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--- |
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base_model: meta-llama/Llama-2-70b-hf |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: qlora-llama70-ft-full-dataset |
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results: [] |
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library_name: peft |
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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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# qlora-llama70-ft-full-dataset |
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This model is a fine-tuned version of [meta-llama/Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4237 |
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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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The following `bitsandbytes` quantization config was used during training: |
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- quant_method: bitsandbytes |
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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: float16 |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0025 |
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- train_batch_size: 2 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- total_train_batch_size: 16 |
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- total_eval_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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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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| 1.5952 | 0.16 | 5 | 1.5179 | |
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| 1.6321 | 0.32 | 10 | 1.5725 | |
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| 1.511 | 0.48 | 15 | 1.4764 | |
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| 1.4807 | 0.65 | 20 | 1.4482 | |
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| 1.439 | 0.81 | 25 | 1.4329 | |
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| 1.4266 | 0.97 | 30 | 1.4245 | |
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### Framework versions |
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- PEFT 0.5.0 |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.14.1 |
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- Tokenizers 0.15.0 |
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