adapter: lora
base_model: Qwen/Qwen2.5-0.5B-Instruct
bf16: auto
dataset_prepared_path: null
datasets:
- data_files:
  - c9ce17c4a51e22d6_train_data.json
  ds_type: json
  format: custom
  path: c9ce17c4a51e22d6_train_data.json
  type:
    field: null
    field_input: null
    field_instruction: pt
    field_output: vmw
    field_system: null
    format: null
    no_input_format: null
    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: 2
flash_attention: null
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: taopanda/a913124e-186b-450e-bb1c-95cae36d4049
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
micro_batch_size: 2
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: ./outputs/lora-out/taopanda-4_aa49eb56-fb68-48a1-aad1-00c44a9c5af8
pad_to_sequence_len: null
resume_from_checkpoint: null
sample_packing: false
saves_per_epoch: 1
seed: 28141
sequence_len: 2048
special_tokens: null
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: fatcat87-taopanda
wandb_log_model: null
wandb_mode: online
wandb_name: taopanda-4_aa49eb56-fb68-48a1-aad1-00c44a9c5af8
wandb_project: subnet56
wandb_runid: taopanda-4_aa49eb56-fb68-48a1-aad1-00c44a9c5af8
wandb_watch: null
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

Visualize in Weights & Biases

a913124e-186b-450e-bb1c-95cae36d4049

This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 3.8045

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: 28141
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 4
total_train_batch_size: 32
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
6.8927	0.0186	1	6.8756
4.6874	0.5023	27	4.6843
3.9746	1.0047	54	4.0122
3.7845	1.5070	81	3.8045

Framework versions

PEFT 0.11.1
Transformers 4.42.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1