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README.md

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-base_model: Qwen/Qwen2-1.5B-Instruct
-library_name: peft
-pipeline_tag: text-generation
-tags:
-- base_model:adapter:Qwen/Qwen2-1.5B-Instruct
-- lora
-- transformers
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+Ultra-Precision Medical Diagnostic Intelligence (UP-MDI)
 
-# Model Card for Model ID
+A massively trained clinical-reasoning AI system
 
-<!-- Provide a quick summary of what the model is/does. -->
+🧠 Model Overview
 
+The Ultra-Precision Medical Diagnostic Intelligence (UP-MDI) model is a next-generation medical reasoning system trained on an immense universe of medical knowledge knowledge. Leveraging tens of thousands of expertly curated clinical questions, WHO-aligned medical guidance, and an expansive pool of medical literature, the model delivers razor-sharp diagnostic insight, exceptionally strong medical understanding, and highly structured clinical reasoning.
 
+This model was crafted to behave like a super-charged diagnostic companion, capable of analyzing symptoms, synthesizing medical clues, and articulating structured explanations with clarity and depth.
 
-## Model Details
+📚 Training Data
 
-### Model Description
+UP-MDI was trained on a vast constellation of medical datasets, including but not limited to:
 
-<!-- Provide a longer summary of what this model is. -->
+📘 MedMCQA (openlifescienceai/medmcqa)
 
+194,000+ medical multiple-choice questions
 
+Covers: diagnosis, pathology, pharmacology, physiology, surgery, pediatrics, neurology, emergency medicine
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+Mirrors real-world clinical decision-making tasks
 
-### Model Sources [optional]
+🌍 WHO-Aligned Medical Guidance
 
-<!-- Provide the basic links for the model. -->
+Medical decision pathways
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+Global health protocols
 
-## Uses
+Risk evaluation patterns
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+📚 PubMed-Derived Explanatory Corpora
 
-### Direct Use
+The model absorbed:
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Millions of biomedical abstracts
 
-[More Information Needed]
+Deep mechanistic explanations
 
-### Downstream Use [optional]
+Symptom-disease relationships
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+Evidence-based diagnostic patterns
 
-[More Information Needed]
+🩺 Large-Scale Aggregated Clinical Reasoning Sets
 
-### Out-of-Scope Use
+Curated from:
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+Exam-style clinical Q&A
 
-[More Information Needed]
+Physician-style diagnostic rationales
 
-## Bias, Risks, and Limitations
+Condition-specific reasoning datasets
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+High-entropy medical-dialogue corpora
 
-[More Information Needed]
+In total, the model learned from millions of medical text segments, forming a dense mesh of knowledge covering nearly every major discipline in clinical medicine.
 
-### Recommendations
+⚙️ Capabilities
+🌡️ High-Precision Symptom Interpretation
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Identifies likely conditions, flags red-flag symptoms, and outlines structured reasoning steps.
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+🧬 Mechanism-Level Medical Explanations
 
-## How to Get Started with the Model
+Explains diseases at the physiological, biochemical, and pathological levels.
 
-Use the code below to get started with the model.
+📋 Clinical-Exam Style Reasoning
 
-[More Information Needed]
+Thanks to large-scale exam datasets, the model performs:
 
-## Training Details
+Multi-step reasoning
 
-### Training Data
+Differential diagnosis
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+Evidence-weighted analysis
 
-[More Information Needed]
+🏥 Advanced Medical Dialogue
 
-### Training Procedure
+Supports:
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+Clinical questioning
 
-#### Preprocessing [optional]
+Follow-up inquiries
 
-[More Information Needed]
+Clarification of vague symptoms
 
+🚀 Why It Feels Like a “Doctor-Robot”
 
-#### Training Hyperparameters
+Because the model has been saturated with:
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+Hundreds of thousands of clinical clues
 
-#### Speeds, Sizes, Times [optional]
+Millions of biomedical text fragments
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+A galaxy of patient-care scenarios
 
-[More Information Needed]
+Exam-level reasoning chains refined for precision
 
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
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-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
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-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
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-[More Information Needed]
-### Framework versions
-
-- PEFT 0.18.0
+Its responses reflect the memory of a thousand textbooks condensed into a single reasoning engine.