End of training
Browse files- README.md +164 -0
- adapter_model.bin +3 -0
README.md
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---
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library_name: peft
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license: llama3.1
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base_model: unsloth/Meta-Llama-3.1-8B
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tags:
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- axolotl
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- generated_from_trainer
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model-index:
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- name: f5fd42be-f8cb-4dca-b32f-c0b597a2d94b
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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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.4.1`
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```yaml
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adapter: lora
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base_model: unsloth/Meta-Llama-3.1-8B
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bf16: true
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bnb_config_kwargs:
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bnb_4bit_quant_type: nf4
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bnb_4bit_use_double_quant: true
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chat_template: llama3
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cosine_min_lr_ratio: 0.1
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data_processes: 16
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dataset_prepared_path: null
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datasets:
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- data_files:
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- dialogsum_train_data.json
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ds_type: json
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path: /workspace/input_data/dialogsum_train_data.json
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type:
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field_input: dialogue
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field_instruction: topic
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field_output: summary
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system_format: '{system}'
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system_prompt: ''
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debug: null
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deepspeed: null
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device_map:
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? ''
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: cuda:0
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do_eval: false
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early_stopping_patience: null
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eval_batch_size: 2
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eval_sample_packing: false
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eval_steps: 0
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evaluation_strategy: 'no'
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flash_attention: true
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fp16: null
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fsdp: null
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fsdp_config: null
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gradient_accumulation_steps: 32
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gradient_checkpointing: true
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group_by_length: true
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hub_model_id: cwaud/f5fd42be-f8cb-4dca-b32f-c0b597a2d94b
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hub_repo: cwaud
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hub_strategy: checkpoint
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hub_token: null
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learning_rate: 0.0001
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load_in_4bit: false
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load_in_8bit: false
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local_rank: null
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logging_steps: 1
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lora_alpha: 64
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lora_dropout: 0.05
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lora_fan_in_fan_out: null
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lora_model_dir: null
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lora_r: 32
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lora_target_linear: true
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lora_target_modules:
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- q_proj
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- v_proj
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lr_scheduler: cosine
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max_grad_norm: 1.0
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max_memory:
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0: 76GiB
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max_steps: 360
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micro_batch_size: 2
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mlflow_experiment_name: /tmp/dialogsum_train_data.json
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model_type: UnknownForCausalLM
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num_epochs: 4
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optim_args:
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adam_beta1: 0.9
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adam_beta2: 0.95
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adam_epsilon: 1e-5
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optimizer: adamw_bnb_8bit
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output_dir: miner_id_24
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pad_to_sequence_len: true
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resume_from_checkpoint: null
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s2_attention: null
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sample_packing: false
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save_strategy: epoch
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sequence_len: 2048
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strict: false
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tf32: false
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tokenizer_type: AutoTokenizer
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torch_compile: false
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train_on_inputs: false
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val_set_size: 50
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wandb_entity: rayonlabs-rayon-labs
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wandb_mode: online
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wandb_project: Public_TuningSN
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wandb_run: miner_id_24
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wandb_runid: f5fd42be-f8cb-4dca-b32f-c0b597a2d94b
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warmup_raio: 0.03
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warmup_ratio: 0.05
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weight_decay: 0.01
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xformers_attention: null
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```
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</details><br>
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# f5fd42be-f8cb-4dca-b32f-c0b597a2d94b
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This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B](https://huggingface.co/unsloth/Meta-Llama-3.1-8B) 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: 2
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- eval_batch_size: 2
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 32
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- total_train_batch_size: 256
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- total_eval_batch_size: 8
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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: 11
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- training_steps: 224
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### Training results
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### Framework versions
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- PEFT 0.13.2
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- Transformers 4.45.2
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- Pytorch 2.4.1+cu124
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- Datasets 3.0.1
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- Tokenizers 0.20.1
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adapter_model.bin
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:ec708eeb8891ae1e1b95debfb00fe1a31f9ee43a760847e2a882d4c936128970
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size 335706186
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