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README.md
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# MedleyMD
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# NOTE: Experimental
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![logo](https://huggingface.co/sethuiyer/MedleyMD/resolve/main/logo.webp)
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* [sethuiyer/Dr_Samantha_7b_mistral](https://huggingface.co/sethuiyer/Dr_Samantha_7b_mistral)
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* [fblgit/UNA-TheBeagle-7b-v1](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1)
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## 🧩 Configuration
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```yaml
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```
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## 💻 Usage
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```python
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messages = [{"role":"system", "content":"You are an helpful AI assistant. Please use </s> when you want to end the answer."},
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{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
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prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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outputs = pipeline(prompt,
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print(outputs[0]["generated_text"])
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```
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# MedleyMD
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![logo](https://huggingface.co/sethuiyer/MedleyMD/resolve/main/logo.webp)
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* [sethuiyer/Dr_Samantha_7b_mistral](https://huggingface.co/sethuiyer/Dr_Samantha_7b_mistral)
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* [fblgit/UNA-TheBeagle-7b-v1](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1)
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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.
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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. 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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## 💻 Usage
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```python
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messages = [{"role":"system", "content":"You are an helpful AI assistant. Please use </s> when you want to end the answer."},
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{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
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prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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outputs = pipeline(prompt, **generation_kwargs)
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print(outputs[0]["generated_text"])
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```
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