License notice: This is a quantized derivative of google/medgemma-1.5-4b-it and is governed by the Gemma Terms of Use. You must accept those terms on the original model page before downloading or using this file. All credit for the base model goes to Google.

Quantized by Mohammed K. A. Abed as part of the MedGemma Impact Challenge — optimised for on-device clinical inference on mobile and CPU-only hardware.


Base model	`google/medgemma-1.5-4b-it` (4B parameters)
Original size	7.3 GB (BF16)
Quantized size	2.0 GB
Reduction	73%
Method	GGUF Q3_K_M (3-bit k-means quantization via llama.cpp)
Runtime	llama.cpp / llama.rn
Tested on	Tecno Spark 40 · MediaTek Helio G100 · 8 GB RAM

About this quantization

Q3_K_M uses 3-bit k-means quantization with medium-sized super-blocks. Compared to lower bit-widths:

Variant	Bits	Size	Notes
Q4_K_M	4.83	2.4 GB	Higher quality — recommended for workstation use
Q3_K_M	3.07	2.0 GB	Best fit for 8 GB mobile RAM — used in Capsule
Q2_K	2.96	1.5 GB	Perplexity penalty too high (+3.5 PPL)
IQ2_M	2.7	1.3 GB	Too slow on mobile CPU

Q3_K_M was selected after benchmarking on a mid-range Android phone (Tecno Spark 40, MediaTek Helio G100). It is the highest quality variant that fits within the phone's working RAM budget after the OS and app overhead are accounted for.

Files

File	Description
`medgemma-1.5-4b-it-Q3_K_M.gguf`	Q3_K_M quantized GGUF weights

Usage

llama.cpp CLI

bash

./llama-cli \
    -m medgemma-1.5-4b-it-Q3_K_M.gguf \
    -n 512 \
    --ctx-size 4096 \
    --temp 0.3 \
    --repeat-penalty 1.1 \
    -p "<start_of_turn>user\nGenerate a SOAP note for the following transcript:\n\n{transcript}<end_of_turn>\n<start_of_turn>model\n"

llama.cpp server (workstation)

bash

./llama-server \
    -m medgemma-1.5-4b-it-Q3_K_M.gguf \
    --host 0.0.0.0 \
    --port 8080 \
    --ctx-size 4096 \
    --n-predict 512

Python (llama-cpp-python)

python

from llama_cpp import Llama

llm = Llama(
    model_path="medgemma-1.5-4b-it-Q3_K_M.gguf",
    n_ctx=4096,
    n_threads=8,
)

prompt = (
    "<start_of_turn>user\n"
    "Generate a SOAP note for the following transcript:\n\n"
    "{transcript}"
    "<end_of_turn>\n"
    "<start_of_turn>model\n"
)

output = llm(
    prompt,
    max_tokens=512,
    temperature=0.3,
    repeat_penalty=1.1,
    stop=["<end_of_turn>"],
)
print(output["choices"][0]["text"])

React Native / Android (llama.rn)

This is how the model is used inside Capsule:

typescript

import { initLlama, LlamaContext } from 'llama.rn';

const context: LlamaContext = await initLlama({
  model: '/data/local/tmp/medgemma.gguf',
  n_ctx: 4096,
  n_threads: 4,
});

const result = await context.completion({
  prompt: `<start_of_turn>user\n${systemPrompt}\n\n${transcript}<end_of_turn>\n<start_of_turn>model\n`,
  n_predict: 512,
  temperature: 0.3,
  repeat_penalty: 1.1,
  stop: ['<end_of_turn>'],
});

See App.tsx for the full reference implementation including memory management (sequential loading with MedASR).

Clinical tasks demonstrated

Task	Where
SOAP note generation from dictation transcript	On-device (phone)
Lab result summarisation and interpretation	On-device (phone)
Clinical chat with voice input	On-device (phone)
Agentic SOAP enhancement (DDI · ICD-10 · lab correlation)	Workstation (Q4_K_M)
EHR Navigator — natural-language FHIR queries	Workstation (Q4_K_M)
Radiology report generation (8 imaging modalities)	Workstation + mmproj

Performance

Measured on Tecno Spark 40 (MediaTek Helio G100, 8 GB RAM, CPU-only):

Metric	Value
SOAP note generation	~60 s
Peak memory during inference	~3.2 GB
Battery impact	< 3% per hour of active use
Model load time	~8 s (pre-loaded during transcript review)

No GPU required. Also tested on Ryzen 7 8845HS (32 GB RAM) where the Q4_K_M variant is used for the workstation pipeline.

Quantization command

bash

# Convert base model weights to GGUF (run inside llama.cpp repo)
python convert_hf_to_gguf.py \
    /path/to/medgemma-1.5-4b-it \
    --outfile medgemma-1.5-4b-it-f16.gguf \
    --outtype f16

# Quantize to Q3_K_M
./llama-quantize \
    medgemma-1.5-4b-it-f16.gguf \
    medgemma-1.5-4b-it-Q3_K_M.gguf \
    Q3_K_M

Memory layout on device

MedASR (101 MB ONNX) and MedGemma (2.0 GB GGUF) are loaded sequentially — never simultaneously — to stay within 8 GB RAM:

Record dictation  →  Load MedASR (101 MB)  →  Transcribe  →  Unload MedASR
                  →  Pre-load MedGemma during transcript review (2.0 GB)
                  →  Generate SOAP note  →  Unload MedGemma (on demand)

Total on-device footprint: 2.1 GB active at any one time.

License

This model is derived from google/medgemma-1.5-4b-it and inherits the Gemma Terms of Use. You must accept those terms before downloading or using this model.

Citation

If you use this model, please cite the original MedGemma work and acknowledge the Capsule project:

@misc{capsule2026,
  title  = {Capsule: Edge AI Clinical Documentation with Agentic Intelligence},
  author = {Mohammed K. A. Abed},
  year   = {2026},
  url    = {https://github.com/mo-saif/capsule}
}

moisf56/medgemma-4b-q3km-gguf

MedGemma 4B — Q3_K_M GGUF (2.0 GB)

About this quantization

Files

Usage

llama.cpp CLI

llama.cpp server (workstation)

Python (llama-cpp-python)

React Native / Android (llama.rn)

Clinical tasks demonstrated

Performance

Quantization command

Memory layout on device

License

Citation

moisf56/
medgemma-4b-q3km-gguf