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---
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license: creativeml-openrail-m
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datasets:
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- avaliev/umls
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- safetensors
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- Unified Medical Language System
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- Qwen2.5
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- 7B
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- Instruct
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- Medical
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- text-generation-inference
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- National Library of Medicine
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- umls
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---
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### Qwen-UMLS-7B-Instruct `[ Unified Medical Language System ]`
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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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| **File Name** | **Size** | **Description** | **Upload Status** |
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|-----------------------------------------|----------------|-------------------------------------------------|--------------------|
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| `.gitattributes` | 1.57 kB | File to specify LFS rules for large file tracking. | Uploaded |
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| `README.md` | 323 Bytes | Basic project information file. | Updated |
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| `added_tokens.json` | 657 Bytes | Contains additional tokens for the tokenizer. | Uploaded |
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| `config.json` | 860 Bytes | Configuration file for the model. | Uploaded |
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| `generation_config.json` | 281 Bytes | Configuration file for generation settings. | Uploaded |
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| `merges.txt` | 1.82 MB | Byte-pair encoding merge rules for tokenization.| Uploaded |
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| `pytorch_model-00001-of-00004.bin` | 4.88 GB | First part of the model's PyTorch checkpoint. | Uploaded (LFS) |
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| `pytorch_model-00002-of-00004.bin` | 4.93 GB | Second part of the model's PyTorch checkpoint. | Uploaded (LFS) |
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| `pytorch_model-00003-of-00004.bin` | 4.33 GB | Third part of the model's PyTorch checkpoint. | Uploaded (LFS) |
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| `pytorch_model-00004-of-00004.bin` | 1.09 GB | Fourth part of the model's PyTorch checkpoint. | Uploaded (LFS) |
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| `pytorch_model.bin.index.json` | 28.1 kB | Index file mapping layers to checkpoint shards. | Uploaded |
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| `special_tokens_map.json` | 644 Bytes | Maps special tokens like `[CLS]`, `[SEP]`, etc. | Uploaded |
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| `tokenizer.json` | 11.4 MB | Tokenizer definition and configuration. | Uploaded (LFS) |
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| `tokenizer_config.json` | 7.73 kB | Configuration file for the tokenizer. | Uploaded |
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| `vocab.json` | 2.78 MB | Vocabulary file for tokenization. | Uploaded |
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### **Key Features:**
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1. **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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2. **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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3. **High-Parameter Model:**
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- Leverages 7 billion parameters to deliver detailed, contextually accurate responses.
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---
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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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---
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### **Capabilities:**
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1. **Clinical Text Analysis:**
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- Interpret medical notes, prescriptions, and research articles.
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2. **Question-Answering:**
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- Answer medical queries, provide explanations for symptoms, and suggest treatments based on user prompts.
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3. **Educational Support:**
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- Assist in learning medical terminologies and understanding complex concepts.
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4. **Healthcare Applications:**
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- Integrate into clinical decision-support systems or patient care applications.
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---
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### **Usage Instructions:**
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1. **Setup:**
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Download all files and ensure compatibility with the Hugging Face Transformers library.
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2. **Loading the Model:**
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```python
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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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```
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3. **Generate Medical Text:**
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```python
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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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```
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4. **Customizing Outputs:**
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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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---
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### **Applications:**
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1. **Clinical Support:**
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- Assist healthcare providers with quick, accurate information retrieval.
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2. **Patient Education:**
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- Provide patients with understandable explanations of medical conditions.
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3. **Medical Research:**
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- Summarize or analyze complex medical research papers.
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4. **AI-Driven Diagnostics:**
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- Integrate with diagnostic systems for preliminary assessments.
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--- |
