---
license: other
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B
model-index:
- name: llama3_8b_odia_v2
  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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: OdiaGenAIdata/culturax-odia
    type: completion
    field: text
dataset_prepared_path:
val_set_size: 0.1
output_dir: ./llama_3_8b_pretrain_v2
hub_model_id: sam2ai/llama3_8b_odia_v2

adapter: qlora
lora_model_dir:

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

lora_r: 64
lora_alpha: 128
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
#lora_modules_to_save:
#  - embed_tokens
#  - lm_head
lora_fan_in_fan_out:

wandb_project: llama-3-8b-pretrain-odia-plain
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 2
num_epochs: 4
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: false

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: "<|end_of_text|>"
save_safetensors: True

```

</details><br>

# llama3_8b_odia_v2

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

## 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
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 13.7841       | 0.0007 | 1    | nan             |
| 0.0           | 0.25   | 384  | nan             |
| 0.0           | 0.5    | 768  | nan             |
| 0.0           | 0.75   | 1152 | nan             |
| 0.0           | 1.0    | 1536 | nan             |
| 0.0           | 1.2362 | 1920 | nan             |
| 0.0           | 1.4862 | 2304 | nan             |
| 0.0           | 1.7362 | 2688 | nan             |
| 0.0           | 1.9862 | 3072 | nan             |
| 0.0           | 2.2220 | 3456 | nan             |
| 0.0           | 2.4720 | 3840 | nan             |
| 0.0           | 2.7220 | 4224 | nan             |
| 0.0           | 2.9720 | 4608 | nan             |
| 0.0           | 3.2078 | 4992 | nan             |
| 0.0           | 3.4578 | 5376 | nan             |
| 0.0           | 3.7078 | 5760 | nan             |
| 0.0           | 3.9578 | 6144 | nan             |


### Framework versions

- PEFT 0.9.0
- Transformers 4.40.0
- Pytorch 2.4.0.dev20240326+rocm6.0
- Datasets 2.15.0
- Tokenizers 0.19.1