|
--- |
|
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. |
|
|
|
--- |
