Qwen2.5-VL-7B-Instruct-tuned-raw

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<details><summary>See axolotl config</summary>

axolotl version: 0.12.2

base_model: Qwen/Qwen2.5-VL-7B-Instruct
processor_type: AutoProcessor

# these 3 lines are needed for now to handle vision chat templates w images
skip_prepare_dataset: true
remove_unused_columns: false
sample_packing: false

chat_template: qwen2_vl
datasets:
  - path: e-zorzi/reasoning_distractors_choice_chat
    type: chat_template
    split: train

test_datasets:
  - path: e-zorzi/reasoning_distractors_choice_chat
    type: chat_template
    split: val_seen[:20%]
  - path: e-zorzi/reasoning_distractors_choice_chat
    type: chat_template
    split: val_unseen[:20%]


output_dir: ../ctex-persistent/outputs/qwen2_5_VL_7B_lora

load_in_8bit: True
adapter: lora
lora_model_dir:

sequence_len: 2048 #8192
pad_to_sequence_len: false

lora_r: 128
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'

wandb_project: axolotl_finetunes
wandb_entity: edo_vi
wandb_watch:
wandb_name: qwen_7B_2xH100
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 32
num_epochs: 15
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.001

bf16: true
fp16:
tf32: true

gradient_checkpointing: true
logging_steps: 1
flash_attention: true
eager_attention:

warmup_steps: 60
evals_per_epoch: 2
saves_per_epoch: 1
save_strategy: epoch
weight_decay: 0.0

# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

</details><br>

ctex-persistent/outputs/qwen2_5_VL_7B_lora

This model is a fine-tuned version of Qwen/Qwen2.5-VL-7B-Instruct on the e-zorzi/reasoning_distractors_choice_chat dataset. It achieves the following results on the evaluation set:

• Loss: 0.4372
• Memory/max Mem Active(gib): 74.46
• Memory/max Mem Allocated(gib): 74.46
• Memory/device Mem Reserved(gib): 77.0

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: 0.001
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 2
• total_train_batch_size: 128
• total_eval_batch_size: 64
• optimizer: Use OptimizerNames.ADAMW_BNB 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: 60
• training_steps: 1193

Training results

Framework versions

• PEFT 0.17.0
• Transformers 4.55.2
• Pytorch 2.6.0+cu126
• Datasets 4.0.0
• Tokenizers 0.21.4