clinical-info-corrector-1B

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🩺 clinical-info-corrector-1B

Finetuned Llama 3.2 1B Instruct model for cleaning clinical text (histories, notes & staff remarks)

<img src="https://huggingface.co/alphaaico/clinical-info-corrector-1B/resolve/main/clinical-info-corrector-1B.png" alt="clinical-info-corrector-1B" style="width: 500px; height: auto; object-position: center top;">

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Model Card

• Model name: clinical-info-corrector-1B
• Base model: unsloth/Llama-3.2-1B-Instruct
• Finetuning framework: Unsloth + TRL SFTTrainer
• Task: Clinical text cleaning & rephrasing (not diagnosis)
• Status: Experimental — trained on a relatively small, noisy custom dataset

This model is designed to take messy clinical content (patient histories, discharge notes, staff remarks, “adviced” sections, etc.) and rewrite it into clear, grammatically correct, medically faithful text while preserving the underlying facts.

It is not a diagnostic or decision-support model. Think of it as a domain-aware “clinical copy-editor” for structured text.

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Motivation & Intended Use

Many hospital information systems accumulate:

• telegraphic note fragments
• mixed-case, ALL-CAPS, abbreviations everywhere
• repeated or contradictory phrases
• encoding artefacts (e.g., Â, broken bullets, etc.)

The goal of clinical-info-corrector-1B is to explore whether even a small 1B parameter model, fine-tuned on a crude but realistic dataset, can:

• clean up this text,
• normalize style and grammar,
• expand common medical abbreviations where helpful, and
• keep the clinical meaning intact.

Primary intended uses:

• Pre-processing / normalization of:
  • patient histories
  • discharge summaries
  • clinician notes / staff remarks
  • “advice” / follow-up instructions
• Creating cleaner text for:
  • analytics / NLP pipelines
  • downstream LLMs (e.g., for summarization or coding)
  • internal documentation

Not intended for:

• Medical diagnosis or triage
• Treatment recommendations
• Patient-facing communication without clinician review

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Training Data & Format

The model was finetuned on a custom clinical dataset derived from hospital discharge summaries and related fields. Each training sample is a pair:

• content – noisy / raw clinical text (e.g. original HISTORY, treatment, adviced, mdescript1)
• response – cleaned version of that text (grammar fixed, abbreviations expanded, structure improved, artefacts removed)

Example pair (simplified):

content:
PATIENT ADMITTED WITH ABOVE SAID COMPLAINT FOR FURTHER EVALUATION AND MANAGEMENT.  -H/O LAP CHOLECYSTECTOMY(12 YEAR BACK

response:
The patient was admitted with the above-mentioned complaints for further evaluation and management.
The patient has a history of laparoscopic cholecystectomy performed 12 years ago.

During finetuning, each row was wrapped as a chat-style conversation:

[
  {"role": "system", "content": "<system prompt (see below)>"},
  {"role": "user",   "content": "<content>"},
  {"role": "assistant", "content": "<response>"}
]

The Llama 3.x chat template (llama-3.1 in Unsloth) was used to convert this into the final training text.
Training used train_on_responses_only, so only the assistant segment (response) contributed to the loss.

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System Prompt (Baked-in Behavior)

During training, a system message of this form was used:

You are a medical AI assistant that rewrites noisy, telegraphic, or poorly formatted clinical text
(eg, discharge summaries, admission notes, advice sections) into clear, grammatically correct,
contextually rich prose.

Given some raw 'content' from a clinician, you must:
- preserve all medically relevant facts and timelines,
- avoid inventing new clinical information,
- expand or clarify abbreviations when appropriate,
- fix spelling, grammar, punctuation, and formatting,
- turn lists or fragments into coherent sentences and paragraphs.

Your reply should be only the cleaned clinical text, nothing else.

For best results, you should re-use a similar system prompt at inference time.

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How to Use

1. Basic Chat-style Inference (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "your-username/clinical-info-corrector-1B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype = torch.float16,
    device_map = "auto",
)

SYSTEM_PROMPT = """
You are a medical AI assistant that rewrites noisy, telegraphic, or poorly formatted clinical text
into clear, grammatically correct, medically faithful prose. Preserve all medical facts and do not
invent new information. Your reply should be only the cleaned clinical text.
"""

raw_content = "PATIENT ADMITTED WITH ABOVE SAID COMPLAINT FOR FURTHER EVALUATION AND MANAGEMENT. -H/O LAP CHOLECYSTECTOMY(12 YEAR BACK"

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user",   "content": raw_content},
]

from unsloth.chat_templates import get_chat_template
tokenizer = get_chat_template(tokenizer, chat_template = "llama-3.1")

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize = True,
    add_generation_prompt = True,
    return_tensors = "pt",
).to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens = 256,
    temperature = 0.7,
    top_p = 0.9,
)

print(tokenizer.decode(outputs[0], skip_special_tokens = True))

2. Recommended Prompt Pattern

User message:

“Please clean and normalize the following clinical text. Preserve all clinical facts and do not add anything new:
<raw content here>”

System message: same as above, or a shorter variant:

“You are a clinical text cleaning assistant. Rewrite the input into clear, grammatically correct, medically faithful prose, preserving all facts.”

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Example

Input (user):

DAILY FLUID INTAKE 1.5LTR/DAY  Check CBC, RFT,K+, RBS,CXR, ECG, in follow up  Chest physiotherapy, wound hygiene & proper diet.  Steam Inhalation three times a day.  Syp Alex or TusQDx 2 spoons threee times a day if dry cough is present  FBS AND PPBS AFTER 3DAYS IF BLOOD SUGAR IS <80 OR >200mg/dl THEN CONSULT to local physician.  WHILE SPLEEPING MUST LIE SUPINE FOR ONE MONTH, CAN REMOVE BELT IN NIGHT ONLY. 

Output (assistant):

Maintain a daily fluid intake of about 1.5 L per day. At the follow-up visit, get a complete blood count (CBC), renal function tests (RFT), serum potassium (K+), random blood sugar (RBS), chest X-ray (CXR), and electrocardiogram (ECG) done. Continue chest physiotherapy, maintain good wound hygiene, and follow a nutritious diet. Do steam inhalation three times daily. If you have a dry cough, take syrup Alex or TusQDx 2 spoonfuls three times daily. Check fasting and post-meal blood sugar after 3 days; if it is less than 80 mg/dL or more than 200 mg/dL, consult your local physician. While sleeping for the next month, lie on your back (supine), and you may remove the belt only at night.

(Output is illustrative of the intended style; exact generations may vary.)

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Training Details

• Base model: unsloth/Llama-3.2-1B-Instruct
• Method: Supervised fine-tuning (SFT)
• Library: Unsloth + TRL SFTTrainer
• Loss masking: train_on_responses_only (only assistant outputs)
• Context length: 2048
• Objective: Minimize loss on cleaned response given content (and system + user context)

Because the dataset is relatively small and noisy, this model should be considered experimental and evaluated carefully before integration into production pipelines.

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Suggested Hyperparameters (Inference)

You can tune to your own taste, but a reasonable starting point:

• Temperature: 0.3 – 0.7
• Top-p: 0.9
• Max new tokens: 256–512
• Repetition penalty: optional (1.0–1.1)

Lower temperatures help keep the model closer to “copy-editor” mode rather than creative rewriting.

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Limitations & Risks

• Not a medical device:
  This model does not replace clinical judgment, and should not be used for diagnosis, triage, or direct treatment recommendations.

• Data coverage:
  Trained on a specific institution’s style and a limited range of note types. May not generalize to all clinical documentation formats.

• Factual hallucination:
  While the training objective and system prompt encourage fidelity to the source, the model can still drop or rephrase details incorrectly. A human clinician must review outputs.

• Biases:
  Any biases or idiosyncrasies present in the source data may be reflected in the cleaned text.

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License

The license: other field is a placeholder.
Before public or commercial use, please set the correct license for this model and its training data in accordance with your institutional and legal requirements.

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Acknowledgments

• Unsloth for efficient finetuning on small hardware.
• The maintainers of Llama 3.x models for providing strong base models.
• Clinical staff and data teams whose documentation (anonymized and processed) made it possible to experiment with this type of text-cleaning model.

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Disclaimer

This model is intended solely for research and internal tooling around text normalization of clinical documentation. It must not be used to:

• make or suggest diagnoses,
• decide treatment options,
• communicate directly with patients, or
• operate without human clinical oversight.

Always have qualified healthcare professionals review and validate outputs before any clinical use.