SaberaBanu/mistral-soap-notes

🏥 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