---
library_name: peft
license: mit
base_model: EleutherAI/gpt-neo-125m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 19783dba-2611-430a-89e2-4d277105a2fb
results: []
---
[
](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config
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:
- 9400c082b072ce22_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9400c082b072ce22_train_data.json
type:
field_instruction: ja
field_output: en
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 4
eval_max_new_tokens: 128
eval_steps: 150
eval_table_size: null
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: Romain-XV/19783dba-2611-430a-89e2-4d277105a2fb
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.3
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 9660
micro_batch_size: 2
mlflow_experiment_name: /tmp/9400c082b072ce22_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: null
sample_packing: false
save_steps: 150
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: 0.03388084783433621
wandb_entity: null
wandb_mode: online
wandb_name: 97f965f0-6c2c-4001-93f0-b3bd5a572767
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 97f965f0-6c2c-4001-93f0-b3bd5a572767
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
# 19783dba-2611-430a-89e2-4d277105a2fb
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: 1.9652
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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: 9660
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 22.3081 | 0.0001 | 1 | 5.3931 |
| 12.1969 | 0.0084 | 150 | 3.0791 |
| 12.8922 | 0.0168 | 300 | 2.9534 |
| 12.5402 | 0.0252 | 450 | 2.8506 |
| 10.4376 | 0.0337 | 600 | 2.7774 |
| 9.6342 | 0.0421 | 750 | 2.7315 |
| 11.2411 | 0.0505 | 900 | 2.6845 |
| 12.9895 | 0.0589 | 1050 | 2.6467 |
| 12.0386 | 0.0673 | 1200 | 2.6053 |
| 11.1212 | 0.0757 | 1350 | 2.5762 |
| 9.9151 | 0.0842 | 1500 | 2.5369 |
| 11.2688 | 0.0926 | 1650 | 2.5001 |
| 10.0221 | 0.1010 | 1800 | 2.4761 |
| 10.3173 | 0.1094 | 1950 | 2.4375 |
| 10.428 | 0.1178 | 2100 | 2.4349 |
| 7.0369 | 0.1262 | 2250 | 2.3860 |
| 10.4659 | 0.1347 | 2400 | 2.3725 |
| 10.4187 | 0.1431 | 2550 | 2.3579 |
| 6.8085 | 0.1515 | 2700 | 2.3285 |
| 12.6518 | 0.1599 | 2850 | 2.3110 |
| 9.9324 | 0.1683 | 3000 | 2.2927 |
| 7.8111 | 0.1767 | 3150 | 2.2739 |
| 9.2326 | 0.1852 | 3300 | 2.2593 |
| 8.6382 | 0.1936 | 3450 | 2.2342 |
| 8.518 | 0.2020 | 3600 | 2.2290 |
| 6.4198 | 0.2104 | 3750 | 2.2118 |
| 9.0537 | 0.2188 | 3900 | 2.2064 |
| 6.6054 | 0.2272 | 4050 | 2.1808 |
| 8.1502 | 0.2357 | 4200 | 2.1758 |
| 7.229 | 0.2441 | 4350 | 2.1579 |
| 7.0952 | 0.2525 | 4500 | 2.1411 |
| 7.7773 | 0.2609 | 4650 | 2.1294 |
| 9.354 | 0.2693 | 4800 | 2.1157 |
| 9.6896 | 0.2777 | 4950 | 2.1120 |
| 9.817 | 0.2862 | 5100 | 2.0999 |
| 9.7308 | 0.2946 | 5250 | 2.0837 |
| 7.0272 | 0.3030 | 5400 | 2.0796 |
| 9.446 | 0.3114 | 5550 | 2.0694 |
| 9.1402 | 0.3198 | 5700 | 2.0556 |
| 7.8589 | 0.3282 | 5850 | 2.0542 |
| 8.3354 | 0.3367 | 6000 | 2.0445 |
| 8.081 | 0.3451 | 6150 | 2.0343 |
| 6.7192 | 0.3535 | 6300 | 2.0259 |
| 10.2732 | 0.3619 | 6450 | 2.0235 |
| 9.3245 | 0.3703 | 6600 | 2.0137 |
| 8.6904 | 0.3787 | 6750 | 2.0092 |
| 6.4253 | 0.3872 | 6900 | 2.0042 |
| 8.0254 | 0.3956 | 7050 | 1.9975 |
| 10.3048 | 0.4040 | 7200 | 1.9963 |
| 9.2663 | 0.4124 | 7350 | 1.9909 |
| 8.596 | 0.4208 | 7500 | 1.9860 |
| 9.4026 | 0.4292 | 7650 | 1.9820 |
| 7.5361 | 0.4377 | 7800 | 1.9791 |
| 10.1732 | 0.4461 | 7950 | 1.9773 |
| 9.5052 | 0.4545 | 8100 | 1.9737 |
| 9.1775 | 0.4629 | 8250 | 1.9720 |
| 5.179 | 0.4713 | 8400 | 1.9702 |
| 6.0604 | 0.4797 | 8550 | 1.9688 |
| 7.6645 | 0.4882 | 8700 | 1.9676 |
| 6.7768 | 0.4966 | 8850 | 1.9666 |
| 8.6168 | 0.5050 | 9000 | 1.9657 |
| 9.4105 | 0.5134 | 9150 | 1.9658 |
| 8.4106 | 0.5218 | 9300 | 1.9655 |
| 5.5724 | 0.5302 | 9450 | 1.9654 |
| 5.8533 | 0.5387 | 9600 | 1.9652 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1