Update README.md

README.md

@@ -16,6 +16,162 @@ base_model: WangCa/Qwen2.5-7B-Medicine
 This model was converted to GGUF format from [`WangCa/Qwen2.5-7B-Medicine`](https://huggingface.co/WangCa/Qwen2.5-7B-Medicine) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/WangCa/Qwen2.5-7B-Medicine) for more details on the model.
 
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+Model Description
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+Qwen2.5-7B-Instruct-Medical is a medical domain-specific model 
+fine-tuned from the Qwen2.5-7B-Instruct model using 340,000 medical 
+dialogue samples. This model is optimized to provide accurate and 
+contextually relevant responses to medical-related inquiries, making it 
+an excellent choice for healthcare applications such as medical 
+chatbots, decision support systems, and educational tools.
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+Model Details
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+Base Model: Qwen2.5-7B-Instruct
+Fine-tuning Dataset: 340,000 medical dialogue samples
+Training Duration: 51 hours
+Hardware Used: 6x NVIDIA RTX 3090 (24GB VRAM)
+Optimization Algorithm: AdamW
+Training Method: LoRA (Low-Rank Adaptation)
+Training Framework: PyTorch
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+Performance
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+BLEU-4 Score:
+Base Model: 23.5 (on a test set of 500 samples)
+Fine-tuned Model: 55.7 (on the same test set)
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+This shows a significant improvement in the model's ability to 
+generate more fluent and contextually relevant responses after 
+fine-tuning on the medical dialogue dataset.
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+Intended Use
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+This model is specifically tailored for medical dialogue tasks and can be used for:
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+Medical question answering
+Healthcare chatbots
+Clinical decision support systems
+Medical education and training
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+Performance
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+The model exhibits a strong understanding of medical terminology, 
+clinical contexts, and patient interactions, making it a powerful tool 
+for applications in healthcare and medical research.
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+Usage
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+To use this model, you can load it using the transformers library in Python:
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+from transformers import AutoModelForCausalLM, AutoTokenizer
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+model = AutoModelForCausalLM.from_pretrained("path_to_model")
+tokenizer = AutoTokenizer.from_pretrained("path_to_model")
+
+input_text = "What are the symptoms of diabetes?"
+inputs = tokenizer(input_text, return_tensors="pt")
+output = model.generate(**inputs)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+<
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+Limitations
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+While this model has been fine-tuned on a medical dialogue dataset, 
+it may still make errors or provide inaccurate responses in highly 
+specialized medical domains or cases where the input data falls outside 
+the training data's coverage. Always ensure human supervision in 
+critical healthcare scenarios.
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+License
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+This model is released under the MIT License.
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+Acknowledgements
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+Dataset: 340,000 medical dialogues (From Modelscope).
+LoRA (Low-Rank Adaptation): This technique was used to efficiently 
+fine-tune the model without modifying the full parameter set, allowing 
+for faster and more memory-efficient training.
+
+---
+
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)