---
library_name: peft
base_model: heegyu/WizardVicuna2-13b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d1526c12-8a5c-45c2-8a7c-e005e4428b34
  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: qlora
auto_resume_from_checkpoints: true
base_model: heegyu/WizardVicuna2-13b-hf
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
  - afb1fdb32bdf02c6_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/afb1fdb32bdf02c6_train_data.json
  type:
    field_instruction: premise
    field_output: hypothesis
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 100
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: error577/d1526c12-8a5c-45c2-8a7c-e005e4428b34
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 128
lora_dropout: 0.05
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: 1
mlflow_experiment_name: /tmp/afb1fdb32bdf02c6_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch_4bit
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: 512
special_tokens:
  pad_token: </s>
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: d8eda0ee-8aeb-47d4-bebc-dd3dba382021
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d8eda0ee-8aeb-47d4-bebc-dd3dba382021
warmup_steps: 30
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# d1526c12-8a5c-45c2-8a7c-e005e4428b34

This model is a fine-tuned version of [heegyu/WizardVicuna2-13b-hf](https://huggingface.co/heegyu/WizardVicuna2-13b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5516

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_TORCH_4BIT 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 |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0003 | 1    | 4.5872          |
| 4.0918        | 0.0311 | 100  | 3.9953          |
| 3.8106        | 0.0621 | 200  | 3.5150          |
| 3.6256        | 0.0932 | 300  | 3.4052          |
| 3.2791        | 0.1242 | 400  | 3.2580          |
| 3.2456        | 0.1553 | 500  | 3.0634          |
| 2.9189        | 0.1864 | 600  | 3.0874          |
| 3.0159        | 0.2174 | 700  | 2.9400          |
| 3.0506        | 0.2485 | 800  | 2.9574          |
| 3.1942        | 0.2795 | 900  | 2.8810          |
| 2.7516        | 0.3106 | 1000 | 2.8531          |
| 2.9822        | 0.3417 | 1100 | 2.8689          |
| 2.7943        | 0.3727 | 1200 | 2.8961          |
| 2.7773        | 0.4038 | 1300 | 2.7702          |
| 3.0787        | 0.4349 | 1400 | 2.7362          |
| 2.6754        | 0.4659 | 1500 | 2.7145          |
| 2.882         | 0.4970 | 1600 | 2.6246          |
| 2.8287        | 0.5280 | 1700 | 2.6403          |
| 2.8178        | 0.5591 | 1800 | 2.5918          |
| 2.8114        | 0.5902 | 1900 | 2.6481          |
| 3.0178        | 0.6212 | 2000 | 2.5809          |
| 2.7718        | 0.6523 | 2100 | 2.5701          |
| 2.785         | 0.6833 | 2200 | 2.5290          |
| 2.8581        | 0.7144 | 2300 | 2.5949          |
| 2.8815        | 0.7455 | 2400 | 2.6250          |
| 2.9384        | 0.7765 | 2500 | 2.5516          |


### Framework versions

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