Instructions to use tuanna08go/3f8548bb-6b5c-489e-b968-307e0fa5f554 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tuanna08go/3f8548bb-6b5c-489e-b968-307e0fa5f554 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B-Instruct") model = PeftModel.from_pretrained(base_model, "tuanna08go/3f8548bb-6b5c-489e-b968-307e0fa5f554") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +10 -3
- adapter_model.bin +1 -1
README.md
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lora_r: 8
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lora_target_linear: true
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lr_scheduler: cosine
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max_steps:
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micro_batch_size: 8
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mlflow_experiment_name: /tmp/d36891e61cdb4491_train_data.json
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model_type: AutoModelForCausalLM
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wandb_project: Gradients-On-Demand
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wandb_run: your_name
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wandb_runid: 3f8548bb-6b5c-489e-b968-307e0fa5f554
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warmup_steps:
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weight_decay: 0.0
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xformers_attention: null
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# 3f8548bb-6b5c-489e-b968-307e0fa5f554
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This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the None dataset.
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## Model description
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- optimizer: Use OptimizerNames.ADAMW_BNB 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: 2
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- training_steps:
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| No log | 0.0013 | 1 | 4.1801 |
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### Framework versions
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lora_r: 8
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lora_target_linear: true
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lr_scheduler: cosine
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max_steps: 50
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micro_batch_size: 8
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mlflow_experiment_name: /tmp/d36891e61cdb4491_train_data.json
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model_type: AutoModelForCausalLM
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wandb_project: Gradients-On-Demand
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wandb_run: your_name
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wandb_runid: 3f8548bb-6b5c-489e-b968-307e0fa5f554
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warmup_steps: 2
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weight_decay: 0.0
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xformers_attention: null
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# 3f8548bb-6b5c-489e-b968-307e0fa5f554
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This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.3374
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## Model description
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- optimizer: Use OptimizerNames.ADAMW_BNB 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: 2
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- training_steps: 50
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| No log | 0.0013 | 1 | 4.1801 |
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| 3.664 | 0.0131 | 10 | 3.0870 |
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| 2.7874 | 0.0262 | 20 | 2.5746 |
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| 2.4451 | 0.0394 | 30 | 2.3952 |
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| 2.3297 | 0.0525 | 40 | 2.3455 |
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| 2.3253 | 0.0656 | 50 | 2.3374 |
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### Framework versions
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adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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size 17717130
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size 17717130
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