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Update README.md

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@@ -24,6 +24,13 @@ MedleyMD is a Mixure of Experts (MoE) made with the following models using [Lazy
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  These models were chosen because `fblgit/UNA-TheBeagle-7b-v1` has excellent performance for a 7B parameter model and Dr.Samantha has capabilities of a medical knowledge-focused model (trained on USMLE databases and doctor-patient interactions) with the philosophical, psychological, and relational understanding, scoring 68.82% in topics related to clinical domain and psychology.
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  ## 🧩 Configuration
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  ```yaml
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  ```
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  ## GGUF
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- GGUF is available to download.
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  1. [medleymd.Q4_K_M](https://huggingface.co/sethuiyer/MedleyMD-GGUF/resolve/main/medleymd.Q4_K_M.gguf) [7.2GB]
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  2. [medleymd.Q5_K_M](https://huggingface.co/sethuiyer/MedleyMD-GGUF/resolve/main/medleymd.Q5_K_M.gguf) [9.13GB]
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  ## Ollama
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- MedleyMD is now available on Ollama. You can use it by running the command ```ollama run stuehieyr/medleymd``` in your
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- terminal.
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  If you have limited computing resources, check out this [video](https://www.youtube.com/watch?v=Qa1h7ygwQq8) to learn how to run it on
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  a Google Colab backend.
 
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  These models were chosen because `fblgit/UNA-TheBeagle-7b-v1` has excellent performance for a 7B parameter model and Dr.Samantha has capabilities of a medical knowledge-focused model (trained on USMLE databases and doctor-patient interactions) with the philosophical, psychological, and relational understanding, scoring 68.82% in topics related to clinical domain and psychology.
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+ ## Benchmark
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+ On a synthetic benchmark of 15 medical diagnosis questions generated by GPT-4, GPT-4 also being an evaluator, MedleyMD scored 94.25/100.
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+ Nous Benchmark numbers shall be available soon.
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  ## 🧩 Configuration
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  ```yaml
 
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  ```
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  ## GGUF
 
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  1. [medleymd.Q4_K_M](https://huggingface.co/sethuiyer/MedleyMD-GGUF/resolve/main/medleymd.Q4_K_M.gguf) [7.2GB]
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  2. [medleymd.Q5_K_M](https://huggingface.co/sethuiyer/MedleyMD-GGUF/resolve/main/medleymd.Q5_K_M.gguf) [9.13GB]
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  ## Ollama
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+ MedleyMD can be used in ollama by running```ollama run stuehieyr/medleymd``` in your terminal.
 
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  If you have limited computing resources, check out this [video](https://www.youtube.com/watch?v=Qa1h7ygwQq8) to learn how to run it on
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  a Google Colab backend.