See axolotl config
axolotl version: 0.4.0
base_model: T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0
base_model_config: T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
is_llama_derived_model: true
hub_model_id: T3Q-LLM-sft1.0-dpo1.0_100QA
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: admin_data.csv
type: alpaca
# The below are defaults. only set what's needed if you use a different column name.
# system_prompt: ""
# system_format: "{system}"
# field_system: system
# field_instruction: instruction
# field_input: input
# field_output: output
# format: |-
# Human: {instruction} {input}
# Assistant:
# no_input_format: "{instruction} "
# dataset_prepared_path: yanolja_preprocessed_data
dataset_prepared_path: last_run_prepared
val_set_size: 0.2
output_dir: ./T3Q-LLM-sft1.0-dpo1.0_100QA
adapter: qlora
lora_model_dir:
# device_map: [0,1,3]
sequence_len: 4096
sample_packing: false
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: axolotl_T3Q
wandb_entity:
wandb_watch:
wandb_run_id: T3Q_mod_100
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 10
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
eval_steps: 0.01
save_strategy: epoch
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "<|im_end|>"
unk_token: "<unk>"
pad_token: "</s>" # EOS와 PAD가 동일
T3Q-LLM-sft1.0-dpo1.0_100QA
This model is a fine-tuned version of T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6629
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: 0.0002
- 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: 100
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
0.8867 | 0.2 | 1 | 1.0564 |
0.9385 | 0.4 | 2 | 1.0557 |
0.9454 | 0.6 | 3 | 1.0535 |
0.8469 | 0.8 | 4 | 1.0494 |
0.8583 | 1.0 | 5 | 1.0412 |
0.8691 | 1.2 | 6 | 1.0262 |
0.8306 | 1.4 | 7 | 1.0073 |
0.8302 | 1.6 | 8 | 0.9834 |
0.8028 | 1.8 | 9 | 0.9556 |
0.8987 | 2.0 | 10 | 0.9181 |
0.7826 | 2.2 | 11 | 0.8777 |
0.6936 | 2.4 | 12 | 0.8379 |
0.6453 | 2.6 | 13 | 0.8035 |
0.6613 | 2.8 | 14 | 0.7741 |
0.6548 | 3.0 | 15 | 0.7483 |
0.6078 | 3.2 | 16 | 0.7238 |
0.6185 | 3.4 | 17 | 0.7004 |
0.5293 | 3.6 | 18 | 0.6815 |
0.5617 | 3.8 | 19 | 0.6666 |
0.4845 | 4.0 | 20 | 0.6541 |
0.4904 | 4.2 | 21 | 0.6443 |
0.5375 | 4.4 | 22 | 0.6349 |
0.5099 | 4.6 | 23 | 0.6254 |
0.4286 | 4.8 | 24 | 0.6187 |
0.4952 | 5.0 | 25 | 0.6133 |
0.4394 | 5.2 | 26 | 0.6089 |
0.4974 | 5.4 | 27 | 0.6041 |
0.3877 | 5.6 | 28 | 0.5999 |
0.4992 | 5.8 | 29 | 0.5952 |
0.4187 | 6.0 | 30 | 0.5902 |
0.4302 | 6.2 | 31 | 0.5871 |
0.3861 | 6.4 | 32 | 0.5836 |
0.3966 | 6.6 | 33 | 0.5805 |
0.4399 | 6.8 | 34 | 0.5786 |
0.3732 | 7.0 | 35 | 0.5777 |
0.3727 | 7.2 | 36 | 0.5780 |
0.3442 | 7.4 | 37 | 0.5786 |
0.3477 | 7.6 | 38 | 0.5801 |
0.3763 | 7.8 | 39 | 0.5808 |
0.3498 | 8.0 | 40 | 0.5824 |
0.312 | 8.2 | 41 | 0.5834 |
0.3282 | 8.4 | 42 | 0.5869 |
0.2938 | 8.6 | 43 | 0.5912 |
0.2908 | 8.8 | 44 | 0.5967 |
0.3083 | 9.0 | 45 | 0.6031 |
0.244 | 9.2 | 46 | 0.6111 |
0.2894 | 9.4 | 47 | 0.6228 |
0.2318 | 9.6 | 48 | 0.6353 |
0.2375 | 9.8 | 49 | 0.6474 |
0.1939 | 10.0 | 50 | 0.6629 |
Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.19.1
- Downloads last month
- 2
Inference Providers
NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API:
The model has no pipeline_tag.
Model tree for superiort/T3Q-LLM-sft1.0-dpo1.0_100QA_10epochs
Base model
T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0