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Model: prithivMLmods/Qwen-UMLS-7B-Instruct Source: Original Platform
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README.md
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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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---
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config.json
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"_name_or_path": "unsloth/qwen2.5-7b-instruct-bnb-4bit",
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"Qwen2ForCausalLM"
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31
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Normal file
31
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Normal file
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3
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216
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Normal file
216
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Normal file
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|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %} {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|PAD_TOKEN|>",
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user