---
library_name: peft
tags:
- generated_from_trainer
base_model: scb10x/typhoon-7b
model-index:
- name: work/out
  results: []
datasets:
- pythainlp/thai_food_v1.0
- ping98k/thai_food_v1.0
language:
- th
---

<!-- 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: ./models/scb10x_typhoon-7b
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

load_in_8bit: false
load_in_4bit: true
strict: false


datasets:
  - path: ./work/thai_food.json
    type: completion

dataset_prepared_path: ./work/last_run_prepared
val_set_size: 0.1
output_dir: ./work/out


adapter: qlora
lora_model_dir:

sequence_len: 4096
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: true

gpu_memory_limit: 20

lora_r: 64
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:


wandb_project: typhoon-7b
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0004

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

gradient_checkpointing: true
early_stopping_patience: 3
resume_from_checkpoint: false
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

# loss_watchdog_threshold: 5.0
# loss_watchdog_patience: 3

warmup_ratio: 0.01
# evals_per_epoch: 10
eval_steps: 2
eval_table_size:
eval_table_max_new_tokens: 128
# saves_per_epoch: 10
save_steps: 2
save_total_limit: 20
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
```

</details><br>

# ping98k/typhoon-thai-food-lora

This model was trained from thai_food dataset but re-order header to เครื่องปรุง -> วิธีทำ -> ชื่ออาหาร.
It achieves the following results on the evaluation set:
- Loss: 1.9505

## Model description

fill ingredients then model will create new menu.


### prompt
you can let model fill more ingredients by remove `## วิธีทำ` from prompt

input

```
## เครื่องปรุง
- ไข่เป็ด
- ใบเตย
```
or
```
## เครื่องปรุง
- ไข่เป็ด
- ใบเตย

## วิธีทำ
```
output
```
ปอกไข่ แช่น้ำใบเตยให้ทั่ว แล้วใส่ชามแช่ไว้ประมาณ 15 นาที ยกขึ้นล้างน้ำเย็นจัด (อย่าใช้น้ำแข็ง) จึงแกะสลัก

## ชื่ออาหาร
ไข่เป็ดตุ๋นใบเตย
```

## 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: 0.0004
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.8268        | 0.13  | 2    | 2.4822          |
| 2.4085        | 0.25  | 4    | 2.2715          |
| 2.2752        | 0.38  | 6    | 2.1985          |
| 2.4104        | 0.51  | 8    | 2.1000          |
| 2.0149        | 0.63  | 10   | 2.0255          |
| 2.1234        | 0.76  | 12   | 1.9926          |
| 2.2013        | 0.89  | 14   | 1.9894          |
| 1.8355        | 1.02  | 16   | 1.9684          |
| 1.4604        | 1.14  | 18   | 1.9610          |
| 1.6539        | 1.27  | 20   | 1.9517          |
| 1.5531        | 1.4   | 22   | 1.9414          |
| 1.4649        | 1.52  | 24   | 1.9230          |
| 1.464         | 1.65  | 26   | 1.9214          |
| 1.3731        | 1.78  | 28   | 1.9116          |
| 1.4451        | 1.9   | 30   | 1.8922          |
| 1.3635        | 2.03  | 32   | 1.8885          |
| 1.1453        | 2.16  | 34   | 1.9034          |
| 1.0397        | 2.29  | 36   | 1.9281          |
| 0.9735        | 2.41  | 38   | 1.9505          |


### Framework versions

- PEFT 0.7.1
- Transformers 4.37.0
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0