Base model: mlx-community/gemma-2-2b-it-4bit
Fine-tuning method: LoRA
Runtime: mlx-lm
Training data: synthetic but realistic Swedish primary-care .eir timelines and supervised answers
Current synthetic dataset size: 31 train / 5 valid / 6 test

This repository contains the fused model weights, so it can be loaded directly by MLX clients without a separate adapter step.

Evaluation snapshot

Held-out synthetic test split:

Base model: token_f1=0.2351, citation_f1=0.0000
Fine-tuned model: token_f1=0.2874, citation_f1=0.3333

These numbers are small-sample and synthetic-only, but they were enough to confirm the fine-tune learned task-specific citation and note-explanation behavior.

Use with MLX

python

from mlx_lm import load, generate

model, tokenizer = load("birgermoell/gemma-2-2b-it-4bit-eir-swedish")

messages = [
    {
        "role": "user",
        "content": "Förklara journalanteckningen på enkel svenska och säg vad som verkar viktigast att följa upp."
    }
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
response = generate(model, tokenizer, prompt=prompt, max_tokens=256, verbose=False)
print(response)

Use in the Eir app

In Eir Settings:

Open Settings
Go to the local models section
Choose Add Model...
Enter birgermoell/gemma-2-2b-it-4bit-eir-swedish

The app can then download and load this model as an on-device MLX model.

Limitations

This is an early research model, not a medical device
Training data is currently synthetic, not real de-identified patient data
It should be used for explanation and support, not diagnosis
Outputs still need stronger factuality and safety evaluation before real deployment