121 lines
2.8 KiB
Markdown
121 lines
2.8 KiB
Markdown
---
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library_name: transformers
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license: llama3.2
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base_model: meta-llama/Llama-3.2-1B-Instruct
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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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- syvai/emotion-reasoning
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model-index:
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- name: emotion-reasoning-1b
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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: `0.10.0.dev0`
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```yaml
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base_model: meta-llama/Llama-3.2-1B-Instruct
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# Automatically upload checkpoint and final model to HF
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hub_model_id: syvai/emotion-reasoning-1b
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datasets:
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- path: syvai/emotion-reasoning
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type: chat_template
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.02
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output_dir: ./outputs/out
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sequence_len: 8192
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sample_packing: true
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pad_to_sequence_len: true
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wandb_project: reasoning-emotions
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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: 8
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micro_batch_size: 2
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num_epochs: 1
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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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bf16: auto
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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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resume_from_checkpoint:
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logging_steps: 1
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flash_attention: true
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warmup_steps: 10
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evals_per_epoch: 2
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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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```
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</details><br>
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# emotion-reasoning-1b
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This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the syvai/emotion-reasoning dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.4510
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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: 2
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- eval_batch_size: 2
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 16
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- optimizer: Use OptimizerNames.PAGED_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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- lr_scheduler_warmup_steps: 10
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- num_epochs: 1.0
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 2.4357 | 0.0047 | 1 | 2.4860 |
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| 1.4295 | 0.5009 | 106 | 1.4510 |
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### Framework versions
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- Transformers 4.51.3
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- Pytorch 2.6.0+cu124
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- Datasets 3.5.1
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- Tokenizers 0.21.1
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