---
license: mit
base_model: microsoft/phi-2
tags:
- axolotl
- generated_from_trainer
- phi
- phi-2
- logical
- reasoning
- transformers
- text-generation-inference
model-index:
- name: phi-2-logical-sft
  results: []
datasets:
- garage-bAInd/Open-Platypus
---

<!-- 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)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
base_model: microsoft/phi-2
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

hub_model_id: MaziyarPanahi/phi-2-logical-sft
hf_use_auth_token: true


load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: garage-bAInd/Open-Platypus
    type: alpaca

dataset_prepared_path:
val_set_size: 0.05
output_dir: ./phi-2-logical-sft-out

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 2
optimizer: adamw_torch
adam_beta2: 0.95
adam_epsilon: 0.00001
max_grad_norm: 1.0
lr_scheduler: cosine
learning_rate: 0.000003

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: True
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.1
fsdp:
fsdp_config:
resize_token_embeddings_to_32x: true
special_tokens:
  pad_token: "<|endoftext|>"
```

</details><br>

# phi-2-logical-sft

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: 1.0075

## Prompt Template

```
### Instruction:

<prompt> (without the <>)

### Response:
```

## Examples

```
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.
### Instruction:

Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.

### Response:
```

```
Goku woke up one day in his hometown of Earth and went out for his daily training routine. He was feeling confident and happy as usual, until he received news that his dear friend Kirby had teamed up with Majin Buu to destroy the world.

Shocked and saddened by this news, Goku immediately knew he had to act fast to save his friends and loved ones. With his superhuman strength and incredible speed, he set off on a mission to stop the evil duo from carrying out their destructive plan.

Along the way, Goku encountered many challenges and obstacles, but he never gave up. He fought fiercely against Kirby and Majin Buu, using all of his powers and skills to try and defeat them.

Finally, after a long and grueling battle, Goku emerged victorious. He managed to convince Kirby to turn against Majin Buu and join forces with him to defeat the evil being once and for all. Together, they were able to defeat Majin Buu and save the world from destruction.

Goku felt a sense of relief and happiness wash over him as he realized that his friends were safe and the world was no longer in danger. He knew that he had done everything he could to protect those he cared about, and he was grateful for the opportunity to use his powers for good.
<|endoftext|>
```


## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8319        | 0.0   | 1    | 1.0229          |
| 0.8799        | 0.25  | 71   | 1.0208          |
| 0.8349        | 0.5   | 142  | 1.0119          |
| 0.7798        | 0.76  | 213  | 1.0093          |
| 0.8317        | 1.01  | 284  | 1.0083          |
| 0.777         | 1.24  | 355  | 1.0080          |
| 0.7544        | 1.49  | 426  | 1.0075          |
| 0.7037        | 1.74  | 497  | 1.0075          |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.0