metadata
library_name: peft
base_model: Korabbit/llama-2-ko-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 552a5907-3f1e-4ce4-85f7-8c59cdfc4191
results: []
See axolotl config
axolotl version: 0.4.1
adapter: lora
base_model: Korabbit/llama-2-ko-7b
bf16: true
chat_template: llama3
datasets:
- data_files:
- 365ee670b7407e0c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/365ee670b7407e0c_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
hub_model_id: sn56c1/552a5907-3f1e-4ce4-85f7-8c59cdfc4191
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory:
0: 77GiB
max_steps: 100
micro_batch_size: 8
mlflow_experiment_name: /tmp/365ee670b7407e0c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 25
save_strategy: steps
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: sn56-miner
wandb_mode: disabled
wandb_name: 552a5907-3f1e-4ce4-85f7-8c59cdfc4191
wandb_project: god
wandb_run: r6k7
wandb_runid: 552a5907-3f1e-4ce4-85f7-8c59cdfc4191
warmup_steps: 10
weight_decay: 0.01
xformers_attention: false
552a5907-3f1e-4ce4-85f7-8c59cdfc4191
This model is a fine-tuned version of Korabbit/llama-2-ko-7b on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0512
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 100
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
2.6618 | 0.0002 | 1 | 2.6443 |
1.3456 | 0.0015 | 9 | 0.9929 |
0.0874 | 0.0031 | 18 | 0.0900 |
0.0649 | 0.0046 | 27 | 0.0615 |
0.0696 | 0.0062 | 36 | 0.0556 |
0.055 | 0.0077 | 45 | 0.0544 |
0.0583 | 0.0092 | 54 | 0.0543 |
0.0551 | 0.0108 | 63 | 0.0530 |
0.049 | 0.0123 | 72 | 0.0528 |
0.052 | 0.0139 | 81 | 0.0514 |
0.0469 | 0.0154 | 90 | 0.0512 |
0.0508 | 0.0169 | 99 | 0.0512 |
Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1