600 | 0.7371 | |
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| 0.7175 | 4.51 | 4650 | 0.7372 | |
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| 0.7356 | 4.56 | 4700 | 0.7368 | |
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| 0.7306 | 4.6 | 4750 | 0.7363 | |
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| 0.7248 | 4.65 | 4800 | 0.7359 | |
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| 0.7266 | 4.7 | 4850 | 0.7343 | |
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| 0.7243 | 4.75 | 4900 | 0.7349 | |
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| 0.7256 | 4.8 | 4950 | 0.7338 | |
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| 0.7301 | 4.85 | 5000 | 0.7335 | |
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| 0.7266 | 4.9 | 5050 | 0.7327 | |
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| 0.7229 | 4.94 | 5100 | 0.7321 | |
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| 0.7355 | 4.99 | 5150 | 0.7315 | |
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| 0.7207 | 5.04 | 5200 | 0.7317 | |
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| 0.7157 | 5.09 | 5250 | 0.7314 | |
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| 0.7214 | 5.14 | 5300 | 0.7299 | |
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| 0.7104 | 5.19 | 5350 | 0.7304 | |
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| 0.7059 | 5.24 | 5400 | 0.7296 | |
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| 0.7181 | 5.28 | 5450 | 0.7295 | |
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| 0.7226 | 5.33 | 5500 | 0.7286 | |
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| 0.7077 | 5.38 | 5550 | 0.7282 | |
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| 0.7239 | 5.43 | 5600 | 0.7276 | |
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| 0.7159 | 5.48 | 5650 | 0.7277 | |
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| 0.7169 | 5.53 | 5700 | 0.7271 | |
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| 0.7101 | 5.57 | 5750 | 0.7269 | |
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| 0.7146 | 5.62 | 5800 | 0.7262 | |
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| 0.7191 | 5.67 | 5850 | 0.7265 | |
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| 0.7124 | 5.72 | 5900 | 0.7248 | |
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| 0.7085 | 5.77 | 5950 | 0.7238 | |
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| 0.7052 | 5.82 | 6000 | 0.7235 | |
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| 0.7222 | 5.87 | 6050 | 0.7222 | |
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| 0.7089 | 5.91 | 6100 | 0.7221 | |
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| 0.7088 | 5.96 | 6150 | 0.7222 | |
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| 0.7017 | 6.01 | 6200 | 0.7218 | |
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| 0.7079 | 6.06 | 6250 | 0.7218 | |
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| 0.7209 | 6.11 | 6300 | 0.7211 | |
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| 0.691 | 6.16 | 6350 | 0.7210 | |
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| 0.7035 | 6.2 | 6400 | 0.7203 | |
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| 0.7075 | 6.25 | 6450 | 0.7207 | |
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| 0.7036 | 6.3 | 6500 | 0.7200 | |
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| 0.7023 | 6.35 | 6550 | 0.7189 | |
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| 0.7201 | 6.4 | 6600 | 0.7192 | |
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| 0.7021 | 6.45 | 6650 | 0.7188 | |
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| 0.6971 | 6.5 | 6700 | 0.7174 | |
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| 0.7087 | 6.54 | 6750 | 0.7184 | |
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| 0.7044 | 6.59 | 6800 | 0.7176 | |
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| 0.6921 | 6.64 | 6850 | 0.7179 | |
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| 0.7079 | 6.69 | 6900 | 0.7166 | |
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| 0.6908 | 6.74 | 6950 | 0.7158 | |
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| 0.687 | 6.79 | 7000 | 0.7158 | |
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| 0.696 | 6.83 | 7050 | 0.7148 | |
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| 0.6954 | 6.88 | 7100 | 0.7152 | |
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| 0.7103 | 6.93 | 7150 | 0.7143 | |
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| 0.6999 | 6.98 | 7200 | 0.7140 | |
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| 0.699 | 7.03 | 7250 | 0.7138 | |
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| 0.6959 | 7.08 | 7300 | 0.7138 | |
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| 0.6871 | 7.13 | 7350 | 0.7122 | |
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| 0.6941 | 7.17 | 7400 | 0.7131 | |
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| 0.6931 | 7.22 | 7450 | 0.7132 | |
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| 0.707 | 7.27 | 7500 | 0.7110 | |
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| 0.6911 | 7.32 | 7550 | 0.7122 | |
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| 0.7036 | 7.37 | 7600 | 0.7113 | |
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| 0.7105 | 7.42 | 7650 | 0.7107 | |
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| 0.7035 | 7.46 | 7700 | 0.7108 | |
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| 0.6901 | 7.51 | 7750 | 0.7113 | |
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| 0.6944 | 7.56 | 7800 | 0.7096 | |
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| 0.6927 | 7.61 | 7850 | 0.7093 | |
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| 0.7052 | 7.66 | 7900 | 0.7090 | |
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| 0.7046 | 7.71 | 7950 | 0.7082 | |
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| 0.6949 | 7.76 | 8000 | 0.7082 | |
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| 0.6888 | 7.8 | 8050 | 0.7071 | |
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| 0.6916 | 7.85 | 8100 | 0.7071 | |
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| 0.6937 | 7.9 | 8150 | 0.7067 | |
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| 0.7077 | 7.95 | 8200 | 0.7066 | |
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| 0.6847 | 8.0 | 8250 | 0.7057 | |
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| 0.6908 | 8.05 | 8300 | 0.7056 | |
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| 0.6813 | 8.1 | 8350 | 0.7060 | |
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| 0.6756 | 8.14 | 8400 | 0.7055 | |
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| 0.7006 | 8.19 | 8450 | 0.7052 | |
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| 0.6842 | 8.24 | 8500 | 0.7035 | |
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| 0.6851 | 8.29 | 8550 | 0.7044 | |
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| 0.6944 | 8.34 | 8600 | 0.7042 | |
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| 0.6929 | 8.39 | 8650 | 0.7040 | |
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| 0.6924 | 8.43 | 8700 | 0.7037 | |
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| 0.6843 | 8.48 | 8750 | 0.7037 | |
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| 0.7005 | 8.53 | 8800 | 0.7028 | |
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| 0.6795 | 8.58 | 8850 | 0.7022 | |
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| 0.6946 | 8.63 | 8900 | 0.7019 | |
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| 0.6761 | 8.68 | 8950 | 0.7016 | |
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| 0.6817 | 8.73 | 9000 | 0.7012 | |
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| 0.6838 | 8.77 | 9050 | 0.7012 | |
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| 0.6877 | 8.82 | 9100 | 0.7006 | |
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| 0.6812 | 8.87 | 9150 | 0.7004 | |
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| 0.6966 | 8.92 | 9200 | 0.7005 | |
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| 0.6778 | 8.97 | 9250 | 0.6993 | |
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| 0.6844 | 9.02 | 9300 | 0.6991 | |
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| 0.6853 | 9.06 | 9350 | 0.7000 | |
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| 0.6839 | 9.11 | 9400 | 0.6998 | |
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| 0.6813 | 9.16 | 9450 | 0.6984 | |
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| 0.6903 | 9.21 | 9500 | 0.6985 | |
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| 0.6819 | 9.26 | 9550 | 0.6987 | |
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| 0.6749 | 9.31 | 9600 | 0.6980 | |
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| 0.6782 | 9.36 | 9650 | 0.6979 | |
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| 0.6805 | 9.4 | 9700 | 0.6975 | |
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| 0.6907 | 9.45 | 9750 | 0.6974 | |
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| 0.6854 | 9.5 | 9800 | 0.6967 | |
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| 0.6803 | 9.55 | 9850 | 0.6969 | |
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| 0.6854 | 9.6 | 9900 | 0.6964 | |
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| 0.6761 | 9.65 | 9950 | 0.6966 | |
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| 0.6939 | 9.69 | 10000 | 0.6959 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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