---
library_name: peft
license: apache-2.0
base_model: echarlaix/tiny-random-PhiForCausalLM
tags:
- axolotl
- generated_from_trainer
model-index:
- name: ee1dcdaf-4c63-4037-a8dd-e9d476c5a849
  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
adapter: lora
base_model: echarlaix/tiny-random-PhiForCausalLM
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - da9e86118b09e5a5_train_data.json
  ds_type: json
  field: question
  path: /workspace/input_data/da9e86118b09e5a5_train_data.json
  type: completion
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: true
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: ardaspear/ee1dcdaf-4c63-4037-a8dd-e9d476c5a849
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: 0
logging_steps: 3
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: true
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
  0: 72GB
max_steps: 100
micro_batch_size: 4
mlflow_experiment_name: /tmp/da9e86118b09e5a5_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: false
sample_packing: false
saves_per_epoch: 4
sequence_len: 1024
special_tokens:
  pad_token: <|endoftext|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: leixa-personal
wandb_mode: online
wandb_name: ee1dcdaf-4c63-4037-a8dd-e9d476c5a849
wandb_project: Gradients-On-Two
wandb_run: your_name
wandb_runid: ee1dcdaf-4c63-4037-a8dd-e9d476c5a849
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null

```

</details><br>

# ee1dcdaf-4c63-4037-a8dd-e9d476c5a849

This model is a fine-tuned version of [echarlaix/tiny-random-PhiForCausalLM](https://huggingface.co/echarlaix/tiny-random-PhiForCausalLM) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.8274

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 100

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0051 | 1    | 6.9396          |
| 6.933         | 0.0459 | 9    | 6.9278          |
| 6.9044        | 0.0918 | 18   | 6.8975          |
| 6.8733        | 0.1378 | 27   | 6.8667          |
| 6.8474        | 0.1837 | 36   | 6.8465          |
| 6.8412        | 0.2296 | 45   | 6.8371          |
| 6.8328        | 0.2755 | 54   | 6.8328          |
| 6.8329        | 0.3214 | 63   | 6.8303          |
| 6.8278        | 0.3673 | 72   | 6.8288          |
| 6.8312        | 0.4133 | 81   | 6.8278          |
| 6.8293        | 0.4592 | 90   | 6.8275          |
| 6.8296        | 0.5051 | 99   | 6.8274          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1