Instructions to use mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/Gemma-2-9B-PL-DevOps-Instruct-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 mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Gemma-2-9B-PL-DevOps-Instruct-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 mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Gemma-2-9B-PL-DevOps-Instruct-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/Gemma-2-9B-PL-DevOps-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/Gemma-2-9B-PL-DevOps-Instruct-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/Gemma-2-9B-PL-DevOps-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF with Ollama:
```
ollama run hf.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-2-9B-PL-DevOps-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

mradermacher commited on Feb 5

Commit

ac02185

verified ·

1 Parent(s): cc06621

auto-patch README.md

Browse files

Files changed (1) hide show

README.md +4 -0

README.md CHANGED Viewed

@@ -48,10 +48,14 @@ more details, including on how to concatenate multi-part files.
 | [GGUF](https://huggingface.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF/resolve/main/Gemma-2-9B-PL-DevOps-Instruct.Q3_K_S.gguf) | Q3_K_S | 4.4 |  |
 | [GGUF](https://huggingface.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF/resolve/main/Gemma-2-9B-PL-DevOps-Instruct.Q3_K_M.gguf) | Q3_K_M | 4.9 | lower quality |
 | [GGUF](https://huggingface.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF/resolve/main/Gemma-2-9B-PL-DevOps-Instruct.Q3_K_L.gguf) | Q3_K_L | 5.2 |  |
 | [GGUF](https://huggingface.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF/resolve/main/Gemma-2-9B-PL-DevOps-Instruct.Q4_K_S.gguf) | Q4_K_S | 5.6 | fast, recommended |
 | [GGUF](https://huggingface.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF/resolve/main/Gemma-2-9B-PL-DevOps-Instruct.Q4_K_M.gguf) | Q4_K_M | 5.9 | fast, recommended |
 | [GGUF](https://huggingface.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF/resolve/main/Gemma-2-9B-PL-DevOps-Instruct.Q6_K.gguf) | Q6_K | 7.7 | very good quality |
 | [GGUF](https://huggingface.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF/resolve/main/Gemma-2-9B-PL-DevOps-Instruct.Q8_0.gguf) | Q8_0 | 9.9 | fast, best quality |
 Here is a handy graph by ikawrakow comparing some lower-quality quant
 types (lower is better):

 | [GGUF](https://huggingface.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF/resolve/main/Gemma-2-9B-PL-DevOps-Instruct.Q3_K_S.gguf) | Q3_K_S | 4.4 |  |
 | [GGUF](https://huggingface.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF/resolve/main/Gemma-2-9B-PL-DevOps-Instruct.Q3_K_M.gguf) | Q3_K_M | 4.9 | lower quality |
 | [GGUF](https://huggingface.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF/resolve/main/Gemma-2-9B-PL-DevOps-Instruct.Q3_K_L.gguf) | Q3_K_L | 5.2 |  |
+| [GGUF](https://huggingface.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF/resolve/main/Gemma-2-9B-PL-DevOps-Instruct.IQ4_XS.gguf) | IQ4_XS | 5.3 |  |
 | [GGUF](https://huggingface.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF/resolve/main/Gemma-2-9B-PL-DevOps-Instruct.Q4_K_S.gguf) | Q4_K_S | 5.6 | fast, recommended |
 | [GGUF](https://huggingface.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF/resolve/main/Gemma-2-9B-PL-DevOps-Instruct.Q4_K_M.gguf) | Q4_K_M | 5.9 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF/resolve/main/Gemma-2-9B-PL-DevOps-Instruct.Q5_K_S.gguf) | Q5_K_S | 6.6 |  |
+| [GGUF](https://huggingface.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF/resolve/main/Gemma-2-9B-PL-DevOps-Instruct.Q5_K_M.gguf) | Q5_K_M | 6.7 |  |
 | [GGUF](https://huggingface.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF/resolve/main/Gemma-2-9B-PL-DevOps-Instruct.Q6_K.gguf) | Q6_K | 7.7 | very good quality |
 | [GGUF](https://huggingface.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF/resolve/main/Gemma-2-9B-PL-DevOps-Instruct.Q8_0.gguf) | Q8_0 | 9.9 | fast, best quality |
+| [GGUF](https://huggingface.co/mradermacher/Gemma-2-9B-PL-DevOps-Instruct-GGUF/resolve/main/Gemma-2-9B-PL-DevOps-Instruct.f16.gguf) | f16 | 18.6 | 16 bpw, overkill |
 Here is a handy graph by ikawrakow comparing some lower-quality quant
 types (lower is better):