Meta-Llama-3-8B-Instruct-mirage-all-teacher-instruct-llama-3-sft
This model is a fine-tuned version of 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. It achieves the following results on the evaluation set:
- Loss: 0.2593
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
0.3535 | 0.0412 | 200 | 0.3586 |
0.4117 | 0.0824 | 400 | 0.3371 |
0.3577 | 0.1236 | 600 | 0.3277 |
0.3594 | 0.1649 | 800 | 0.3194 |
0.3603 | 0.2061 | 1000 | 0.3096 |
0.3633 | 0.2473 | 1200 | 0.3063 |
0.3078 | 0.2885 | 1400 | 0.3000 |
0.3274 | 0.3297 | 1600 | 0.2948 |
0.3474 | 0.3709 | 1800 | 0.2925 |
0.3401 | 0.4122 | 2000 | 0.2875 |
0.3124 | 0.4534 | 2200 | 0.2839 |
0.3095 | 0.4946 | 2400 | 0.2802 |
0.3532 | 0.5358 | 2600 | 0.2775 |
0.301 | 0.5770 | 2800 | 0.2757 |
0.3204 | 0.6182 | 3000 | 0.2712 |
0.3158 | 0.6595 | 3200 | 0.2687 |
0.3032 | 0.7007 | 3400 | 0.2667 |
0.2851 | 0.7419 | 3600 | 0.2645 |
0.2903 | 0.7831 | 3800 | 0.2629 |
0.2943 | 0.8243 | 4000 | 0.2613 |
0.2787 | 0.8655 | 4200 | 0.2603 |
0.2558 | 0.9067 | 4400 | 0.2596 |
0.3107 | 0.9480 | 4600 | 0.2593 |
0.2894 | 0.9892 | 4800 | 0.2593 |
Framework versions
- PEFT 0.10.0
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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