Qwen-UMLS-7B-Instruct [ Unified Medical Language System ]
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.
| File Name | Size | Description | Upload Status |
|---|---|---|---|
.gitattributes | 1.57 kB | File to specify LFS rules for large file tracking. | Uploaded |
README.md | 323 Bytes | Basic project information file. | Updated |
added_tokens.json | 657 Bytes | Contains additional tokens for the tokenizer. | Uploaded |
config.json | 860 Bytes | Configuration file for the model. | Uploaded |
generation_config.json | 281 Bytes | Configuration file for generation settings. | Uploaded |
merges.txt | 1.82 MB | Byte-pair encoding merge rules for tokenization. | Uploaded |
pytorch_model-00001-of-00004.bin | 4.88 GB | First part of the model's PyTorch checkpoint. | Uploaded (LFS) |
pytorch_model-00002-of-00004.bin | 4.93 GB | Second part of the model's PyTorch checkpoint. | Uploaded (LFS) |
pytorch_model-00003-of-00004.bin | 4.33 GB | Third part of the model's PyTorch checkpoint. | Uploaded (LFS) |
pytorch_model-00004-of-00004.bin | 1.09 GB | Fourth part of the model's PyTorch checkpoint. | Uploaded (LFS) |
pytorch_model.bin.index.json | 28.1 kB | Index file mapping layers to checkpoint shards. | Uploaded |
special_tokens_map.json | 644 Bytes | Maps special tokens like [CLS], [SEP], etc. | Uploaded |
tokenizer.json | 11.4 MB | Tokenizer definition and configuration. | Uploaded (LFS) |
tokenizer_config.json | 7.73 kB | Configuration file for the tokenizer. | Uploaded |
vocab.json | 2.78 MB | Vocabulary file for tokenization. | Uploaded |
Key Features:
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Medical Expertise:
- Trained on the UMLS dataset, ensuring deep domain knowledge in medical terminology, diagnostics, and treatment plans.
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Instruction-Following:
- Designed to handle complex queries with clarity and precision, suitable for diagnostic support, patient education, and research.
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High-Parameter Model:
- Leverages 7 billion parameters to deliver detailed, contextually accurate responses.
Training Details:
- Base Model: Qwen2.5-7B-Instruct
- Dataset: avaliev/UMLS
- Comprehensive dataset of medical terminologies, relationships, and use cases with 99.1k samples.
Capabilities:
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Clinical Text Analysis:
- Interpret medical notes, prescriptions, and research articles.
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Question-Answering:
- Answer medical queries, provide explanations for symptoms, and suggest treatments based on user prompts.
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Educational Support:
- Assist in learning medical terminologies and understanding complex concepts.
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Healthcare Applications:
- Integrate into clinical decision-support systems or patient care applications.
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:
from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "prithivMLmods/Qwen-UMLS-7B-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) -
Generate Medical Text:
input_text = "What are the symptoms and treatments for diabetes?" inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**inputs, max_length=200, temperature=0.7) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) -
Customizing Outputs: Modify
generation_config.jsonto optimize output style:temperaturefor creativity vs. determinism.max_lengthfor concise or extended responses.
Applications:
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Clinical Support:
- Assist healthcare providers with quick, accurate information retrieval.
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Patient Education:
- Provide patients with understandable explanations of medical conditions.
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Medical Research:
- Summarize or analyze complex medical research papers.
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AI-Driven Diagnostics:
- Integrate with diagnostic systems for preliminary assessments.