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--- |
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base_model: meta-llama/Meta-Llama-3-8B-Instruct |
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datasets: |
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- nthakur/mirage-gpt-4o-sft-instruct-llama-3 |
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- nthakur/mirage-meta-llama-3-mistral-sft-instruct-meta-llama-tokenizer |
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library_name: peft |
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license: llama3 |
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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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model-index: |
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- name: Meta-Llama-3-8B-Instruct-mirage-all-teacher-instruct-llama-3-sft |
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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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# Meta-Llama-3-8B-Instruct-mirage-all-teacher-instruct-llama-3-sft |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the nthakur/mirage-gpt-4o-sft-instruct-llama-3 and the nthakur/mirage-meta-llama-3-mistral-sft-instruct-meta-llama-tokenizer datasets. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2593 |
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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: 0.0002 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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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: 16 |
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- total_eval_batch_size: 8 |
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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_ratio: 0.1 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 0.3535 | 0.0412 | 200 | 0.3586 | |
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| 0.4117 | 0.0824 | 400 | 0.3371 | |
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| 0.3577 | 0.1236 | 600 | 0.3277 | |
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| 0.3594 | 0.1649 | 800 | 0.3194 | |
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| 0.3603 | 0.2061 | 1000 | 0.3096 | |
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| 0.3633 | 0.2473 | 1200 | 0.3063 | |
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| 0.3078 | 0.2885 | 1400 | 0.3000 | |
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| 0.3274 | 0.3297 | 1600 | 0.2948 | |
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| 0.3474 | 0.3709 | 1800 | 0.2925 | |
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| 0.3401 | 0.4122 | 2000 | 0.2875 | |
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| 0.3124 | 0.4534 | 2200 | 0.2839 | |
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| 0.3095 | 0.4946 | 2400 | 0.2802 | |
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| 0.3532 | 0.5358 | 2600 | 0.2775 | |
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| 0.301 | 0.5770 | 2800 | 0.2757 | |
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| 0.3204 | 0.6182 | 3000 | 0.2712 | |
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| 0.3158 | 0.6595 | 3200 | 0.2687 | |
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| 0.3032 | 0.7007 | 3400 | 0.2667 | |
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| 0.2851 | 0.7419 | 3600 | 0.2645 | |
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| 0.2903 | 0.7831 | 3800 | 0.2629 | |
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| 0.2943 | 0.8243 | 4000 | 0.2613 | |
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| 0.2787 | 0.8655 | 4200 | 0.2603 | |
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| 0.2558 | 0.9067 | 4400 | 0.2596 | |
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| 0.3107 | 0.9480 | 4600 | 0.2593 | |
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| 0.2894 | 0.9892 | 4800 | 0.2593 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
