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---
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
---
### 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:**
- **Base Model:** [Qwen2.5-7B-Instruct](#)
- **Dataset:** [avaliev/UMLS](#)
- Comprehensive dataset of medical terminologies, relationships, and use cases with 99.1k samples.
---
### **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:**
```python
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:**
```python
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.
--- |