---
base_model: microsoft/Phi-3.5-mini-instruct
library_name: peft
license: mit
tags:
- axolotl
- generated_from_trainer
model-index:
- name: phi-3.5-alpaca-test-classifier
  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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
strict: false

base_model: microsoft/Phi-3.5-mini-instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true

chat_template: phi_3
datasets:
  - path: fozziethebeat/alpaca_messages_classifier_2k_test
    type: chat_template
    split: train
    chat_template: phi_3
    field_messages: messages
    message_field_role: role
    message_field_content: content
    roles:
      user:
        - user
      assistant:
        - assistant

dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/lora-out

sequence_len: 2048
sample_packing: false
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 8
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 5.0e-5

train_on_inputs: false
group_by_length: false
bfloat16: true
bf16: true
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
s2_attention:

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 4
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:

```

</details><br>

# phi-3.5-alpaca-test-classifier

This model is a fine-tuned version of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co./microsoft/Phi-3.5-mini-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1174

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 11.7206       | 0.0187 | 1    | 11.9120         |
| 9.4452        | 0.2617 | 14   | 9.1059          |
| 2.2582        | 0.5234 | 28   | 1.8353          |
| 0.1463        | 0.7850 | 42   | 0.1658          |
| 0.1315        | 1.0467 | 56   | 0.1291          |
| 0.1207        | 1.3084 | 70   | 0.1218          |
| 0.1238        | 1.5701 | 84   | 0.1196          |
| 0.1103        | 1.8318 | 98   | 0.1174          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1