---
library_name: peft
license: llama3
base_model: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0
tags:
- axolotl
- generated_from_trainer
model-index:
- name: b87cb2d3-c6c0-437c-8e3a-05b2b2c28233
  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: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 0804ea238a964493_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/0804ea238a964493_train_data.json
  type:
    field_instruction: text
    field_output: positive
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 256
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 32
gradient_checkpointing: true
group_by_length: false
hub_model_id: mamung/b87cb2d3-c6c0-437c-8e3a-05b2b2c28233
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: 3
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
lr_scheduler: cosine
max_grad_norm: 2
max_steps: 100
micro_batch_size: 2
mlflow_experiment_name: /tmp/0804ea238a964493_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1.0e-05
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: eddysang
wandb_mode: online
wandb_name: ebdc83e2-621e-4250-8ebd-ae34fc8a673f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: ebdc83e2-621e-4250-8ebd-ae34fc8a673f
warmup_steps: 20
weight_decay: 0.02
xformers_attention: false

```

</details><br>

# b87cb2d3-c6c0-437c-8e3a-05b2b2c28233

This model is a fine-tuned version of [WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0](https://huggingface.co/WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3212

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- training_steps: 100

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0001 | 1    | 4.5439          |
| 2.7809        | 0.0013 | 9    | 2.1959          |
| 1.4818        | 0.0025 | 18   | 1.4632          |
| 1.3521        | 0.0038 | 27   | 1.3808          |
| 1.3521        | 0.0050 | 36   | 1.3701          |
| 1.2218        | 0.0063 | 45   | 1.3689          |
| 1.3547        | 0.0076 | 54   | 1.3416          |
| 1.3197        | 0.0088 | 63   | 1.3331          |
| 1.2828        | 0.0101 | 72   | 1.3292          |
| 1.3653        | 0.0114 | 81   | 1.3240          |
| 1.3475        | 0.0126 | 90   | 1.3224          |
| 1.3432        | 0.0139 | 99   | 1.3212          |


### Framework versions

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