medx-gemma4-e4b

A fine-tune of Gemma 4 E4B-it that reads one clinical note and returns the facts as a single flat JSON object. The schema goes in the system prompt: the default schema has 44 fields (demographics, vitals, social history, symptoms, history, medications, labs, assessment), and a deployer can rename fields, take a subset, add typed custom fields or ask for nulls without retraining. Notes may be in English, French, Kinyarwanda or a mix. The model answers directly, with thinking off.

Research model, not a medical device. It was trained on model-generated notes whose labels no clinician has reviewed. Do not use its output for clinical decisions without human verification.

Files

File	What it is
medx-gemma4-e4b.Q8_0.gguf	The shipped model, merged and quantized to Q8_0 for llama.cpp (8.0 GB)
medx-gemma4-e4b.BF16-mmproj.gguf	Vision projector exported with it; not needed for text-only extraction
adapters/run2_brand/	LoRA adapter (rank 32, alpha 32) for google/gemma-4-E4B-it: stage 1, 30,000 English rows with brand-name drug mentions
adapters/topup/	LoRA adapter (rank 32, alpha 32) for the merged stage-1 model: stage 2, 7,980 rows of French, Kinyarwanda, long English dictations and fresh English notes

To rebuild the model in Transformers: load google/gemma-4-E4B-it, apply adapters/run2_brand, merge it, then apply adapters/topup and merge again. The GGUF is the result of exactly that.

Usage (llama.cpp)

llama-server -m medx-gemma4-e4b.Q8_0.gguf -c 8192 --jinja \
  --chat-template-kwargs '{"enable_thinking": false}' --temp 0

Send the schema as the system message and the note as the user message. Thinking must stay off: the model was trained without reasoning and the chat template otherwise opens a thought channel. Use greedy decoding.

Evaluation

Micro-F1 over gold facts on six held-out sets (no note or schema in them was trained on). Real is 150 de-identified MTSamples transcriptions; the other five sets are model-generated. Labels come from model annotators with model adjudication.

Set	Notes	Micro-F1	Precision	Recall
Synthetic (English, default schema)	987	0.945	0.947	0.943
Hard cases (English)	250	0.939	0.942	0.936
Real transcriptions (English)	150	0.734	0.768	0.702
Custom held-out schemas (English)	1,000	0.950	0.948	0.952
French, held-out schemas	300	0.928	0.927	0.929
Kinyarwanda, held-out schemas	300	0.910	0.906	0.915

Every output on these sets was valid JSON. Scores are from greedy decoding of the bf16 weights; the Q8_0 GGUF was not scored separately.

Training

Unsloth 16-bit LoRA on all attention and MLP projections of the language layers, answer-only loss, 8-bit AdamW, cosine schedule, effective batch 32, context 4,096, one NVIDIA GB10 (DGX Spark). Stage 1: one epoch on 30,000 rows, learning rate 1e-4, with half of the generic drug mentions in each note rewritten as brand names. Stage 2: one epoch on 7,980 rows at 5e-5, starting from the merged stage-1 model.

Every training note was generated from a seeded specification and kept only if each gold fact had a verbatim quote in the note and every number was reproducible from it; a second model extracted the facts blind and a third adjudicated disagreements.

Limitations

• No clinician has reviewed the training or evaluation labels.
• Real-note accuracy (0.734) is far below accuracy on generated notes; the real set has only 150 English notes from one public source, and no real French or Kinyarwanda notes have been scored.
• The model can map brand names to the wrong generic drug.
• A larger successor (Qwen3.6-35B-A3B fine-tune) scores higher on every set above.