---
library_name: peft
base_model: katuni4ka/tiny-random-olmo-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f30e72fb-6193-420a-9b38-443b7dc3bdaf
  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: katuni4ka/tiny-random-olmo-hf
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 53a34d491d7a389b_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/53a34d491d7a389b_train_data.json
  type:
    field_input: orig_instruction
    field_instruction: prompt
    field_output: chosen
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: diaenra/f30e72fb-6193-420a-9b38-443b7dc3bdaf
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
  0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/53a34d491d7a389b_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-5
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: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: diaenra-tao-miner
wandb_mode: online
wandb_name: 97ac7cdd-1bb7-4029-b62a-2b06090ed76f
wandb_project: tao
wandb_run: diaenra
wandb_runid: 97ac7cdd-1bb7-4029-b62a-2b06090ed76f
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# f30e72fb-6193-420a-9b38-443b7dc3bdaf

This model is a fine-tuned version of [katuni4ka/tiny-random-olmo-hf](https://huggingface.co./katuni4ka/tiny-random-olmo-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 10.4454

## 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: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 10.831        | 0.0120 | 1    | 10.8072         |
| 10.7191       | 0.6006 | 50   | 10.6072         |
| 10.0484       | 1.2012 | 100  | 10.5228         |
| 10.6908       | 1.8018 | 150  | 10.4741         |
| 15.6116       | 2.4024 | 200  | 10.4454         |


### Framework versions

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