phi-2-sft-out
This model is a fine-tuned version of microsoft/phi-2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2813
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: 3e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.0 | 1 | 1.7973 |
| 1.9767 | 0.25 | 5290 | 1.4832 |
| 1.8474 | 0.5 | 10580 | 1.4356 |
| 1.8121 | 0.75 | 15870 | 1.4022 |
| 1.8333 | 1.0 | 21160 | 1.3678 |
| 1.6601 | 1.25 | 26450 | 1.3508 |
| 1.5452 | 1.5 | 31740 | 1.3357 |
| 1.7381 | 1.75 | 37030 | 1.3191 |
| 1.6256 | 2.0 | 42320 | 1.3090 |
| 1.5521 | 2.25 | 47610 | 1.2961 |
| 1.8318 | 2.5 | 52900 | 1.2910 |
| 1.6761 | 2.75 | 58190 | 1.2901 |
| 1.6312 | 3.0 | 63480 | 1.2879 |
| 1.7003 | 3.25 | 68770 | 1.2820 |
| 1.6915 | 3.5 | 74060 | 1.2814 |
| 1.5757 | 3.75 | 79350 | 1.2813 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
Training procedure
The following bitsandbytes quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
Framework versions
- PEFT 0.6.0