metadata
language: en
license: apache-2.0
tags:
- medical
- soap-notes
- mistral
- lora
- fine-tuned
🏥 Mistral 7B Fine-Tuned for SOAP Note Generation
This model is a fine-tuned version of Mistral-7B-v0.1 specialized for generating clinical SOAP notes from doctor-patient conversations.
Model Details
- Base Model: mistralai/Mistral-7B-v0.1
- Fine-tuning Method: LoRA (Low-Rank Adaptation)
- LoRA Rank: 16
- Training: 25 minutes on Google Colab T4 GPU
- Output Format: Structured JSON with Subjective, Objective, Assessment, Plan sections
Evaluation Results (Groq Llama-3.3-70B Judge)
| Metric | Score |
|---|---|
| Answer Relevancy | 0.86 |
| Contextual Precision | 0.60 |
| Contextual Recall | 0.66 |
| Contextual Relevancy | 0.78 |
| Faithfulness | 0.70 |
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model = AutoModelForCausalLM.from_pretrained(
"SaberaBanu/mistral-soap-notes",
torch_dtype=torch.float16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("SaberaBanu/mistral-soap-notes")
PROMPT = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
Generate a SOAP note from the clinical conversation. Output MUST be a valid JSON object.
### Input:
{conversation}
### Response:
"""
inputs = tokenizer(PROMPT.format(conversation=your_conversation), return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=600, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Limitations
- Objective section may hallucinate vitals not mentioned in conversation
- Works best with clearly structured doctor-patient dialogues
- Not intended for real clinical use without human review