skkjodhpur
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Gemma-Code-Instruct-Finetune-by-skk

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-library_name: transformers
-tags: []
 ---
 # Model Card for Model ID
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 ---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{}
 ---
 # Model Card for Model ID
+Fine-Tuning GEMMA-2B for Doctor-Patient Interaction: Efficient Model Adaptation Using LoRA and 4-bit Quantization
+library_name: transformers
+tags: ["healthcare", "doctor-patient interaction", "fine-tuning", "LoRA", "quantization"]
+Model Card for Fine-Tuned GEMMA-2B
+Model Details
+Model Description
+This model is a fine-tuned version of GEMMA-2B, adapted specifically for doctor-patient interaction tasks. Fine-tuning was performed using Low-Rank Adaptation (LoRA) and 4-bit quantization for efficient model adaptation. The model aims to facilitate improved, context-aware, and relevant interactions in medical consultations, enhancing communication and understanding between healthcare providers and patients.
 ## Model Details
 ### Model Description
+Developed by: Shailesh Kumar Khanchandani
+Shared by: Shailesh Kumar Khanchandani
+Model type: Causal Language Model
+Language(s) (NLP): English
+License: Apache-2.0
+Finetuned from model: google/gemma-2b
 ### Model Sources [optional]
+Repository: Fine-Tuned GEMMA-2B
+Paper: [More Information Needed]
+Demo: [More Information Needed]
 ### Direct Use
+The model is intended for direct use in generating contextually appropriate responses for doctor-patient interactions. It can be used in virtual assistants, chatbots, and other medical consultation platforms to assist healthcare providers in communicating with patients.
 ### Downstream Use [optional]
+The model can be further fine-tuned for specific medical specialties or customized to fit the unique requirements of different healthcare systems.
 ### Out-of-Scope Use
+The model is not intended for use in generating medical advice or diagnoses without human oversight. It should not be used in any scenario where critical health decisions are made solely based on its outputs.
 ## Bias, Risks, and Limitations
+The model inherits biases present in the training data. Users should be cautious of potential biases and inaccuracies, particularly in sensitive medical contexts.
 ### Recommendations
+Users should ensure that outputs are reviewed by qualified healthcare professionals before being utilized in a clinical setting. Continuous monitoring and updating of the model with diverse and representative data can help mitigate biases.