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--- |
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base_model: ondevicellm/tinyllama_moe_v2 |
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tags: |
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- alignment-handbook |
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- trl |
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- sft |
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- generated_from_trainer |
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- trl |
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- sft |
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- generated_from_trainer |
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datasets: |
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- HuggingFaceH4/ultrachat_200k |
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model-index: |
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- name: tinyllama_moe_sft_routeraux_ep3 |
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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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# tinyllama_moe_sft_routeraux_ep3 |
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This model is a fine-tuned version of [ondevicellm/tinyllama_moe_v2](https://huggingface.co/ondevicellm/tinyllama_moe_v2) on the HuggingFaceH4/ultrachat_200k dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2954 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 120 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.72 | 0.09 | 100 | 1.6775 | |
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| 1.4985 | 0.18 | 200 | 1.4900 | |
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| 1.4482 | 0.26 | 300 | 1.4473 | |
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| 1.4152 | 0.35 | 400 | 1.4215 | |
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| 1.3777 | 0.44 | 500 | 1.4031 | |
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| 1.3932 | 0.53 | 600 | 1.3886 | |
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| 1.375 | 0.61 | 700 | 1.3762 | |
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| 1.3574 | 0.7 | 800 | 1.3657 | |
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| 1.349 | 0.79 | 900 | 1.3563 | |
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| 1.3276 | 0.88 | 1000 | 1.3481 | |
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| 1.3491 | 0.96 | 1100 | 1.3409 | |
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| 1.2812 | 1.05 | 1200 | 1.3358 | |
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| 1.2831 | 1.14 | 1300 | 1.3308 | |
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| 1.2917 | 1.23 | 1400 | 1.3258 | |
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| 1.2812 | 1.31 | 1500 | 1.3219 | |
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| 1.2819 | 1.4 | 1600 | 1.3178 | |
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| 1.2756 | 1.49 | 1700 | 1.3145 | |
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| 1.2584 | 1.58 | 1800 | 1.3107 | |
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| 1.2806 | 1.66 | 1900 | 1.3083 | |
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| 1.2815 | 1.75 | 2000 | 1.3054 | |
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| 1.2676 | 1.84 | 2100 | 1.3031 | |
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| 1.2388 | 1.93 | 2200 | 1.3011 | |
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| 1.2385 | 2.01 | 2300 | 1.3015 | |
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| 1.2459 | 2.1 | 2400 | 1.3000 | |
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| 1.2349 | 2.19 | 2500 | 1.2989 | |
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| 1.2277 | 2.28 | 2600 | 1.2981 | |
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| 1.2243 | 2.37 | 2700 | 1.2973 | |
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| 1.2298 | 2.45 | 2800 | 1.2967 | |
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| 1.2362 | 2.54 | 2900 | 1.2961 | |
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| 1.216 | 2.63 | 3000 | 1.2958 | |
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| 1.2381 | 2.72 | 3100 | 1.2957 | |
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| 1.2274 | 2.8 | 3200 | 1.2955 | |
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| 1.2235 | 2.89 | 3300 | 1.2954 | |
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| 1.2438 | 2.98 | 3400 | 1.2954 | |
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### Framework versions |
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- Transformers 4.37.0 |
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- Pytorch 2.1.2+cu118 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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