End of training
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README.md
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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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