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README.md
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---
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license: apache-2.0
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base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
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tags:
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- generated_from_trainer
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model-index:
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- name: tiny-llama
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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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# tiny-llama
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This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on an unknown dataset.
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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: bfloat16
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- bnb_4bit_quant_storage: uint8
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- load_in_4bit: True
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- load_in_8bit: False
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 8
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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: linear
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- training_steps: 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 | Accuracy | Precision | Recall | Precision Macro | Recall Macro | Macro Fpr | Weighted Fpr | Weighted Specificity | Macro Specificity | Weighted Sensitivity | Macro Sensitivity | F1 Micro | F1 Macro | F1 Weighted |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
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| No log | 0.0 | 1 | 4.1563 | 0.0225 | 0.0757 | 0.0225 | 0.0327 | 0.0617 | 0.4479 | 0.7566 | 0.9655 | 0.9325 | 0.0225 | 0.0617 | 0.0225 | 0.0149 | 0.0211 |
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### Framework versions
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- PEFT 0.4.0
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- Transformers 4.39.3
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- Pytorch 2.1.2
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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