---
library_name: peft
license: llama3
base_model: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7b869ff4-f688-43b6-8109-9e97d7544b7a
  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: false
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 5964e954f8a774ad_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/5964e954f8a774ad_train_data.json
  type:
    field_input: text
    field_instruction: prompt
    field_output: score_level
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: true
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: lesso07/7b869ff4-f688-43b6-8109-9e97d7544b7a
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: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 70GiB
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/5964e954f8a774ad_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 25
save_strategy: steps
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 7b869ff4-f688-43b6-8109-9e97d7544b7a
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 7b869ff4-f688-43b6-8109-9e97d7544b7a
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null

```

</details><br>

# 7b869ff4-f688-43b6-8109-9e97d7544b7a

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: 0.4170

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 9.7459        | 0.0022 | 1    | 9.7895          |
| 8.6089        | 0.0110 | 5    | 8.1335          |
| 2.9754        | 0.0219 | 10   | 1.9544          |
| 0.765         | 0.0329 | 15   | 0.5049          |
| 0.9201        | 0.0439 | 20   | 0.7542          |
| 0.5405        | 0.0549 | 25   | 0.5149          |
| 0.5533        | 0.0658 | 30   | 0.5817          |
| 0.8103        | 0.0768 | 35   | 0.4703          |
| 0.4951        | 0.0878 | 40   | 0.4914          |
| 0.4763        | 0.0987 | 45   | 0.4491          |
| 0.3867        | 0.1097 | 50   | 0.4170          |


### Framework versions

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