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--- |
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library_name: adapter-transformers |
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license: mit |
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datasets: |
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- FreedomIntelligence/medical-o1-reasoning-SFT |
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language: |
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- en |
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--- |
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# Model Card: Imran1/Med-R1-v1 |
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## Model Details |
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- **Name**: Imran1/Med-R1-v1 |
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- **Size**: 1B parameters |
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- **Dataset**: `FreedomIntelligence/medical-o1-reasoning-SFT` |
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- **Max Sequence Length**: 2048 tokens |
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- **Quantization**: Supports 4-bit inference |
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## Description |
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Imran1/Med-R1-v1 is a fine-tuned language model for medical reasoning, trained on structured medical datasets to provide contextually relevant and accurate responses for healthcare-related queries. |
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## Intended Use |
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- **Use Cases**: Medical Q&A, reasoning for diagnoses, and treatment suggestions |
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- **Users**: Healthcare professionals, researchers, and developers |
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## Example |
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```python |
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from unsloth import FastLanguageModel |
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from transformers import TextStreamer |
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model_name = "Imran1/Med-R1-v1" |
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max_seq_length = 2048 |
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model, tokenizer = FastLanguageModel.from_pretrained( |
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model_name=model_name, |
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max_seq_length=max_seq_length, |
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dtype=None, |
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load_in_4bit=True, |
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) |
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FastLanguageModel.for_inference(model) # Enable native 2x faster inference |
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prompt = "i have pain in my kidney, what should i need to do?" |
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messages = [ |
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{"role": "system", "content": "You are AI medical assistant."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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text_streamer = TextStreamer(tokenizer) |
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generated_ids = model.generate( |
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**inputs, |
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streamer=text_streamer, |
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max_new_tokens=2000 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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``` |
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## output |
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``` |
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<｜begin▁of▁sentence｜>You are AI medical assistant.<｜User｜>i have pain in my kidney, what should i need to do?<｜Assistant｜><|thinking|> Okay, let's think about this. When we have pain in the kidney, it's definitely something to take seriously. The kidneys are the main filters for blood, so there's definitely a risk of kidney issues. |
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First, I should consider what the pain might mean. Could it be a urinary tract infection? That's a big possibility, but it could also be something like a kidney stones or a urinary tract blockage. And what about the rest of the body? Could there be a urinary tract infection or some kind of renal or urinary blockage? |
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Now, I should think about what to do next. If it's a urinary tract infection, I'd want to go for an emergency treatment. This could mean antibiotics or maybe even surgery if it's severe. But if it's not severe, maybe a simple follow-up with a urinalysis could help. |
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If it's not a kidney blockage, but there's pain, it might mean a urinary tract issue, like a kidney stones. In this case, I'd need to go for a urinalysis to see what's going on. |
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Also, I should think about other potential issues. Like, could it be something like a renal vein or glomerulonephritis? Those would have different treatment plans. |
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Hmm, let's not forget about the possibility of something like a kidney transplant. If that's the case, it's important to make sure the patient is healthy and there's no other risk to consider. |
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So, in the end, the best next step is to check for what's causing the pain. If it's something like a kidney stone, a urinalysis is the way to go. But if it's a urinary tract infection, I'd need to get it treated fast. |
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Alright, let's wrap it up. If it's a urinary tract infection, I need to act quickly. If it's a kidney stone, I'll need to do a urinalysis. And just in case, a kidney transplant is something to think about, but only if it seems like the best option given the situation. <|thinking_end|> |
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<|Final|> When you experience pain in your kidney, it's important to approach the situation with a clear mind and consider the possible causes of the pain. Here's a structured approach to what you should do next: |
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1. **Assess the Pain Type**: Determine if the pain is likely due to a urinary tract infection (UTI), a kidney stone, or some other condition. This will guide the treatment approach. |
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2. **If Urethral Infection**: |
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- If the pain is severe and worsens with exertion, it's likely a urinary tract infection. In this case, you should: |
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- **Conduct a Urinalysis**: This will help identify any stones, infections, or other kidney issues. |
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- **Immediate Treatment**: Depending on the severity, you may need antibiotics, surgery, or more invasive treatment like a kidney transplant if the infection is severe and advanced. |
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3. **If Kidney Stones**: |
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- If pain is noticeable but not severe, it might indicate a kidney stone. In this situation, a urinalysis is recommended to assess the stone's size and shape. |
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4. **Other Considerations**: |
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- **Renal Vein**: If the pain is in the urinary tract and there's no clear diagnosis of a kidney stone or infection, consider a renal vein. This may involve a lumbar puncture to check for blood flow and assess the condition. |
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5. **Other Potential Conditions**: |
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- **Renal Transplant**: If you suspect renal vein involvement, a renal transplant could be considered, but only if there's no other risk or evidence of another renal issue. |
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By following this structured approach, you can manage the pain effectively and take appropriate steps to address the underlying cause. <|Final_end|><｜end▁of▁sentence｜> |
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``` |
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## Limitations |
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- **Not a substitute for medical advice**: This model provides general reasoning and suggestions but should not replace professional medical consultation. |
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- **Dataset Bias**: The model’s outputs are influenced by the `FreedomIntelligence/medical-o1-reasoning-SFT` dataset and may reflect its limitations. |
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- **Sensitive Use**: Ensure appropriate use in non-critical scenarios and always verify with certified professionals. |
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## Deployment |
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The model supports efficient deployment with 4-bit quantization for inference. It is optimized for use in applications requiring medical reasoning and is compatible with Hugging Face and `unsloth` frameworks. |
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## Citation |
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If you use this model, please cite: |
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```bibtex |
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@model{imran1_med_r1_v1, |
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title={Imran1/Med-R1-v1: A Medical Reasoning Language Model}, |
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author={Imran1}, |
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year={2025}, |
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url={https://huggingface.co/Imran1/Med-R1-v1} |
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} |
