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---
library_name: peft
tags:
- generated_from_trainer
base_model: scb10x/typhoon-7b
model-index:
- name: work/out
results: []
datasets:
- scb_mt_enth_2020
language:
- th
pipeline_tag: text-generation
---
<!-- 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/scb-mt-en-th-2020/apdf.csv
type:
system_prompt: ""
field_system: system
field_instruction: en_text
field_output: th_text
format: "{instruction}<translate>"
- path: ./work/scb-mt-en-th-2020/assorted_government.csv
type:
system_prompt: ""
field_system: system
field_instruction: en_text
field_output: th_text
format: "{instruction}<translate>"
- path: ./work/scb-mt-en-th-2020/generated_reviews_crowd.csv
type:
system_prompt: ""
field_system: system
field_instruction: en_text
field_output: th_text
format: "{instruction}<translate>"
- path: ./work/scb-mt-en-th-2020/generated_reviews_translator.csv
type:
system_prompt: ""
field_system: system
field_instruction: en_text
field_output: th_text
format: "{instruction}<translate>"
- path: ./work/scb-mt-en-th-2020/generated_reviews_yn.csv
type:
system_prompt: ""
field_system: system
field_instruction: en_text
field_output: th_text
format: "{instruction}<translate>"
- path: ./work/scb-mt-en-th-2020/mozilla_common_voice.csv
type:
system_prompt: ""
field_system: system
field_instruction: en_text
field_output: th_text
format: "{instruction}<translate>"
- path: ./work/scb-mt-en-th-2020/msr_paraphrase.csv
type:
system_prompt: ""
field_system: system
field_instruction: en_text
field_output: th_text
format: "{instruction}<translate>"
- path: ./work/scb-mt-en-th-2020/nus_sms.csv
type:
system_prompt: ""
field_system: system
field_instruction: en_text
field_output: th_text
format: "{instruction}<translate>"
- path: ./work/scb-mt-en-th-2020/paracrawl.csv
type:
system_prompt: ""
field_system: system
field_instruction: en_text
field_output: th_text
format: "{instruction}<translate>"
- path: ./work/scb-mt-en-th-2020/task_master_1.csv
type:
system_prompt: ""
field_system: system
field_instruction: en_text
field_output: th_text
format: "{instruction}<translate>"
- path: ./work/scb-mt-en-th-2020/thai_websites.csv
type:
system_prompt: ""
field_system: system
field_instruction: en_text
field_output: th_text
format: "{instruction}<translate>"
- path: ./work/scb-mt-en-th-2020/wikipedia.csv
type:
system_prompt: ""
field_system: system
field_instruction: en_text
field_output: th_text
format: "{instruction}<translate>"
dataset_prepared_path: ./work/last_run_prepared
val_set_size: 0.02
output_dir: ./work/out
adapter: qlora
lora_model_dir:
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
gpu_memory_limit: 20
lora_r: 32
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: 8
micro_batch_size: 2
num_epochs: 1
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
resume_from_checkpoint: true
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_ratio: 0.01
eval_steps: 10
eval_table_size:
eval_table_max_new_tokens: 128
save_steps: 10
save_total_limit: 10
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
```
</details><br>
# ping98k/typhoon-7b-en-to-th-lora
This model was qlora finetuned on the scb_mt_enth_2020 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8657
## Model description
### prompt
```
Why can camels survive for long without water?<translate>
```
### output
```
ทำไมอูฐสามารถอยู่รอดได้นานโดยไม่มีน้ำ
```
### known issue
model not train with end translate token correctly. some time model will output `<translate>` or `</translate>`
```
Why can camels survive for long without water?<translate>ทำไมอูฐสามารถอยู่รอดได้นานโดยไม่มีน้ำ<translate>
Why can camels survive for long without water?<translate>ทำไมอูฐสามารถอยู่รอดได้นานโดยไม่มีน้ำ</translate>
```
## 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: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 90
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.8002 | 0.01 | 10 | 2.7164 |
| 2.1186 | 0.02 | 20 | 2.0709 |
| 1.717 | 0.03 | 30 | 1.6999 |
| 1.5327 | 0.04 | 40 | 1.5332 |
| 1.3684 | 0.04 | 50 | 1.4293 |
| 1.3992 | 0.05 | 60 | 1.3651 |
| 1.3031 | 0.06 | 70 | 1.3198 |
| 1.3067 | 0.07 | 80 | 1.2831 |
| 1.2685 | 0.08 | 90 | 1.2542 |
| 1.2469 | 0.09 | 100 | 1.2293 |
| 1.2067 | 0.1 | 110 | 1.2096 |
| 1.1458 | 0.11 | 120 | 1.1942 |
| 1.1679 | 0.11 | 130 | 1.1732 |
| 1.1914 | 0.12 | 140 | 1.1609 |
| 1.2329 | 0.13 | 150 | 1.1491 |
| 1.1151 | 0.14 | 160 | 1.1365 |
| 1.1138 | 0.15 | 170 | 1.1252 |
| 1.1607 | 0.16 | 180 | 1.1188 |
| 1.083 | 0.17 | 190 | 1.1095 |
| 1.1068 | 0.18 | 200 | 1.1016 |
| 1.1214 | 0.18 | 210 | 1.0921 |
| 1.061 | 0.19 | 220 | 1.0862 |
| 1.1072 | 0.2 | 230 | 1.0792 |
| 1.0275 | 0.21 | 240 | 1.0739 |
| 1.0735 | 0.22 | 250 | 1.0666 |
| 1.0549 | 0.23 | 260 | 1.0634 |
| 1.0336 | 0.24 | 270 | 1.0561 |
| 1.0784 | 0.25 | 280 | 1.0519 |
| 1.0313 | 0.26 | 290 | 1.0459 |
| 1.0459 | 0.26 | 300 | 1.0415 |
| 1.0824 | 0.27 | 310 | 1.0390 |
| 1.0543 | 0.28 | 320 | 1.0327 |
| 1.0732 | 0.29 | 330 | 1.0287 |
| 1.0071 | 0.3 | 340 | 1.0237 |
| 1.0336 | 0.31 | 350 | 1.0200 |
| 1.0694 | 0.32 | 360 | 1.0155 |
| 0.9799 | 0.33 | 370 | 1.0111 |
| 1.0025 | 0.33 | 380 | 1.0073 |
| 0.9805 | 0.34 | 390 | 1.0044 |
| 0.9398 | 0.35 | 400 | 1.0011 |
| 1.0133 | 0.36 | 410 | 0.9957 |
| 1.0465 | 0.37 | 420 | 0.9916 |
| 0.9711 | 0.38 | 430 | 0.9887 |
| 0.9786 | 0.39 | 440 | 0.9858 |
| 0.9687 | 0.4 | 450 | 0.9835 |
| 0.988 | 0.4 | 460 | 0.9810 |
| 1.021 | 0.41 | 470 | 0.9770 |
| 0.9754 | 0.42 | 480 | 0.9734 |
| 0.9677 | 0.43 | 490 | 0.9705 |
| 1.0114 | 0.44 | 500 | 0.9667 |
| 0.978 | 0.45 | 510 | 0.9643 |
| 0.9762 | 0.46 | 520 | 0.9611 |
| 0.9795 | 0.47 | 530 | 0.9597 |
| 0.9419 | 0.48 | 540 | 0.9558 |
| 0.9403 | 0.48 | 550 | 0.9519 |
| 0.9408 | 0.49 | 560 | 0.9495 |
| 0.9704 | 0.5 | 570 | 0.9460 |
| 0.9426 | 0.51 | 580 | 0.9447 |
| 0.9288 | 0.52 | 590 | 0.9406 |
| 0.9986 | 0.53 | 600 | 0.9394 |
| 0.9129 | 0.54 | 610 | 0.9374 |
| 0.9797 | 0.55 | 620 | 0.9349 |
| 0.9269 | 0.55 | 630 | 0.9317 |
| 0.9258 | 0.56 | 640 | 0.9296 |
| 0.9041 | 0.57 | 650 | 0.9268 |
| 0.9383 | 0.58 | 660 | 0.9240 |
| 0.9289 | 0.59 | 670 | 0.9220 |
| 0.8906 | 0.6 | 680 | 0.9201 |
| 0.9275 | 0.61 | 690 | 0.9171 |
| 0.99 | 0.62 | 700 | 0.9150 |
| 0.9063 | 0.62 | 710 | 0.9124 |
| 0.8757 | 0.63 | 720 | 0.9107 |
| 0.9276 | 0.64 | 730 | 0.9087 |
| 0.9315 | 0.65 | 740 | 0.9064 |
| 0.9442 | 0.66 | 750 | 0.9037 |
| 0.8848 | 0.67 | 760 | 0.9015 |
| 0.8901 | 0.68 | 770 | 0.8993 |
| 0.8714 | 0.69 | 780 | 0.8973 |
| 0.8641 | 0.7 | 790 | 0.8956 |
| 0.8915 | 0.7 | 800 | 0.8938 |
| 0.9069 | 0.71 | 810 | 0.8921 |
| 0.8798 | 0.72 | 820 | 0.8901 |
| 0.9195 | 0.73 | 830 | 0.8884 |
| 0.8936 | 0.74 | 840 | 0.8868 |
| 0.8284 | 0.75 | 850 | 0.8851 |
| 0.9469 | 0.76 | 860 | 0.8833 |
| 0.8854 | 0.77 | 870 | 0.8820 |
| 0.8865 | 0.77 | 880 | 0.8809 |
| 0.8982 | 0.78 | 890 | 0.8799 |
| 0.8683 | 0.79 | 900 | 0.8786 |
| 0.9326 | 0.8 | 910 | 0.8773 |
| 0.8937 | 0.81 | 920 | 0.8758 |
| 0.8995 | 0.82 | 930 | 0.8746 |
| 0.9263 | 0.83 | 940 | 0.8735 |
| 0.907 | 0.84 | 950 | 0.8725 |
| 0.8467 | 0.84 | 960 | 0.8715 |
| 0.9037 | 0.85 | 970 | 0.8708 |
| 0.833 | 0.86 | 980 | 0.8699 |
| 0.878 | 0.87 | 990 | 0.8693 |
| 0.8897 | 0.88 | 1000 | 0.8686 |
| 0.8931 | 0.89 | 1010 | 0.8681 |
| 0.8766 | 0.9 | 1020 | 0.8676 |
| 0.839 | 0.91 | 1030 | 0.8672 |
| 0.8973 | 0.92 | 1040 | 0.8669 |
| 0.8806 | 0.92 | 1050 | 0.8666 |
| 0.8683 | 0.93 | 1060 | 0.8664 |
| 0.8736 | 0.94 | 1070 | 0.8662 |
| 0.8495 | 0.95 | 1080 | 0.8660 |
| 0.8364 | 0.96 | 1090 | 0.8659 |
| 0.8934 | 0.97 | 1100 | 0.8658 |
| 0.8954 | 0.98 | 1110 | 0.8658 |
| 0.8783 | 0.99 | 1120 | 0.8657 |
| 0.8678 | 0.99 | 1130 | 0.8657 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.0
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0 |