---
library_name: peft
license: other
base_model: facebook/opt-125m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 18092189-46f9-4027-aa36-175d1e605e4a
  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
auto_resume_from_checkpoints: true
base_model: facebook/opt-125m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
dataset_processes: 6
datasets:
- data_files:
  - 818f8460304dc8f3_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/818f8460304dc8f3_train_data.json
  type:
    field_input: hypothesis
    field_instruction: premise
    field_output: augmented_hypothesis
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 200
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: error577/18092189-46f9-4027-aa36-175d1e605e4a
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: null
micro_batch_size: 24
mlflow_experiment_name: /tmp/818f8460304dc8f3_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: 200
sequence_len: 256
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.005
wandb_entity: null
wandb_mode: online
wandb_name: af50a6d4-247f-4e76-817b-1cfd6dd7131d
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: af50a6d4-247f-4e76-817b-1cfd6dd7131d
warmup_steps: 30
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# 18092189-46f9-4027-aa36-175d1e605e4a

This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1241

## 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: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- 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: 30
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 12.7141       | 0.0005 | 1    | 3.1690          |
| 1.3992        | 0.0960 | 200  | 0.3349          |
| 1.0472        | 0.1921 | 400  | 0.2430          |
| 0.883         | 0.2881 | 600  | 0.2006          |
| 0.7357        | 0.3842 | 800  | 0.1706          |
| 0.7221        | 0.4802 | 1000 | 0.1574          |
| 0.7825        | 0.5763 | 1200 | 0.1546          |
| 0.5444        | 0.6723 | 1400 | 0.1439          |
| 0.6198        | 0.7684 | 1600 | 0.1449          |
| 0.5838        | 0.8644 | 1800 | 0.1378          |
| 0.5814        | 0.9605 | 2000 | 0.1363          |
| 0.552         | 1.0565 | 2200 | 0.1346          |
| 0.5699        | 1.1526 | 2400 | 0.1337          |
| 0.5114        | 1.2486 | 2600 | 0.1359          |
| 0.5164        | 1.3447 | 2800 | 0.1310          |
| 0.4851        | 1.4407 | 3000 | 0.1314          |
| 0.4927        | 1.5368 | 3200 | 0.1320          |
| 0.485         | 1.6328 | 3400 | 0.1287          |
| 0.5204        | 1.7289 | 3600 | 0.1270          |
| 0.5643        | 1.8249 | 3800 | 0.1277          |
| 0.525         | 1.9210 | 4000 | 0.1275          |
| 0.4682        | 2.0170 | 4200 | 0.1264          |
| 0.5078        | 2.1131 | 4400 | 0.1259          |
| 0.5088        | 2.2091 | 4600 | 0.1257          |
| 0.5096        | 2.3052 | 4800 | 0.1250          |
| 0.5158        | 2.4012 | 5000 | 0.1252          |
| 0.5353        | 2.4973 | 5200 | 0.1241          |
| 0.5068        | 2.5933 | 5400 | 0.1242          |
| 0.5512        | 2.6894 | 5600 | 0.1241          |
| 0.472         | 2.7854 | 5800 | 0.1242          |
| 0.4644        | 2.8815 | 6000 | 0.1242          |
| 0.47          | 2.9775 | 6200 | 0.1241          |


### Framework versions

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