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--- |
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license: apache-2.0 |
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library_name: peft |
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tags: |
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- axolotl |
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- generated_from_trainer |
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base_model: byroneverson/LLaVA-v1.5-7B-rehome |
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model-index: |
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- name: LLaVA-v1.5-7B-rehome-qlora |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.3.0` |
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```yaml |
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base_model: byroneverson/LLaVA-v1.5-7B-rehome |
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model_type: AutoModelForCausalLM #MistralForCausalLM |
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tokenizer_type: AutoTokenizer #LlamaTokenizer |
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#is_mistral_derived_model: true |
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# |
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load_in_8bit: false |
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load_in_4bit: true |
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strict: false |
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# |
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datasets: |
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- path: yahma/alpaca-cleaned #byroneverson/shell-cmd-instruct |
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type: completion #solar_shell_instruct #alpaca |
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# For `completion` datsets only, uses the provided field instead of `text` column |
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field: output |
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dataset_prepared_path: last_run_prepared |
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val_set_size: 0.02 |
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output_dir: /kaggle/working/qlora-out |
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# |
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# Push checkpoints to hub |
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hub_model_id: byroneverson/LLaVA-v1.5-7B-rehome-shell-qlora |
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# How to push checkpoints to hub |
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# https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy |
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hub_strategy: checkpoint |
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# Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets |
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# Required to be true when used in combination with `push_dataset_to_hub` |
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hf_use_auth_token: true |
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# |
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adapter: qlora |
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lora_model_dir: |
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# |
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sequence_len: 2048 |
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sample_packing: true |
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eval_sample_packing: false |
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pad_to_sequence_len: true |
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# Train only these params when performing full train |
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#unfrozen_parameters: [ |
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# "\\bmlp\\.up_proj\\b", |
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# "\\bmlp\\.down_proj\\b", |
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# "\\bmlp\\.gate_proj\\b", |
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# ] |
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# LoRA/QLoRA |
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lora_r: 160 #128 |
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lora_alpha: 80 #384 # A good ballpark is double lora_r for 2x or half for 0.5x |
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lora_dropout: 0.025 |
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lora_target_linear: false # Only target specified modules |
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lora_fan_in_fan_out: |
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lora_target_modules: [ |
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"up_proj", |
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"down_proj", |
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"gate_proj", |
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] |
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# |
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wandb_project: "LLaVA-v1.5-7B-rehome-qlora" |
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wandb_log_model: "checkpoint" |
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wandb_entity: |
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wandb_watch: |
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wandb_run_id: |
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# |
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gradient_accumulation_steps: 16 # 1 |
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micro_batch_size: 1 |
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num_epochs: 0.2 |
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optimizer: paged_lion_8bit #paged_adamw_8bit |
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lr_scheduler: cosine |
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cosine_min_lr_ratio: 1.0 # Constant lr if 1.0 |
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learning_rate: 0.0001 |
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# |
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train_on_inputs: false |
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group_by_length: false |
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bf16: false #true |
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fp16: true |
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tf32: false |
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# |
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gradient_checkpointing: true |
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early_stopping_patience: |
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# Resume from a specific checkpoint dir |
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resume_from_checkpoint: #last-checkpoint |
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# If resume_from_checkpoint isn't set and you simply want it to start where it left off. |
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# Be careful with this being turned on between different models. |
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auto_resume_from_checkpoints: true #false |
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local_rank: |
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logging_steps: 1 |
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xformers_attention: |
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# Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention: |
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flash_attention: false #true |
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flash_attn_cross_entropy: # Whether to use flash-attention cross entropy implementation - advanced use only |
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flash_attn_rms_norm: false # Whether to use flash-attention rms norm implementation - advanced use only |
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flash_attn_fuse_qkv: # Whether to fuse QKV into a single operation |
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flash_attn_fuse_mlp: # Whether to fuse part of the MLP into a single operation |
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# |
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warmup_steps: 4 |
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eval_steps: 50 |
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eval_table_size: |
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eval_table_max_new_tokens: 128 |
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save_steps: |
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debug: true # |
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deepspeed: |
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weight_decay: 0.0 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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bos_token: "<s>" |
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eos_token: "</s>" |
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unk_token: "<unk>" |
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``` |
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</details><br> |
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# LLaVA-v1.5-7B-rehome-shell-qlora |
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This model is a fine-tuned version of [byroneverson/LLaVA-v1.5-7B-rehome](https://huggingface.co/byroneverson/LLaVA-v1.5-7B-rehome) on the None dataset. |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 2 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 4 |
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- num_epochs: 0.2 |
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- mixed_precision_training: Native AMP |
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### Training results |
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### Framework versions |
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- PEFT 0.7.2.dev0 |
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- Transformers 4.37.0.dev0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |