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--- |
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license: llama2 |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: codellama/CodeLlama-13b-Instruct-hf |
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model-index: |
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- name: stg-cli13b-t7-cdp-ca.dt.hlms.cln.inter-b4s1e1-20240102-0727 |
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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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# stg-cli13b-t7-cdp-ca.dt.hlms.cln.inter-b4s1e1-20240102-0727 |
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This model is a fine-tuned version of [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0656 |
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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: 0.0002 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- num_epochs: 1 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.4007 | 0.02 | 100 | 0.0985 | |
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| 0.0888 | 0.04 | 200 | 0.0823 | |
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| 0.0834 | 0.05 | 300 | 0.0800 | |
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| 0.0783 | 0.07 | 400 | 0.0799 | |
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| 0.0782 | 0.09 | 500 | 0.0755 | |
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| 0.0798 | 0.11 | 600 | 0.0771 | |
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| 0.077 | 0.13 | 700 | 0.0734 | |
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| 0.0747 | 0.14 | 800 | 0.0745 | |
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| 0.076 | 0.16 | 900 | 0.0727 | |
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| 0.0791 | 0.18 | 1000 | 0.0775 | |
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| 0.0752 | 0.2 | 1100 | 0.0717 | |
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| 0.0721 | 0.22 | 1200 | 0.0729 | |
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| 0.0731 | 0.23 | 1300 | 0.0710 | |
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| 0.0832 | 0.25 | 1400 | 0.0727 | |
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| 0.0722 | 0.27 | 1500 | 0.0715 | |
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| 0.0738 | 0.29 | 1600 | 0.0715 | |
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| 0.071 | 0.31 | 1700 | 0.0705 | |
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| 0.0738 | 0.32 | 1800 | 0.0713 | |
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| 0.075 | 0.34 | 1900 | 0.0710 | |
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| 0.0732 | 0.36 | 2000 | 0.0703 | |
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| 0.0712 | 0.38 | 2100 | 0.0701 | |
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| 0.0702 | 0.4 | 2200 | 0.0699 | |
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| 0.0733 | 0.41 | 2300 | 0.0697 | |
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| 0.0739 | 0.43 | 2400 | 0.0691 | |
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| 0.0688 | 0.45 | 2500 | 0.0684 | |
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| 0.0692 | 0.47 | 2600 | 0.0689 | |
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| 0.0727 | 0.49 | 2700 | 0.0690 | |
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| 0.073 | 0.5 | 2800 | 0.0685 | |
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| 0.0752 | 0.52 | 2900 | 0.0691 | |
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| 0.0696 | 0.54 | 3000 | 0.0681 | |
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| 0.0708 | 0.56 | 3100 | 0.0684 | |
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| 0.072 | 0.58 | 3200 | 0.0681 | |
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| 0.0716 | 0.59 | 3300 | 0.0689 | |
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| 0.0723 | 0.61 | 3400 | 0.0678 | |
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| 0.0678 | 0.63 | 3500 | 0.0676 | |
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| 0.0695 | 0.65 | 3600 | 0.0672 | |
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| 0.0689 | 0.67 | 3700 | 0.0676 | |
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| 0.0716 | 0.68 | 3800 | 0.0671 | |
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| 0.07 | 0.7 | 3900 | 0.0667 | |
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| 0.0683 | 0.72 | 4000 | 0.0665 | |
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| 0.0704 | 0.74 | 4100 | 0.0664 | |
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| 0.0702 | 0.76 | 4200 | 0.0665 | |
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| 0.0678 | 0.77 | 4300 | 0.0662 | |
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| 0.0679 | 0.79 | 4400 | 0.0661 | |
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| 0.069 | 0.81 | 4500 | 0.0660 | |
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| 0.0675 | 0.83 | 4600 | 0.0661 | |
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| 0.0682 | 0.85 | 4700 | 0.0660 | |
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| 0.0697 | 0.86 | 4800 | 0.0659 | |
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| 0.0689 | 0.88 | 4900 | 0.0658 | |
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| 0.0665 | 0.9 | 5000 | 0.0658 | |
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| 0.067 | 0.92 | 5100 | 0.0657 | |
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| 0.0666 | 0.94 | 5200 | 0.0657 | |
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| 0.0704 | 0.95 | 5300 | 0.0656 | |
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| 0.0682 | 0.97 | 5400 | 0.0656 | |
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| 0.0663 | 0.99 | 5500 | 0.0656 | |
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### Framework versions |
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- Transformers 4.37.0.dev0 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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## Training procedure |
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The following `bitsandbytes` quantization config was used during training: |
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- quant_method: QuantizationMethod.BITS_AND_BYTES |
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- load_in_8bit: False |
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- load_in_4bit: True |
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- llm_int8_threshold: 6.0 |
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- llm_int8_skip_modules: None |
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- llm_int8_enable_fp32_cpu_offload: False |
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- llm_int8_has_fp16_weight: False |
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- bnb_4bit_quant_type: nf4 |
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- bnb_4bit_use_double_quant: True |
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- bnb_4bit_compute_dtype: bfloat16 |
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### Framework versions |
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- PEFT 0.6.2 |
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