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--- |
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library_name: transformers |
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tags: |
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- medical |
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- text-generation-inference |
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- llm |
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- falcon |
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- falcon-mamba |
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- multiturn |
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license: apache-2.0 |
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datasets: |
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- ruslanmv/ai-medical-chatbot |
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- lavita/ChatDoctor-HealthCareMagic-100k |
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language: |
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- en |
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metrics: |
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- rouge |
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- bleu |
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base_model: |
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- tiiuae/falcon-mamba-7b |
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pipeline_tag: question-answering |
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--- |
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# Model Card for falcon mamba Multi-Turn Doctor Conversation Model |
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## Model Details |
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### Model Description |
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This model is a fine-tuned version of the falcon mamba model, specifically tailored for multi-turn doctor-patient conversations. It leverages the powerful language generation capabilities of falcon to provide accurate and context-aware responses in medical dialogue scenarios. |
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- **Developed by:** Siyahul Haque T P |
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- **Model type:** Text-generation (LLM) |
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- **Language(s) (NLP):** English (en) |
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- **License:** Apache-2.0 |
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- **Finetuned from model:** falcon mamba |
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## Uses |
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### Direct Use |
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This model can be directly used for generating responses in multi-turn medical conversations, making it useful for applications like virtual health assistants and medical chatbots. |
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### Downstream Use |
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This model can be further fine-tuned or integrated into larger healthcare applications, such as patient management systems or automated symptom checkers. |
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### Out-of-Scope Use |
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The model is not suitable for use in emergency medical situations, providing final diagnoses, or replacing professional medical advice. |
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## Bias, Risks, and Limitations |
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The model may reflect biases present in the training data, including underrepresentation of certain medical conditions or demographic groups. The model should not be used as a sole source of medical information and must be supervised by qualified healthcare professionals. |
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### Recommendations |
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Users should be aware of the potential biases and limitations of the model. It is recommended to use the model as a supplementary tool rather than a primary source of medical advice. |
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## How to Get Started with the Model |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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# Load the tokenizer and model from the Hugging Face Hub |
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tokenizer = AutoTokenizer.from_pretrained("siyah1/falcon-7b-mamba-mental-health") |
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model = AutoModelForCausalLM.from_pretrained("siyah1/falcon-7b-mamba-mental-health") |
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# Example input: patient describing a symptom |
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input_text = "Hi, doctor" |
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# Tokenize the input text |
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inputs = tokenizer(input_text, return_tensors="pt") |
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# Generate a response from the model |
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outputs = model.generate(**inputs, max_length=100, num_return_sequences=1) |
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# Decode the generated response |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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# Print the model's response |
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print("Doctor:", response) |
