Instructions to use mradermacher/Medra27B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/Medra27B-GGUF with Transformers:

# Use a pipeline as a high-level helper
# Warning: Pipeline type "summarization" is no longer supported in transformers v5.
# You must load the model directly (see below) or downgrade to v4.x with:
# 'pip install "transformers<5.0.0'
from transformers import pipeline

pipe = pipeline("summarization", model="mradermacher/Medra27B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/Medra27B-GGUF", dtype="auto")

llama-cpp-python

How to use mradermacher/Medra27B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mradermacher/Medra27B-GGUF",
	filename="Medra27B.IQ4_XS.gguf",
)

llm.create_chat_completion(
	messages = "\"The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.\""
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use mradermacher/Medra27B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mradermacher/Medra27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/Medra27B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mradermacher/Medra27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/Medra27B-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 mradermacher/Medra27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/Medra27B-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 mradermacher/Medra27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/Medra27B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/Medra27B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/Medra27B-GGUF with Ollama:
```
ollama run hf.co/mradermacher/Medra27B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use mradermacher/Medra27B-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 mradermacher/Medra27B-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 mradermacher/Medra27B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/Medra27B-GGUF to start chatting

Docker Model Runner
How to use mradermacher/Medra27B-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/Medra27B-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/Medra27B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/Medra27B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Medra27B-GGUF-Q4_K_M

List all available models

lemonade list

About

static quants of https://huggingface.co/nicoboss/Medra27B

For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Medra27B-i1-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
GGUF	mmproj-Q8_0	0.7	multi-modal supplement
GGUF	mmproj-f16	1.0	multi-modal supplement
GGUF	Q2_K	10.6
GGUF	Q3_K_S	12.3
GGUF	Q3_K_M	13.5	lower quality
GGUF	Q3_K_L	14.6
GGUF	IQ4_XS	15.0
GGUF	Q4_K_S	15.8	fast, recommended
GGUF	Q4_K_M	16.6	fast, recommended
GGUF	Q5_K_S	18.9
GGUF	Q5_K_M	19.4
GGUF	Q6_K	22.3	very good quality
GGUF	Q8_0	28.8	fast, best quality

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

Downloads last month: 146

GGUF

Model size

27B params

Architecture

gemma3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for mradermacher/Medra27B-GGUF

Base model

google/gemma-3-27b-pt

Finetuned

google/gemma-3-27b-it

Adapter

nicoboss/Medra27B

Quantized

(2)

this model

mradermacher
/

Medra27B-GGUF