---
library_name: peft
base_model: hf-tiny-model-private/tiny-random-OPTForCausalLM
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 942cfd35-534e-4ab7-9dd2-335ce292d7c2
  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: hf-tiny-model-private/tiny-random-OPTForCausalLM
bf16: true
chat_template: llama3
dataloader_num_workers: 6
dataset_prepared_path: null
datasets:
- data_files:
  - 9742221b6e81649f_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/9742221b6e81649f_train_data.json
  type:
    field_input: language
    field_instruction: company
    field_output: sentence
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping:
  metric: eval_loss
  mode: min
  patience: 3
eval_max_new_tokens: 128
eval_steps: 200
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: true
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: true
hub_model_id: error577/942cfd35-534e-4ab7-9dd2-335ce292d7c2
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: 16
lora_dropout: 0.3
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 3500
micro_batch_size: 16
mlflow_experiment_name: /tmp/9742221b6e81649f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 50
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: 64
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.02
wandb_entity: null
wandb_mode: online
wandb_name: 1be7f53b-7c51-4d01-9396-e086bf3f09dd
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 1be7f53b-7c51-4d01-9396-e086bf3f09dd
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null

```

</details><br>

# 942cfd35-534e-4ab7-9dd2-335ce292d7c2

This model is a fine-tuned version of [hf-tiny-model-private/tiny-random-OPTForCausalLM](https://huggingface.co/hf-tiny-model-private/tiny-random-OPTForCausalLM) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.9015

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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: 3500

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 27.7344       | 0.0025 | 1    | 6.9353          |
| 27.4324       | 0.5057 | 200  | 6.9087          |
| 27.6605       | 1.0114 | 400  | 6.9080          |
| 27.6448       | 1.5171 | 600  | 6.9054          |
| 27.6474       | 2.0228 | 800  | 6.9055          |
| 27.6403       | 2.5284 | 1000 | 6.9049          |
| 27.6381       | 3.0341 | 1200 | 6.9040          |
| 27.6395       | 3.5398 | 1400 | 6.9025          |
| 27.6276       | 4.0455 | 1600 | 6.9028          |
| 27.6327       | 4.5512 | 1800 | 6.9028          |
| 27.6274       | 5.0569 | 2000 | 6.9020          |
| 27.6326       | 5.5626 | 2200 | 6.9019          |
| 27.6167       | 6.0683 | 2400 | 6.9010          |
| 27.645        | 6.5740 | 2600 | 6.9005          |
| 27.6261       | 7.0796 | 2800 | 6.9017          |
| 27.6214       | 7.5853 | 3000 | 6.9015          |
| 27.6217       | 8.0910 | 3200 | 6.9016          |
| 27.6327       | 8.5967 | 3400 | 6.9015          |


### Framework versions

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