Instructions to use mradermacher/QiMing-Gemma-3-4b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/QiMing-Gemma-3-4b-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/QiMing-Gemma-3-4b-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/QiMing-Gemma-3-4b-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/QiMing-Gemma-3-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/QiMing-Gemma-3-4b-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/QiMing-Gemma-3-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/QiMing-Gemma-3-4b-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/QiMing-Gemma-3-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/QiMing-Gemma-3-4b-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/QiMing-Gemma-3-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/QiMing-Gemma-3-4b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/QiMing-Gemma-3-4b-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/QiMing-Gemma-3-4b-GGUF with Ollama:
```
ollama run hf.co/mradermacher/QiMing-Gemma-3-4b-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use mradermacher/QiMing-Gemma-3-4b-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/QiMing-Gemma-3-4b-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/QiMing-Gemma-3-4b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/QiMing-Gemma-3-4b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.QiMing-Gemma-3-4b-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

mradermacher commited on Aug 15, 2025

Commit

a6e1043

verified ·

1 Parent(s): e661d82

auto-patch README.md

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README.md CHANGED Viewed

@@ -32,7 +32,7 @@ static quants of https://huggingface.co/aifeifei798/QiMing-Gemma-3-4b
 ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#QiMing-Gemma-3-4b-GGUF).***
-weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
 ## Usage
 If you are unsure how to use GGUF files, refer to one of [TheBloke's
@@ -47,6 +47,18 @@ more details, including on how to concatenate multi-part files.
 |:-----|:-----|--------:|:------|
 | [GGUF](https://huggingface.co/mradermacher/QiMing-Gemma-3-4b-GGUF/resolve/main/QiMing-Gemma-3-4b.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.7 | multi-modal supplement |
 | [GGUF](https://huggingface.co/mradermacher/QiMing-Gemma-3-4b-GGUF/resolve/main/QiMing-Gemma-3-4b.mmproj-f16.gguf) | mmproj-f16 | 1.0 | multi-modal supplement |
 Here is a handy graph by ikawrakow comparing some lower-quality quant
 types (lower is better):

 ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#QiMing-Gemma-3-4b-GGUF).***
+weighted/imatrix quants are available at https://huggingface.co/mradermacher/QiMing-Gemma-3-4b-i1-GGUF
 ## Usage
 If you are unsure how to use GGUF files, refer to one of [TheBloke's
 |:-----|:-----|--------:|:------|
 | [GGUF](https://huggingface.co/mradermacher/QiMing-Gemma-3-4b-GGUF/resolve/main/QiMing-Gemma-3-4b.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.7 | multi-modal supplement |
 | [GGUF](https://huggingface.co/mradermacher/QiMing-Gemma-3-4b-GGUF/resolve/main/QiMing-Gemma-3-4b.mmproj-f16.gguf) | mmproj-f16 | 1.0 | multi-modal supplement |
+| [GGUF](https://huggingface.co/mradermacher/QiMing-Gemma-3-4b-GGUF/resolve/main/QiMing-Gemma-3-4b.Q2_K.gguf) | Q2_K | 1.8 |  |
+| [GGUF](https://huggingface.co/mradermacher/QiMing-Gemma-3-4b-GGUF/resolve/main/QiMing-Gemma-3-4b.Q3_K_S.gguf) | Q3_K_S | 2.0 |  |
+| [GGUF](https://huggingface.co/mradermacher/QiMing-Gemma-3-4b-GGUF/resolve/main/QiMing-Gemma-3-4b.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
+| [GGUF](https://huggingface.co/mradermacher/QiMing-Gemma-3-4b-GGUF/resolve/main/QiMing-Gemma-3-4b.Q3_K_L.gguf) | Q3_K_L | 2.3 |  |
+| [GGUF](https://huggingface.co/mradermacher/QiMing-Gemma-3-4b-GGUF/resolve/main/QiMing-Gemma-3-4b.IQ4_XS.gguf) | IQ4_XS | 2.4 |  |
+| [GGUF](https://huggingface.co/mradermacher/QiMing-Gemma-3-4b-GGUF/resolve/main/QiMing-Gemma-3-4b.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/QiMing-Gemma-3-4b-GGUF/resolve/main/QiMing-Gemma-3-4b.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/QiMing-Gemma-3-4b-GGUF/resolve/main/QiMing-Gemma-3-4b.Q5_K_S.gguf) | Q5_K_S | 2.9 |  |
+| [GGUF](https://huggingface.co/mradermacher/QiMing-Gemma-3-4b-GGUF/resolve/main/QiMing-Gemma-3-4b.Q5_K_M.gguf) | Q5_K_M | 2.9 |  |
+| [GGUF](https://huggingface.co/mradermacher/QiMing-Gemma-3-4b-GGUF/resolve/main/QiMing-Gemma-3-4b.Q6_K.gguf) | Q6_K | 3.3 | very good quality |
+| [GGUF](https://huggingface.co/mradermacher/QiMing-Gemma-3-4b-GGUF/resolve/main/QiMing-Gemma-3-4b.Q8_0.gguf) | Q8_0 | 4.2 | fast, best quality |
+| [GGUF](https://huggingface.co/mradermacher/QiMing-Gemma-3-4b-GGUF/resolve/main/QiMing-Gemma-3-4b.f16.gguf) | f16 | 7.9 | 16 bpw, overkill |
 Here is a handy graph by ikawrakow comparing some lower-quality quant
 types (lower is better):