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README.md
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---
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base_model: NousResearch/Llama-2-7b-chat-hf
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tags:
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- generated_from_trainer
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model-index:
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- name: Cosine_matric_llama2_prompt1
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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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# Cosine_matric_llama2_prompt1
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This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9479
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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: False
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- bnb_4bit_compute_dtype: float16
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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: 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: constant
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- lr_scheduler_warmup_ratio: 0.05
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- num_epochs: 2
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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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| 1.0276 | 0.4 | 384 | 0.9479 |
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| 1.0277 | 0.8 | 768 | 0.9479 |
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| 1.0213 | 1.2 | 1152 | 0.9479 |
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| 1.0277 | 1.6 | 1536 | 0.9479 |
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| 0.8449 | 2.0 | 1920 | 0.9479 |
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### Framework versions
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- PEFT 0.4.0
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- Transformers 4.38.2
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- Pytorch 2.1.2
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- Datasets 2.1.0
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- Tokenizers 0.15.2
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