---
library_name: peft
license: llama3
base_model: yentinglin/Llama-3-Taiwan-8B-Instruct
tags:
- axolotl
- generated_from_trainer
datasets:
- jasonhuang3/taitung_gemini_v1v3_messages
model-index:
- name: jason-2k-1
  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: `0.7.0`
```yaml
base_model: yentinglin/Llama-3-Taiwan-8B-Instruct
#trust_remote_code: true
load_in_8bit: false
load_in_4bit: true
strict: false

hub_model_id: jasonhuang3/jason-2k-1
hub_strategy: end
wandb_name: jason 8b 2k

dataset_processes: 16
datasets:
  - path: jasonhuang3/taitung_gemini_v1v3_messages
#  - path: jasonhuang3/instruct_output_v1_messages
    type: chat_template
    field_messages: messages
chat_template: llama3

dataset_prepared_path: last_run_prepared_jason_8b
val_set_size: 0.05 #
output_dir: ./output/8b/jason/2k-1

save_safetensors: true
sequence_len: 2048 #
sample_packing: true
pad_to_sequence_len: true

wandb_project: jasontwllm
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 2 #
num_epochs: 4 #
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-4

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

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 5 #
evals_per_epoch: 4 #
eval_table_size:
saves_per_epoch: 1
save_total_limit: 10
save_steps:
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.0 #
fsdp:
fsdp_config:

#
adapter: lora
lora_r: 64
lora_alpha: 64
lora_dropout: 0.0
lora_target_modules:
  - q_proj
  - v_proj
  - k_proj
  - o_proj
  - gate_proj
  - down_proj
  - up_proj

#lora_mlp_kernel: true
#lora_qkv_kernel: true
#lora_o_kernel: true

eval_sample_packing: False #

```

</details><br>

# jason-2k-1

This model is a fine-tuned version of [yentinglin/Llama-3-Taiwan-8B-Instruct](https://huggingface.co/yentinglin/Llama-3-Taiwan-8B-Instruct) on the jasonhuang3/taitung_gemini_v1v3_messages dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6743

## 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: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- total_eval_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_8BIT 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: 5
- num_epochs: 4.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.5468        | 0.0690 | 1    | 2.7452          |
| 2.2849        | 0.2759 | 4    | 2.2667          |
| 1.8185        | 0.5517 | 8    | 1.9545          |
| 1.705         | 0.8276 | 12   | 1.8034          |
| 1.5654        | 1.0690 | 16   | 1.7497          |
| 1.4897        | 1.3448 | 20   | 1.7219          |
| 1.4709        | 1.6207 | 24   | 1.7026          |
| 1.4316        | 1.8966 | 28   | 1.6768          |
| 1.3117        | 2.1379 | 32   | 1.6765          |
| 1.2819        | 2.4138 | 36   | 1.6759          |
| 1.2825        | 2.6897 | 40   | 1.6815          |
| 1.2934        | 2.9655 | 44   | 1.6787          |
| 1.2153        | 3.2069 | 48   | 1.6747          |
| 1.2287        | 3.4828 | 52   | 1.6745          |
| 1.1894        | 3.7586 | 56   | 1.6743          |


### Framework versions

- PEFT 0.14.0
- Transformers 4.48.3
- Pytorch 2.4.0+cu121
- Datasets 3.2.0
- Tokenizers 0.21.1