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update model card README.md

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  ---
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- library_name: peft
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Training procedure
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- The following `bitsandbytes` quantization config was used during training:
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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: False
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- - bnb_4bit_compute_dtype: float16
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- ### Framework versions
 
 
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- - PEFT 0.6.0.dev0
 
 
 
 
 
 
 
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+ base_model: meta-llama/Llama-2-7b-hf
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: llama2-7B-ft-IM-med
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+ results: []
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  ---
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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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+
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+ # llama2-7B-ft-IM-med
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+
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+ This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset.
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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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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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 16
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: constant
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+ - lr_scheduler_warmup_ratio: 0.03
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+ - training_steps: 1000
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+
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+ ### Training results
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+ ### Framework versions
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+
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+ - Transformers 4.31.0
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+ - Pytorch 2.0.1+cu117
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+ - Datasets 2.12.0
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+ - Tokenizers 0.13.3