Instructions to use lmstudio-community/gemma-2-27b-it-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lmstudio-community/gemma-2-27b-it-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="lmstudio-community/gemma-2-27b-it-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("lmstudio-community/gemma-2-27b-it-GGUF", dtype="auto")

llama-cpp-python

How to use lmstudio-community/gemma-2-27b-it-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lmstudio-community/gemma-2-27b-it-GGUF",
	filename="gemma-2-27b-it-IQ3_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use lmstudio-community/gemma-2-27b-it-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lmstudio-community/gemma-2-27b-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf lmstudio-community/gemma-2-27b-it-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 lmstudio-community/gemma-2-27b-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf lmstudio-community/gemma-2-27b-it-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 lmstudio-community/gemma-2-27b-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf lmstudio-community/gemma-2-27b-it-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 lmstudio-community/gemma-2-27b-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lmstudio-community/gemma-2-27b-it-GGUF:Q4_K_M

Use Docker

docker model run hf.co/lmstudio-community/gemma-2-27b-it-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use lmstudio-community/gemma-2-27b-it-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lmstudio-community/gemma-2-27b-it-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lmstudio-community/gemma-2-27b-it-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lmstudio-community/gemma-2-27b-it-GGUF:Q4_K_M

SGLang

How to use lmstudio-community/gemma-2-27b-it-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "lmstudio-community/gemma-2-27b-it-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lmstudio-community/gemma-2-27b-it-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "lmstudio-community/gemma-2-27b-it-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lmstudio-community/gemma-2-27b-it-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use lmstudio-community/gemma-2-27b-it-GGUF with Ollama:
```
ollama run hf.co/lmstudio-community/gemma-2-27b-it-GGUF:Q4_K_M
```

Unsloth Studio new

How to use lmstudio-community/gemma-2-27b-it-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 lmstudio-community/gemma-2-27b-it-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 lmstudio-community/gemma-2-27b-it-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lmstudio-community/gemma-2-27b-it-GGUF to start chatting

Docker Model Runner
How to use lmstudio-community/gemma-2-27b-it-GGUF with Docker Model Runner:
```
docker model run hf.co/lmstudio-community/gemma-2-27b-it-GGUF:Q4_K_M
```

Lemonade

How to use lmstudio-community/gemma-2-27b-it-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lmstudio-community/gemma-2-27b-it-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-2-27b-it-GGUF-Q4_K_M

List all available models

lemonade list

💫 Community Model> Gemma 2 27b Instruct by Google

👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord.

Model creator: Google
Original model: gemma-2-27b-it
GGUF quantization: provided by bartowski based on llama.cpp release b3259

Model Settings:

Requires LM Studio 0.2.27, update can be downloaded from here: https://lmstudio.ai

Model Summary:

Gemma 2 instruct is a a brand new model from Google in the Gemma family based on the technology from Gemini. Trained on a combination of web documents, code, and mathematics, this model should excel at anything you throw at it.
With 27B parameters, this fills in a really great gap between the typical ~8B and 70B models, and should be great for anyone with moderate VRAM availability.

Prompt Template:

Choose the 'Google Gemma Instruct' preset in your LM Studio.

Under the hood, the model will see a prompt that's formatted like so:

<start_of_turn>user
{prompt}<end_of_turn>
<start_of_turn>model

Note that this model does not support a System prompt.

Technical Details

Gemma 2 features the same extremely large vocabulary from release 1.1, which tends to help with multilingual and coding proficiency.

Gemma 2 27B was trained on a wide dataset of 13 trillion tokens, more than twice as many as Gemma 1.1, and an extra 60% over the 9B model, using similar datasets including:

Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. Primarily English-language content.
Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code or understand code-related questions.
Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries.

For more details check out their blog post here: https://huggingface.co/blog/gemma2

Special thanks

🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.

🙏 Special thanks to Kalomaze and Dampf for their work on the dataset (linked here) that was used for calculating the imatrix for all sizes.

Disclaimers

LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.

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GGUF

Model size

27B params

Architecture

gemma2

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for lmstudio-community/gemma-2-27b-it-GGUF

Base model

google/gemma-2-27b

Finetuned

google/gemma-2-27b-it

Quantized

(62)

this model