Instructions to use 2kfi/medgemma-4B-it-fine-tuned-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 2kfi/medgemma-4B-it-fine-tuned-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="2kfi/medgemma-4B-it-fine-tuned-gguf",
	filename="MedGemma-3b-it-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use 2kfi/medgemma-4B-it-fine-tuned-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf 2kfi/medgemma-4B-it-fine-tuned-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 2kfi/medgemma-4B-it-fine-tuned-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf 2kfi/medgemma-4B-it-fine-tuned-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 2kfi/medgemma-4B-it-fine-tuned-gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf 2kfi/medgemma-4B-it-fine-tuned-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf 2kfi/medgemma-4B-it-fine-tuned-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf 2kfi/medgemma-4B-it-fine-tuned-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 2kfi/medgemma-4B-it-fine-tuned-gguf:Q4_K_M

Use Docker

docker model run hf.co/2kfi/medgemma-4B-it-fine-tuned-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use 2kfi/medgemma-4B-it-fine-tuned-gguf with Ollama:
```
ollama run hf.co/2kfi/medgemma-4B-it-fine-tuned-gguf:Q4_K_M
```

Unsloth Studio

How to use 2kfi/medgemma-4B-it-fine-tuned-gguf 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 2kfi/medgemma-4B-it-fine-tuned-gguf 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 2kfi/medgemma-4B-it-fine-tuned-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for 2kfi/medgemma-4B-it-fine-tuned-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use 2kfi/medgemma-4B-it-fine-tuned-gguf with Docker Model Runner:
```
docker model run hf.co/2kfi/medgemma-4B-it-fine-tuned-gguf:Q4_K_M
```

Lemonade

How to use 2kfi/medgemma-4B-it-fine-tuned-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 2kfi/medgemma-4B-it-fine-tuned-gguf:Q4_K_M

Run and chat with the model

lemonade run user.medgemma-4B-it-fine-tuned-gguf-Q4_K_M

List all available models

lemonade list

Model Details

Model Description

Developed by: [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

Uses

Direct Use

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Downloads last month: 118

GGUF

Model size

4B params

Architecture

gemma3

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Inference Providers NEW

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Model tree for 2kfi/medgemma-4B-it-fine-tuned-gguf

Base model

google/gemma-3-4b-pt

Finetuned

google/medgemma-4b-pt

Finetuned

google/medgemma-4b-it

Finetuned

unsloth/medgemma-4b-it

Quantized

(3)

this model

2kfi
/

medgemma-4B-it-fine-tuned-gguf

Model Details

Model Description

Model Sources [optional]

Uses

Direct Use

Out-of-Scope Use

Bias, Risks, and Limitations

Recommendations

Training Details

Training Data

Training Procedure

Preprocessing [optional]

Training Hyperparameters

Speeds, Sizes, Times [optional]

Evaluation

Testing Data, Factors & Metrics

Metrics

Results

Summary

Model tree for 2kfi/medgemma-4B-it-fine-tuned-gguf

Datasets used to train 2kfi/medgemma-4B-it-fine-tuned-gguf