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--- |
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library_name: peft |
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tags: |
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- PyTorch |
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- Transformers |
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- trl |
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- sft |
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- BitsAndBytes |
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- PEFT |
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- QLoRA |
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datasets: |
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- databricks/databricks-dolly-15k |
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base_model: meta-llama/Llama-2-7b-chat |
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model-index: |
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- name: llama2-7-dolly-query |
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results: [] |
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license: mit |
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language: |
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- en |
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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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# llama2-7-dolly-query |
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This model is a fine-tuned version of [Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat) on the generator dataset. |
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Can be used in conjunction with [LukeOLuck/llama2-7-dolly-answer](https://huggingface.co/LukeOLuck/llama2-7-dolly-answer) |
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## Model description |
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A Fine-Tuned PEFT Adapter for the llama2 7b chat hf model |
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Leverages [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://arxiv.org/abs/2205.14135), [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314), and [PEFT](https://huggingface.co/blog/peft) |
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## Intended uses & limitations |
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Generate a query based on context and input |
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## Training and evaluation data |
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Used SFTTrainer, [checkout the code](https://colab.research.google.com/drive/1sr0mUF8dwYKo6NNR3tkjk0Z-p5FFr1_6?usp=sharing) |
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## Training procedure |
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[Checkout the code here](https://colab.research.google.com/drive/1sr0mUF8dwYKo6NNR3tkjk0Z-p5FFr1_6?usp=sharing) |
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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: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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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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- num_epochs: 3 |
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### Training results |
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### Framework versions |
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- PEFT 0.8.2 |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
