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--- |
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license: mit |
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base_model: TheBloke/zephyr-7B-alpha-GPTQ |
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tags: |
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- generated_from_trainer |
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- gptq |
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- peft |
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model-index: |
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- name: thesa |
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results: [] |
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datasets: |
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- loaiabdalslam/counselchat |
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language: |
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- en |
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pipeline_tag: text-generation |
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--- |
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# Thesa: A Therapy Chatbot 👩🏻⚕️ |
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Thesa is an experimental project of a therapy chatbot trained on mental health data and fine-tuned with the Zephyr GPTQ model that uses quantization to decrease high computatinal and storage costs. |
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## Model description |
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- Model type: fine-tuned from [TheBloke/zephyr-7B-alpha-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-alpha-GPTQ) on various mental health datasets |
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- Language(s): English |
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- License: MIT |
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## Intended uses & limitations |
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This model is purely experimental and should not be used as substitute for a mental health professional. |
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## Training evaluation |
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Training loss: |
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<img src="imgs/loss_27.2.24.png" alt="loss" width="550"/> |
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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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- warmup_ratio: 0.1 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- gradient_accumulation_steps: 1 |
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- seed: 35 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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- fp16: True |
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Learning rate overtime (warm up ratio was used during training): |
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<img src="imgs/lr_27.2.24.png" alt="lr" width="550"/> |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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- Accelerate 0.27.2 |
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- PEFT 0.8.2 |
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- Auto-GPTQ 0.6.0 |
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- TRL 0.7.11 |
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- Optimum 1.17.1 |
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- Bitsandbytes 0.42.0 |
