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metadata
base_model: huzaifa525/MedGenius_LLaMA-3.2B
datasets:
  - >-
    huzaifa525/Medical_Intelligence_Dataset_40k_Rows_of_Disease_Info_Treatments_and_Medical_QA
language:
  - en
library_name: transformers
license: apache-2.0
tags:
  - Medical AI
  - AI-powered healthcare
  - Diagnostic AI model
  - Medical chatbot
  - Healthcare AI solutions
  - Symptom analysis AI
  - Disease diagnosis model
  - Medical NLP model
  - AI for doctors
  - Medical Q&A model
  - Healthcare chatbot
  - AI in telemedicine
  - Medical research assistant
  - AI medical assistant
  - Disease treatment suggestions AI
  - Medical education AI
  - AI in healthcare innovation
  - LLaMA medical model
  - AI healthcare applications
  - Medical intelligence dataset
  - llama-cpp
  - gguf-my-repo

huzaifa525/MedGenius_LLaMA-3.2B-Q4_K_M-GGUF

Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo huzaifa525/MedGenius_LLaMA-3.2B-Q4_K_M-GGUF --hf-file medgenius_llama-3.2b-q4_k_m.gguf -p "What is the use of Paracetamol"

Server:

llama-server --hf-repo huzaifa525/MedGenius_LLaMA-3.2B-Q4_K_M-GGUF --hf-file medgenius_llama-3.2b-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo huzaifa525/MedGenius_LLaMA-3.2B-Q4_K_M-GGUF --hf-file medgenius_llama-3.2b-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo huzaifa525/MedGenius_LLaMA-3.2B-Q4_K_M-GGUF --hf-file medgenius_llama-3.2b-q4_k_m.gguf -c 2048