---
license: other
library_name: peft
tags:
- generated_from_trainer
base_model: microsoft/phi-2
model-index:
- name: phi-2-sft-out
  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. -->

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# phi-2-sft-out

This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/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