145 lines
4.0 KiB
Markdown
145 lines
4.0 KiB
Markdown
---
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library_name: transformers
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base_model: nvidia/Llama-3.1-Minitron-4B-Width-Base
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tags:
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- axolotl
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- generated_from_trainer
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model-index:
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- name: MagpieLM-4B-SFT-v0.1
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results: []
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datasets:
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- Magpie-Align/MagpieLM-SFT-Data-v0.1
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language:
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- en
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---
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://api.wandb.ai/links/uw-nsl/7grozq8s)
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# 🐦 MagpieLM-4B-SFT-v0.1
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Project Web: [https://magpie-align.github.io/](https://magpie-align.github.io/)
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Arxiv Technical Report: [https://arxiv.org/abs/2406.08464](https://arxiv.org/abs/2406.08464)
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Codes: [https://github.com/magpie-align/magpie](https://github.com/magpie-align/magpie)
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## About This Model
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*Model full name: Llama3.1-MagpieLM-4B-SFT-v0.1*
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This model is a fine-tuned version of [nvidia/Llama-3.1-Minitron-4B-Width-Base](https://huggingface.co/nvidia/Llama-3.1-Minitron-4B-Width-Base) on [Magpie-Align/MagpieLM-SFT-Data-v0.1](https://huggingface.co/datasets/Magpie-Align/MagpieLM-SFT-Data-v0.1) dataset.
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This is the intermediate checkpoint for fine-tuning [Magpie-Align/MagpieLM-4B-Chat-v0.1](https://huggingface.co/Magpie-Align/MagpieLM-4B-Chat-v0.1).
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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: 1
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- eval_batch_size: 1
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 32
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- total_train_batch_size: 128
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- total_eval_batch_size: 4
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 51
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- num_epochs: 2
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 1.1026 | 0.0038 | 1 | 1.1547 |
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| 0.6994 | 0.2015 | 53 | 0.7142 |
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| 0.6181 | 0.4030 | 106 | 0.6375 |
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| 0.5967 | 0.6045 | 159 | 0.6134 |
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| 0.5793 | 0.8060 | 212 | 0.6004 |
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| 0.5736 | 1.0075 | 265 | 0.5914 |
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| 0.5411 | 1.1938 | 318 | 0.5883 |
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| 0.5402 | 1.3953 | 371 | 0.5864 |
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| 0.5423 | 1.5968 | 424 | 0.5856 |
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| 0.5408 | 1.7983 | 477 | 0.5854 |
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### Framework versions
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- Transformers 4.45.0.dev0
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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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: `0.4.1`
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```yaml
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base_model: nvidia/Llama-3.1-Minitron-4B-Width-Base
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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chat_template: llama3
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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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datasets:
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- path: Magpie-Align/MagpieLM-SFT-Data-v0.1
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type: sharegpt
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conversation: llama3
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.001
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output_dir: axolotl_out/MagpieLM-4B-SFT-v0.1
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sequence_len: 8192
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sample_packing: true
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eval_sample_packing: false
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pad_to_sequence_len: true
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wandb_project: SynDa
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wandb_entity:
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wandb_watch:
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wandb_name: Llama3.1-MagpieLM-4B-SFT-v0.1
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wandb_log_model:
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hub_model_id: Magpie-Align/MagpieLM-4B-SFT-v0.1
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gradient_accumulation_steps: 32
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micro_batch_size: 1
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num_epochs: 2
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optimizer: paged_adamw_8bit
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lr_scheduler: cosine
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learning_rate: 2e-5
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train_on_inputs: false
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group_by_length: false
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bf16: true
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fp16:
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tf32: false
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: false
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early_stopping_patience:
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resume_from_checkpoint:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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warmup_ratio: 0.1
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evals_per_epoch: 5
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eval_table_size:
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saves_per_epoch: 1
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debug:
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deepspeed:
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weight_decay: 0.0
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fsdp:
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fsdp_config:
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special_tokens:
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pad_token: <|end_of_text|>
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```
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</details><br> |