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--- |
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license: other |
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library_name: peft |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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base_model: deepseek-ai/deepseek-coder-1.3b-base |
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datasets: |
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- generator |
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model-index: |
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- name: hyperparam-rust-lora-32 |
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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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# hyperparam-rust-lora-32 |
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This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) on the generator dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4306 |
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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: 0.0003 |
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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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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 32 |
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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_ratio: 0.05 |
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- lr_scheduler_warmup_steps: 3 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.7347 | 0.3 | 25 | 0.5297 | |
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| 0.4878 | 0.59 | 50 | 0.4810 | |
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| 0.4589 | 0.89 | 75 | 0.4646 | |
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| 0.4459 | 1.18 | 100 | 0.4549 | |
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| 0.4273 | 1.48 | 125 | 0.4481 | |
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| 0.429 | 1.77 | 150 | 0.4428 | |
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| 0.423 | 2.07 | 175 | 0.4387 | |
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| 0.4131 | 2.37 | 200 | 0.4363 | |
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| 0.4027 | 2.66 | 225 | 0.4345 | |
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| 0.4085 | 2.96 | 250 | 0.4326 | |
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| 0.3993 | 3.25 | 275 | 0.4322 | |
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| 0.4024 | 3.55 | 300 | 0.4313 | |
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| 0.4011 | 3.84 | 325 | 0.4308 | |
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| 0.3982 | 4.14 | 350 | 0.4307 | |
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| 0.3982 | 4.43 | 375 | 0.4307 | |
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| 0.3934 | 4.73 | 400 | 0.4306 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.39.3 |
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- Pytorch 2.2.1 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
