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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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- en |
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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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