---
library_name: peft
license: other
base_model: NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3b71b44e-7e35-4d0c-add1-9c6d1e59e39a
  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: NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer
bf16: true
chat_template: llama3
datasets:
- data_files:
  - c9a035a85fed4940_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/c9a035a85fed4940_train_data.json
  type:
    field_input: A
    field_instruction: question
    field_output: correct_answer
    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: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
hub_model_id: sn56m1/3b71b44e-7e35-4d0c-add1-9c6d1e59e39a
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: 77GiB
max_steps: 100
micro_batch_size: 8
mlflow_experiment_name: /tmp/c9a035a85fed4940_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: sn56-miner
wandb_mode: disabled
wandb_name: 3b71b44e-7e35-4d0c-add1-9c6d1e59e39a
wandb_project: god
wandb_run: i9er
wandb_runid: 3b71b44e-7e35-4d0c-add1-9c6d1e59e39a
warmup_steps: 10
weight_decay: 0.01
xformers_attention: false

```

</details><br>

# 3b71b44e-7e35-4d0c-add1-9c6d1e59e39a

This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8053

## 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: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- 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: 64

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 14.9551       | 0.0465 | 1    | 15.1260         |
| 14.1719       | 0.2791 | 6    | 13.0561         |
| 6.1172        | 0.5581 | 12   | 5.6846          |
| 4.3784        | 0.8372 | 18   | 4.2774          |
| 4.0412        | 1.1163 | 24   | 3.9769          |
| 3.9404        | 1.3953 | 30   | 3.9006          |
| 3.8809        | 1.6744 | 36   | 3.8561          |
| 3.8623        | 1.9535 | 42   | 3.8388          |
| 3.8276        | 2.2326 | 48   | 3.8372          |
| 3.8165        | 2.5116 | 54   | 3.8157          |
| 3.8162        | 2.7907 | 60   | 3.8053          |


### Framework versions

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