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README.md CHANGED
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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: Aculi/Tinyllama-2B
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+ library_name: peft
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: outputs/thinking-tiny-llama
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+ results: []
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+ ---
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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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+
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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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+
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+ axolotl version: `0.4.1`
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+ ```yaml
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+ base_model: Aculi/Tinyllama-2B
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+ model_type: LlamaForCausalLM
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+ tokenizer_type: LlamaTokenizer
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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: ./datas/1.json
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+ type: alpaca
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+ - path: ./datas/2.json
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+ type: alpaca
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+
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+ dataset_prepared_path:
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+ val_set_size: 0.05
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+ output_dir: ./outputs/thinking-tiny-llama
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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: 4096
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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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+
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+ lora_r: 32
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+ lora_alpha: 16
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+ lora_dropout: 0.05
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+ lora_target_modules:
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+ lora_target_linear: true
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+ lora_fan_in_fan_out:
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+
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+ wandb_project:
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+ wandb_entity:
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+ wandb_watch:
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+ wandb_name:
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+ wandb_log_model:
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+
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+ gradient_accumulation_steps: 4
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+ micro_batch_size: 2
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+ num_epochs: 4
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+ optimizer: paged_adamw_32bit
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+ lr_scheduler: cosine
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+ learning_rate: 0.0002
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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: auto
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+ fp16:
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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_checkpoint:
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+ local_rank:
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+ logging_steps: 1
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+ xformers_attention: false
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+ flash_attention: true
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+
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+ warmup_steps: 10
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+ evals_per_epoch: 2
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+ saves_per_epoch: 2
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+ debug:
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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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+ ```
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+
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+ </details><br>
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+
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+ # outputs/thinking-tiny-llama
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+
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+ This model is a fine-tuned version of [Aculi/Tinyllama-2B](https://huggingface.co/Aculi/Tinyllama-2B) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.0222
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0002
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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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+ - gradient_accumulation_steps: 4
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+ - total_train_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: 10
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+ - num_epochs: 4
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:------:|:----:|:---------------:|
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+ | 1.5625 | 0.0013 | 1 | 1.5692 |
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+ | 1.1161 | 0.5002 | 400 | 1.0995 |
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+ | 1.0509 | 1.0003 | 800 | 1.0633 |
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+ | 1.0665 | 1.4867 | 1200 | 1.0422 |
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+ | 1.012 | 1.9869 | 1600 | 1.0287 |
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+ | 1.0124 | 2.4733 | 2000 | 1.0250 |
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+ | 0.8544 | 2.9734 | 2400 | 1.0212 |
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+ | 0.9435 | 3.4605 | 2800 | 1.0222 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.11.1
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+ - Transformers 4.43.1
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+ - Pytorch 2.3.1+cu121
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+ - Datasets 2.19.1
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+ - Tokenizers 0.19.1
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