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  This model was converted to GGUF format from [`prithivMLmods/Qwen-UMLS-7B-Instruct`](https://huggingface.co/prithivMLmods/Qwen-UMLS-7B-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/Qwen-UMLS-7B-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/Qwen-UMLS-7B-Instruct`](https://huggingface.co/prithivMLmods/Qwen-UMLS-7B-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/Qwen-UMLS-7B-Instruct) for more details on the model.
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+ ---
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+ Model details:
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+ -
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+ The Qwen-UMLS-7B-Instruct model is a specialized, instruction-tuned language model designed for medical and healthcare-related tasks. It is fine-tuned on the Qwen2.5-7B-Instruct base model using the UMLS (Unified Medical Language System) dataset, making it an invaluable tool for medical professionals, researchers, and developers building healthcare applications.
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+ Key Features:
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+ Medical Expertise:
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+ Trained on the UMLS dataset, ensuring deep domain knowledge in medical terminology, diagnostics, and treatment plans.
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+ Instruction-Following:
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+ Designed to handle complex queries with clarity and precision, suitable for diagnostic support, patient education, and research.
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+ High-Parameter Model:
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+ Leverages 7 billion parameters to deliver detailed, contextually accurate responses.
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+ Training Details:
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+ Base Model: Qwen2.5-7B-Instruct
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+ Dataset: avaliev/UMLS
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+ Comprehensive dataset of medical terminologies, relationships, and use cases with 99.1k samples.
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+ Capabilities:
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+ Clinical Text Analysis:
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+ Interpret medical notes, prescriptions, and research articles.
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+ Question-Answering:
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+ Answer medical queries, provide explanations for symptoms, and suggest treatments based on user prompts.
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+ Educational Support:
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+ Assist in learning medical terminologies and understanding complex concepts.
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+ Healthcare Applications:
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+ Integrate into clinical decision-support systems or patient care applications.
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+ Usage Instructions:
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+ Setup: Download all files and ensure compatibility with the Hugging Face Transformers library.
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+ Loading the Model:
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ model_name = "prithivMLmods/Qwen-UMLS-7B-Instruct"
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+ model = AutoModelForCausalLM.from_pretrained(model_name)
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+ Generate Medical Text:
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+ input_text = "What are the symptoms and treatments for diabetes?"
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+ inputs = tokenizer(input_text, return_tensors="pt")
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+ outputs = model.generate(**inputs, max_length=200, temperature=0.7)
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+ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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+ Customizing Outputs: Modify generation_config.json to optimize output style:
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+ temperature for creativity vs. determinism.
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+ max_length for concise or extended responses.
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+ Applications:
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+ Clinical Support:
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+ Assist healthcare providers with quick, accurate information retrieval.
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+ Patient Education:
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+ Provide patients with understandable explanations of medical conditions.
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+ Medical Research:
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+ Summarize or analyze complex medical research papers.
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+ AI-Driven Diagnostics:
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+ Integrate with diagnostic systems for preliminary assessments.
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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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