---
library_name: peft
base_model: HuggingFaceH4/tiny-random-LlamaForCausalLM
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 90dbfeae-95d3-47a2-a988-98c5906bae01
  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: HuggingFaceH4/tiny-random-LlamaForCausalLM
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 0e2a206b6cbe63d9_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/0e2a206b6cbe63d9_train_data.json
  type:
    field_instruction: prompt
    field_output: chosen
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
device_map:
  ? ''
  : 0,1,2,3,4,5,6,7
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: true
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/90dbfeae-95d3-47a2-a988-98c5906bae01
hub_repo: null
hub_strategy: null
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- down_proj
- up_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 3600
micro_batch_size: 4
mlflow_experiment_name: /tmp/0e2a206b6cbe63d9_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
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: 100
sequence_len: 2048
special_tokens:
  pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.03361547925588775
wandb_entity: null
wandb_mode: online
wandb_name: 65ecc54e-1ce1-46d0-8d8f-a58fd50f5f0f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 65ecc54e-1ce1-46d0-8d8f-a58fd50f5f0f
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# 90dbfeae-95d3-47a2-a988-98c5906bae01

This model is a fine-tuned version of [HuggingFaceH4/tiny-random-LlamaForCausalLM](https://huggingface.co/HuggingFaceH4/tiny-random-LlamaForCausalLM) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 10.3113

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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: 3600

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 10.3673       | 0.0002 | 1    | 10.3688         |
| 10.3363       | 0.0223 | 100  | 10.3367         |
| 10.3308       | 0.0445 | 200  | 10.3281         |
| 10.324        | 0.0668 | 300  | 10.3221         |
| 10.3186       | 0.0890 | 400  | 10.3186         |
| 10.3196       | 0.1113 | 500  | 10.3180         |
| 10.3207       | 0.1336 | 600  | 10.3175         |
| 10.321        | 0.1558 | 700  | 10.3171         |
| 10.3129       | 0.1781 | 800  | 10.3167         |
| 10.3111       | 0.2004 | 900  | 10.3158         |
| 10.3192       | 0.2226 | 1000 | 10.3152         |
| 10.3219       | 0.2449 | 1100 | 10.3147         |
| 10.3197       | 0.2671 | 1200 | 10.3142         |
| 10.3143       | 0.2894 | 1300 | 10.3139         |
| 10.3172       | 0.3117 | 1400 | 10.3135         |
| 10.315        | 0.3339 | 1500 | 10.3132         |
| 10.3181       | 0.3562 | 1600 | 10.3128         |
| 10.3104       | 0.3785 | 1700 | 10.3125         |
| 10.3086       | 0.4007 | 1800 | 10.3123         |
| 10.3091       | 0.4230 | 1900 | 10.3121         |
| 10.3141       | 0.4452 | 2000 | 10.3119         |
| 10.314        | 0.4675 | 2100 | 10.3118         |
| 10.3158       | 0.4898 | 2200 | 10.3117         |
| 10.3147       | 0.5120 | 2300 | 10.3117         |
| 10.3192       | 0.5343 | 2400 | 10.3116         |
| 10.3062       | 0.5565 | 2500 | 10.3115         |
| 10.3046       | 0.5788 | 2600 | 10.3115         |
| 10.3184       | 0.6011 | 2700 | 10.3114         |
| 10.3104       | 0.6233 | 2800 | 10.3114         |
| 10.3105       | 0.6456 | 2900 | 10.3114         |
| 10.3174       | 0.6679 | 3000 | 10.3114         |
| 10.3149       | 0.6901 | 3100 | 10.3113         |
| 10.3201       | 0.7124 | 3200 | 10.3113         |
| 10.3111       | 0.7346 | 3300 | 10.3113         |
| 10.3115       | 0.7569 | 3400 | 10.3113         |
| 10.3168       | 0.7792 | 3500 | 10.3113         |
| 10.3123       | 0.8014 | 3600 | 10.3113         |


### Framework versions

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