Instructions to use AXONVERTEX-AI-RESEARCH/Gemma3-270M-EU-Medical-CandidateSelector-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AXONVERTEX-AI-RESEARCH/Gemma3-270M-EU-Medical-CandidateSelector-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-3-270m-it") model = PeftModel.from_pretrained(base_model, "AXONVERTEX-AI-RESEARCH/Gemma3-270M-EU-Medical-CandidateSelector-LoRA") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use AXONVERTEX-AI-RESEARCH/Gemma3-270M-EU-Medical-CandidateSelector-LoRA with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for AXONVERTEX-AI-RESEARCH/Gemma3-270M-EU-Medical-CandidateSelector-LoRA to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for AXONVERTEX-AI-RESEARCH/Gemma3-270M-EU-Medical-CandidateSelector-LoRA to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for AXONVERTEX-AI-RESEARCH/Gemma3-270M-EU-Medical-CandidateSelector-LoRA to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="AXONVERTEX-AI-RESEARCH/Gemma3-270M-EU-Medical-CandidateSelector-LoRA", max_seq_length=2048, )
Gemma3-270M-EU-Medical-CandidateSelector-LoRA
LoRA adapter for unsloth/gemma-3-270m-it.
This model is a candidate-conditioned German/EU medical terminology selector.
It is not a diagnostic model. It must not be used for treatment, prescribing, dosage, triage, or clinical decision-making.
Intended role
Verifier retrieves ICD/EMA candidates โ 270M LoRA selects or flags ambiguity โ Verifier confirms selected candidate โ MedGemma explains verified facts.
Training
- Base model:
unsloth/gemma-3-270m-it - Dataset:
MediNorm-DE-EU-TeacherSelect-100K - Epochs: 2
- Steps: 2,624
- Effective batch size: 64
- Trainable parameters: 3,796,992 / 271,895,168
- Final validation loss: 0.001195
Full held-out test results
Evaluated on 8,311 rows:
- JSON parse rate: 0.9998
- Task exact rate: 1.0000
- Safety-scope exact rate: 1.0000
- ICD selected-code exact rate: 0.9956
- Selected-from-candidate-set rate: 1.0000
- Invalid out-of-candidate code rate: 0.0000
- ICD no-match null rate: 1.0000
- ICD ambiguity code exact rate: 0.9970
- ICD ambiguity terminal exact rate: 0.9985
- EMA medicine selection exact rate: 1.0000
- EMA active-substance exact rate: 0.9702
- EMA ATC exact rate: 0.9979
- Safety refusal rate: 1.0000
Trust boundary
Trusted for:
- valid JSON
- candidate selection from provided lists
- ambiguity/non-terminal flagging
- terminology-only safe outputs
Not trusted for:
- free-generation of ICD codes
- free-generation of EMA metadata
- diagnosis
- treatment advice
- dosage advice
Production rule
The selected code or medicine must be verified against deterministic SQLite/EMA tables and must be present in the provided candidate list.
The verifier should overwrite metadata fields from deterministic sources:
title_determinalchapteractive_substanceinn_or_common_nameatc_codemedicine_status
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