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--- |
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library_name: transformers |
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license: llama3.2 |
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base_model: meta-llama/Llama-3.2-3B |
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tags: |
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- axolotl |
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- generated_from_trainer |
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datasets: |
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- yahma/alpaca-cleaned |
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model-index: |
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- name: qat-nvfp4-llama3B |
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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: meta-llama/Llama-3.2-3B |
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hub_model_id: smohammadi/qat-nvfp4-llama3B |
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load_in_8bit: false |
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load_in_4bit: false |
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strict: false |
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#chunked_cross_entropy: true |
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#plugins: |
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# - axolotl.integrations.liger.LigerPlugin |
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liger_rope: true |
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liger_rms_norm: true |
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liger_glu_activation: true |
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liger_layer_norm: true |
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#liger_fused_linear_cross_entropy: true |
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datasets: |
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- path: yahma/alpaca-cleaned |
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type: alpaca |
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split: train[:95%] |
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output_dir: ./outputs/qat_out/ |
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dataset_prepared_path: ./outputs/qat_out/dataset_prepared |
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sample_packing: false #true |
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sequence_len: 4096 |
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flash_attention: true |
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#flex_attention: true |
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#flex_attn_compile_kwargs: |
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# dynamic: false |
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#mode: max-autotune-no-cudagraphs |
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qat: |
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activation_dtype: nvfp4 |
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weight_dtype: nvfp4 |
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group_size: 16 # only group_size of 16 is supported with nvfp4 |
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wandb_project: qat_v2 |
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wandb_entity: |
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wandb_watch: |
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wandb_name: nvfp4 |
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wandb_log_model: |
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gradient_accumulation_steps: 1 |
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micro_batch_size: 64 |
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num_epochs: 1 |
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optimizer: adamw_torch_fused |
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gradient_checkpointing: true |
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cosine_constant_lr_ratio: 0 |
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cosine_min_lr_ratio: 1.0 |
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learning_rate: 2e-5 |
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save_only_model: true |
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bf16: true |
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resume_from_checkpoint: |
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logging_steps: 1 |
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evals_per_epoch: 1 |
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saves_per_epoch: 1 |
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warmup_ratio: 0.1 |
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weight_decay: 0.0 |
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special_tokens: |
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pad_token: <|finetune_right_pad_id|> |
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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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# qat-nvfp4-llama3B |
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This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) on the yahma/alpaca-cleaned 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: 2e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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: 76 |
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- training_steps: 769 |
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### Training results |
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### Framework versions |
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- Transformers 4.55.4 |
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- Pytorch 2.8.0+cu128 |
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- Datasets 4.0.0 |
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- Tokenizers 0.21.4 |
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