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--- |
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tags: |
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- autotrain |
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- text-generation |
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- pytorch |
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- text-generation-inference |
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- transformers |
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widget: |
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- text: 'I love AutoTrain because ' |
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license: apache-2.0 |
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datasets: |
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- Amod/mental_health_counseling_conversations |
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library_name: peft |
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--- |
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# Model Trained Using AutoTrain |
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This model was trained using AutoTrain and is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the [mental_health_counseling_conversations](https://huggingface.co/datasets/Amod/mental_health_counseling_conversations) dataset. |
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For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). |
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## Model description |
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A Mistral-7B-Instruct-v0.2 model finetuned on a corpus of mental health conversations between a psychologist and a user. |
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The intention was to create a mental health assistant, "Connor", to address user questions based on responses from a psychologist. |
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## Training data |
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The model is finetuned on a corpus of mental health conversations between a psychologist and a client, in the form of context - response pairs. This dataset is a collection of questions and answers sourced from two online counseling and therapy platforms. The questions cover a wide range of mental health topics, and the answers are provided by qualified psychologists. |
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Dataset found here :- |
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* [Kaggle](https://www.kaggle.com/datasets/thedevastator/nlp-mental-health-conversations) |
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* [Huggingface](https://huggingface.co/datasets/Amod/mental_health_counseling_conversations) |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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TODO |
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# Usage |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_path = "GRMenon/mental-mistral-7b-instruct-autotrain" |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_path, |
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device_map="auto", |
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torch_dtype='auto' |
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).eval() |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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# Prompt content: |
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messages = [ |
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{"role": "user", "content": "Hey Connor! I have been feeling a bit down lately. I could really use some advice on how to feel better?"} |
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] |
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input_ids = tokenizer.apply_chat_template(conversation=messages, |
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tokenize=True, |
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add_generation_prompt=True, |
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return_tensors='pt').to(device) |
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output_ids = model.generate(input_ids=input_ids, |
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max_new_tokens=512, |
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do_sample=True, |
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pad_token_id=2) |
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response = tokenizer.batch_decode(output_ids.detach().cpu().numpy(), |
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skip_special_tokens = True) |
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# Model response: |
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print(response[0]) |
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``` |
