yujia23/axolotl-qwen-cold-cn-3e-4-lora

See axolotl config

axolotl version: 0.4.0

# base_model: Qwen/Qwen-7B
base_model: Qwen/Qwen1.5-7B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

trust_remote_code: true

load_in_8bit: true
load_in_4bit: false
strict: false

datasets:
  # - path: mhenrichsen/alpaca_2k_test
  # - path: /home/yujia/home/CN_Hateful/train_toxiCN_cn.json
  # - path: /home/yujia/home/CN_Hateful/train_toxiCN.json
  # - path: /home/yujia/home/CN_Hateful/train.json
  - path: /home/yujia/home/CN_Hateful/train_cn.json
    ds_type: json
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
# output_dir: /home/yujia/home/CN_Hateful/trained_models/qwen/CN/toxi/3e-5/
# output_dir: /home/yujia/home/CN_Hateful/trained_models/qwen/toxi/1e-5/
# output_dir: /home/yujia/home/CN_Hateful/trained_models/qwen/cold/3e-4/
output_dir: /home/yujia/home/CN_Hateful/trained_models/qwen/CN/cold/3e-4/

sequence_len: 256  # supports up to 8192
sample_packing: false
pad_to_sequence_len:

adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0003

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

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

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 20
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

home/yujia/home/CN_Hateful/trained_models/qwen/CN/cold/3e-4/

This model is a fine-tuned version of Qwen/Qwen1.5-7B on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0615

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: 0.0003
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• total_eval_batch_size: 4
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 10
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
3.3447	0.0	1	3.3427
0.0469	0.25	382	0.0515
0.0265	0.5	764	0.0471
0.0712	0.75	1146	0.0430
0.0272	1.0	1528	0.0422
0.0108	1.25	1910	0.0518
0.0237	1.5	2292	0.0426
0.0282	1.75	2674	0.0463
0.0022	2.0	3056	0.0455
0.0009	2.25	3438	0.0576
0.0001	2.5	3820	0.0648
0.0003	2.75	4202	0.0615

Framework versions

• PEFT 0.10.0
• Transformers 4.40.0.dev0
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.0