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air5978/
clinical-tinyllama

Name: clinical-tinyllama
Availability: InStock

transformers

safetensors

endpoints_compatible

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library_name: transformers license: apache-2.0 tags:

llm
causal-lm
tinyllama
clinical
summarization
peft
lora
healthcare
instruction-tuned base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 pipeline_tag: text-generation datasets:
geekdom/clinical_data

Clinical TinyLlama 🏥🧠

Model Description

This model is a fine-tuned version of TinyLlama-1.1B-Chat-v1.0 trained on a synthetic clinical dataset for clinical note summarization.

It takes structured hospital notes (ICU admission, treatment, discharge condition) and generates concise medical summaries.

The model was fine-tuned using:

LoRA (Low-Rank Adaptation)
Hugging Face Trainer
Clinical note → summary pairs

Model Type

Architecture: Decoder-only Transformer (LLaMA-style)
Base Model: TinyLlama 1.1B Chat
Fine-tuning Method: LoRA (PEFT)

Intended Use

Direct Use

Clinical note summarization
Medical report compression
Educational NLP demos

Downstream Use

Healthcare documentation assistants
EHR summarization tools
Clinical NLP research

Out-of-Scope Use

Medical diagnosis
Real patient decision-making
Legal/clinical decision support

Training Data

Dataset: geekdom/clinical_data
Format:
- Input: Clinical hospital notes
- Output: Human-written summaries

Training Details

Framework: Hugging Face Transformers + TRL + PEFT
Method: Supervised Fine-Tuning (SFT)
Parameter-Efficient Fine-Tuning: LoRA
Sequence length: 256 tokens
Batch size: 1 (gradient accumulation used)
Hardware: CPU / Apple MPS (depending on setup)

Limitations

May generate overly verbose or slightly redundant summaries
Not medically validated
Performance depends heavily on prompt quality
Not suitable for real clinical deployment

How to Use

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "air5978/clinical-tinyllama"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = """Summarize the following clinical note:
Hospital Course: ...
Discharge Condition: ..."""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=120)

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