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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-t6-cdp-ca.mt.him.cln.inter-b4s1e1-20231220-1052 |
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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-t6-cdp-ca.mt.him.cln.inter-b4s1e1-20231220-1052 |
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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: 0.0472 |
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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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| 0.3435 | 0.03 | 100 | 0.0703 | |
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| 0.0654 | 0.07 | 200 | 0.0586 | |
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| 0.0579 | 0.1 | 300 | 0.0563 | |
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| 0.0567 | 0.14 | 400 | 0.0562 | |
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| 0.0551 | 0.17 | 500 | 0.0547 | |
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| 0.0547 | 0.21 | 600 | 0.0526 | |
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| 0.0532 | 0.24 | 700 | 0.0516 | |
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| 0.0534 | 0.28 | 800 | 0.0515 | |
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| 0.0521 | 0.31 | 900 | 0.0520 | |
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| 0.0522 | 0.35 | 1000 | 0.0517 | |
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| 0.0518 | 0.38 | 1100 | 0.0511 | |
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| 0.051 | 0.42 | 1200 | 0.0502 | |
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| 0.0517 | 0.45 | 1300 | 0.0494 | |
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| 0.0506 | 0.49 | 1400 | 0.0499 | |
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| 0.0511 | 0.52 | 1500 | 0.0496 | |
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| 0.05 | 0.56 | 1600 | 0.0493 | |
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| 0.05 | 0.59 | 1700 | 0.0497 | |
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| 0.049 | 0.63 | 1800 | 0.0485 | |
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| 0.0487 | 0.66 | 1900 | 0.0484 | |
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| 0.0492 | 0.7 | 2000 | 0.0483 | |
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| 0.0493 | 0.73 | 2100 | 0.0481 | |
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| 0.0483 | 0.77 | 2200 | 0.0478 | |
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| 0.048 | 0.8 | 2300 | 0.0478 | |
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| 0.048 | 0.83 | 2400 | 0.0476 | |
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| 0.0476 | 0.87 | 2500 | 0.0474 | |
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| 0.0471 | 0.9 | 2600 | 0.0473 | |
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| 0.0472 | 0.94 | 2700 | 0.0472 | |
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| 0.0469 | 0.97 | 2800 | 0.0472 | |
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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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