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typhoon-thai-food-lora

PEFT
Thai
mistral
Generated from Trainer
4-bit precision
bitsandbytes
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metadata
library_name: peft
tags:
  - generated_from_trainer
base_model: scb10x/typhoon-7b
model-index:
  - name: work/out
    results: []

Built with Axolotl

See axolotl config

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

# lora_modules_to_save:
#   - embed_tokens
#   - lm_head


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:

work/out

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.9505

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: 0.0004
  • 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