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--- |
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library_name: peft |
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license: llama3.2 |
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base_model: NousResearch/Llama-3.2-1B |
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tags: |
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- generated_from_trainer |
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datasets: |
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- chrisdboyce/qwc |
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model-index: |
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- name: outputs/lora-out |
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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: `0.8.0.dev0` |
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```yaml |
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base_model: NousResearch/Llama-3.2-1B |
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# Automatically upload checkpoint and final model to HF |
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# hub_model_id: username/custom_model_name |
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datasets: |
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- path: chrisdboyce/qwc |
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type: alpaca |
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val_set_size: 0.1 |
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output_dir: ./outputs/lora-out |
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adapter: lora |
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lora_model_dir: |
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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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lora_r: 16 |
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lora_alpha: 32 |
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lora_dropout: 0.05 |
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lora_target_modules: |
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- gate_proj |
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- down_proj |
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- up_proj |
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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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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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gradient_accumulation_steps: 2 |
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micro_batch_size: 2 |
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num_epochs: 1 |
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optimizer: adamw_8bit |
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lr_scheduler: cosine |
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learning_rate: 0.0002 |
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bf16: auto |
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label_names: |
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- labels |
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tf32: false |
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evaluation_strategy: "no" |
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gradient_checkpointing: true |
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resume_from_checkpoint: |
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logging_steps: 1 |
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flash_attention: true |
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loss_watchdog_threshold: 5.0 |
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loss_watchdog_patience: 3 |
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warmup_ratio: 0.1 |
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evals_per_epoch: 0 |
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# evals_per_epoch: 4 |
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saves_per_epoch: 1 |
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weight_decay: 0.0 |
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special_tokens: |
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pad_token: "<|end_of_text|>" |
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# save_first_step: true # uncomment this to validate checkpoint saving works with your config |
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``` |
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</details><br> |
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# outputs/lora-out |
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This model is a fine-tuned version of [NousResearch/Llama-3.2-1B](https://huggingface.co/NousResearch/Llama-3.2-1B) on the chrisdboyce/qwc 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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 4 |
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- optimizer: Use OptimizerNames.ADAMW_8BIT 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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- num_epochs: 1.0 |
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### Training results |
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### Framework versions |
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- PEFT 0.14.0 |
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- Transformers 4.49.0 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
