File size: 3,104 Bytes
fe93121 b11aa57 fe93121 b11aa57 fe93121 b11aa57 fe93121 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
datasets:
- nthakur/mirage-gpt-4o-sft-instruct-llama-3
- nthakur/mirage-meta-llama-3-mistral-sft-instruct-meta-llama-tokenizer
library_name: peft
license: llama3
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
model-index:
- name: Meta-Llama-3-8B-Instruct-mirage-all-teacher-instruct-llama-3-sft
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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](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.
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 |