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Qwen-UMLS-7B-Instruct

Text Generation
Transformers
Safetensors
English
qwen2
Unified Medical Language System
Qwen2.5
7B
Instruct
Medical
text-generation-inference
National Library of Medicine
umls
conversational
Inference Endpoints
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metadata
license: creativeml-openrail-m
datasets:
  - avaliev/umls
language:
  - en
base_model:
  - Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
  - safetensors
  - Unified Medical Language System
  - Qwen2.5
  - 7B
  - Instruct
  - Medical
  - text-generation-inference
  - National Library of Medicine
  - umls

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:

  1. Medical Expertise:

    • Trained on the UMLS dataset, ensuring deep domain knowledge in medical terminology, diagnostics, and treatment plans.

  2. Instruction-Following:

    • Designed to handle complex queries with clarity and precision, suitable for diagnostic support, patient education, and research.

  3. High-Parameter Model:

    • Leverages 7 billion parameters to deliver detailed, contextually accurate responses.

Training Details:

Capabilities:

  1. Clinical Text Analysis:

    • Interpret medical notes, prescriptions, and research articles.

  2. Question-Answering:

    • Answer medical queries, provide explanations for symptoms, and suggest treatments based on user prompts.

  3. Educational Support:

    • Assist in learning medical terminologies and understanding complex concepts.

  4. Healthcare Applications:

    • Integrate into clinical decision-support systems or patient care applications.

Usage Instructions:

  1. Setup: Download all files and ensure compatibility with the Hugging Face Transformers library.

  2. 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)

  3. 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))

  4. Customizing Outputs: Modify generation_config.json to optimize output style:

    • temperature for creativity vs. determinism.
    • max_length for concise or extended responses.

Applications:

  1. Clinical Support:

    • Assist healthcare providers with quick, accurate information retrieval.

  2. Patient Education:

    • Provide patients with understandable explanations of medical conditions.

  3. Medical Research:

    • Summarize or analyze complex medical research papers.

  4. AI-Driven Diagnostics:

    • Integrate with diagnostic systems for preliminary assessments.