typhoon-thai-food-lora

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<details><summary>See axolotl config</summary>

axolotl version: 0.4.0

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

Framework versions

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