---
license: mit
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: microsoft/phi-1_5
model-index:
- name: test_upload
  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)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
adam_beta2: 0.95
adam_epsilon: 1.0e-05
adapter: qlora
base_model: microsoft/phi-1_5
dataset_prepared_path: null
datasets:
- path: garage-bAInd/Open-Platypus
  type: alpaca
debug: null
deepspeed: null
early_stopping_patience: null
evals_per_epoch: 1
flash_attention: true
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: true
hub_model_id: AdamRTomkins/test_upload
hub_strategy: end
learning_rate: 3.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 2
micro_batch_size: 1
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_torch
output_dir: ./outputs/phi-sft-out
pad_to_sequence_len: true
resize_token_embeddings_to_32x: true
resume_from_checkpoint: null
sample_packing: true
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
  pad_token: <|endoftext|>
strict: false
tokenizer_type: AutoTokenizer
val_set_size: 0.05
wandb_entity: null
wandb_log_model: null
wandb_name: null
wandb_project: null
wandb_watch: null
warmup_steps: 100
weight_decay: 0.1
xformers_attention: null

```

</details><br>

# test_upload

This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3469

## 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: 100
- training_steps: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.6676        | 0.0002 | 2    | 1.3469          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1