---
library_name: peft
license: mit
base_model: EleutherAI/gpt-neo-125m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 4ce2ceca-17a8-49c5-83c2-35618600ca7f
  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: EleutherAI/gpt-neo-125m
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - aa838734bbefd9c8_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/aa838734bbefd9c8_train_data.json
  type:
    field_instruction: prompt
    field_output: GEITje-7B-ultra
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
device_map:
  ? ''
  : 0,1,2,3,4,5,6,7
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: false
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/4ce2ceca-17a8-49c5-83c2-35618600ca7f
hub_repo: null
hub_strategy: null
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
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: 16
lora_target_linear: true
lora_target_modules: null
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 4140
micro_batch_size: 4
mlflow_experiment_name: /tmp/aa838734bbefd9c8_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
sequence_len: 1024
special_tokens:
  pad_token: <|endoftext|>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.04
wandb_entity: null
wandb_mode: online
wandb_name: 0130bc26-f4a9-49f9-b382-a5b971eeaf04
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 0130bc26-f4a9-49f9-b382-a5b971eeaf04
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# 4ce2ceca-17a8-49c5-83c2-35618600ca7f

This model is a fine-tuned version of [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2814

## 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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: 3002

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 22.3516       | 0.0007 | 1    | 2.7369          |
| 20.2093       | 0.0666 | 100  | 2.5747          |
| 19.7446       | 0.1333 | 200  | 2.5223          |
| 20.6209       | 0.1999 | 300  | 2.4892          |
| 20.299        | 0.2666 | 400  | 2.4635          |
| 19.2732       | 0.3332 | 500  | 2.4406          |
| 19.7515       | 0.3998 | 600  | 2.4214          |
| 18.9969       | 0.4665 | 700  | 2.4035          |
| 19.396        | 0.5331 | 800  | 2.3888          |
| 18.8607       | 0.5998 | 900  | 2.3739          |
| 18.908        | 0.6664 | 1000 | 2.3621          |
| 18.5906       | 0.7330 | 1100 | 2.3504          |
| 18.9578       | 0.7997 | 1200 | 2.3409          |
| 19.0391       | 0.8663 | 1300 | 2.3319          |
| 18.6685       | 0.9329 | 1400 | 2.3235          |
| 15.9064       | 0.9996 | 1500 | 2.3170          |
| 16.0333       | 1.0664 | 1600 | 2.3104          |
| 17.8283       | 1.1330 | 1700 | 2.3052          |
| 18.3232       | 1.1997 | 1800 | 2.3006          |
| 16.2907       | 1.2663 | 1900 | 2.2966          |
| 20.2129       | 1.3329 | 2000 | 2.2936          |
| 17.9803       | 1.3996 | 2100 | 2.2905          |
| 17.2894       | 1.4662 | 2200 | 2.2879          |
| 18.2833       | 1.5329 | 2300 | 2.2861          |
| 16.9763       | 1.5995 | 2400 | 2.2844          |
| 18.0936       | 1.6661 | 2500 | 2.2834          |
| 17.6034       | 1.7328 | 2600 | 2.2823          |
| 17.8223       | 1.7994 | 2700 | 2.2819          |
| 18.5859       | 1.8661 | 2800 | 2.2816          |
| 17.2502       | 1.9327 | 2900 | 2.2815          |
| 17.3237       | 1.9993 | 3000 | 2.2814          |


### Framework versions

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