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README.md
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This model was converted to GGUF format from [`prithivMLmods/Llama-Doctor-3.2-3B-Instruct`](https://huggingface.co/prithivMLmods/Llama-Doctor-3.2-3B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/prithivMLmods/Llama-Doctor-3.2-3B-Instruct) for more details on the model.
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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This model was converted to GGUF format from [`prithivMLmods/Llama-Doctor-3.2-3B-Instruct`](https://huggingface.co/prithivMLmods/Llama-Doctor-3.2-3B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/prithivMLmods/Llama-Doctor-3.2-3B-Instruct) for more details on the model.
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Model details:
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The Llama-Doctor-3.2-3B-Instruct model is designed for text generation tasks, particularly in contexts where instruction-following capabilities are needed. This model is a fine-tuned version of the base Llama-3.2-3B-Instruct model and is optimized for understanding and responding to user-provided instructions or prompts. The model has been trained on a specialized dataset, avaliev/chat_doctor, to enhance its performance in providing conversational or advisory responses, especially in medical or technical fields.
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Key Use Cases:
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Conversational AI: Engage in dialogue, answering questions, or providing responses based on user instructions.
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Text Generation: Generate content, summaries, explanations, or solutions to problems based on given prompts.
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Instruction Following: Understand and execute instructions, potentially in complex or specialized domains like medical, technical, or academic fields.
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The model leverages a PyTorch-based architecture and comes with various files such as configuration files, tokenizer files, and special tokens maps to facilitate smooth deployment and interaction.
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Intended Applications:
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Chatbots for customer support or virtual assistants.
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Medical Consultation Tools for generating advice or answering medical queries (given its training on the chat_doctor dataset).
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Content Creation tools, helping generate text based on specific instructions.
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Problem-solving Assistants that offer explanations or answers to user queries, particularly in instructional contexts.
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---
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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