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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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<!-- 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: `0.13.0.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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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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dataset_prepared_path: |
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/workspace/output/prepared |
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output_dir: /workspace/output/finetune-gemma3-270m-it |
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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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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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</details><br> |
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# workspace/output/finetune-gemma3-270m-it |
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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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## 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: 0.0002 |
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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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### Training results |
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### Framework versions |
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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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