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.gitattributes CHANGED
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: gemma
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+ base_model: google/gemma-3-270m-it
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - Humenuik/train.jsonl
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+ model-index:
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+ - name: workspace/output/finetune-gemma3-270m-it
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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: `0.13.0.dev0`
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+ ```yaml
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+ base_model: google/gemma-3-270m-it
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+ model_type: GemmaForCausalLM
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+ tokenizer_type: AutoTokenizer
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+ trust_remote_code: true
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+
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+ datasets:
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+ - path: Humenuik/train.jsonl
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+ type: alpaca
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+ conversation: "alpaca"
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+
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+ dataset_prepared_path:
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+ /workspace/output/prepared
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+
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+ output_dir: /workspace/output/finetune-gemma3-270m-it
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+
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+ lora_r: 32 # Set the LoRA rank, influencing the capacity of the adapter layers.
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+ lora_alpha: 16 # Adjust the LoRA alpha, a scaling factor for LoRA updates.
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+ lora_dropout: 0.05 # Apply a dropout rate to LoRA layers for regularization.
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+ lora_target_modules:
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+ - q_proj
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+ - v_proj
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+ - k_proj
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+ - o_proj
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+ - gate_proj
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+ - up_proj
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+ - down_proj # Specify the modules within the model where LoRA adapters will be applied (e.g., attention projections, MLP layers).
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+
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+ gradient_checkpointing: false
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+ micro_batch_size: 8 # Define the size of each micro-batch processed by the GPU.
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+ num_epochs: 2 # Set the number of training epochs.
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+ learning_rate: 0.0002 # Specify the learning rate for the optimizer.
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+ lr_scheduler: cosine # Choose a learning rate scheduler (e.g., cosine).
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+ warmup_steps: 10 # Configure warmup steps for the learning rate scheduler.
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+ batch_size: 24
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+ gradient_checkpointing: true # Enable gradient checkpointing to reduce memory consumption during training.
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+ ```
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+
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+ </details><br>
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+
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+ # workspace/output/finetune-gemma3-270m-it
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+
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+ This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it) on the Humenuik/train.jsonl dataset.
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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: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_steps: 10
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+ - training_steps: 4528
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+
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+ ### Training results
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+
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.55.2
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+ - Pytorch 2.7.1+cu126
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+ - Datasets 4.0.0
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+ - Tokenizers 0.21.4
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