Instructions to use Mikrodev/stcoder-gemma4-12b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Mikrodev/stcoder-gemma4-12b-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 Mikrodev/stcoder-gemma4-12b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Mikrodev/stcoder-gemma4-12b-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 Mikrodev/stcoder-gemma4-12b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Mikrodev/stcoder-gemma4-12b-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 Mikrodev/stcoder-gemma4-12b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Mikrodev/stcoder-gemma4-12b-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 Mikrodev/stcoder-gemma4-12b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Mikrodev/stcoder-gemma4-12b-gguf:Q4_K_M

Use Docker

docker model run hf.co/Mikrodev/stcoder-gemma4-12b-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use Mikrodev/stcoder-gemma4-12b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mikrodev/stcoder-gemma4-12b-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": "Mikrodev/stcoder-gemma4-12b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Mikrodev/stcoder-gemma4-12b-gguf:Q4_K_M

Ollama
How to use Mikrodev/stcoder-gemma4-12b-gguf with Ollama:
```
ollama run hf.co/Mikrodev/stcoder-gemma4-12b-gguf:Q4_K_M
```

Unsloth Studio

How to use Mikrodev/stcoder-gemma4-12b-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 Mikrodev/stcoder-gemma4-12b-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 Mikrodev/stcoder-gemma4-12b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Mikrodev/stcoder-gemma4-12b-gguf to start chatting

Docker Model Runner
How to use Mikrodev/stcoder-gemma4-12b-gguf with Docker Model Runner:
```
docker model run hf.co/Mikrodev/stcoder-gemma4-12b-gguf:Q4_K_M
```

Lemonade

How to use Mikrodev/stcoder-gemma4-12b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Mikrodev/stcoder-gemma4-12b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.stcoder-gemma4-12b-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

Mikrodev commited on 23 days ago

Commit

fff4647

verified ·

1 Parent(s): 5f904a2

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@@ -60,9 +60,11 @@ If you are choosing for the first time, start with [`Mikrodev/stcoder-qwen25-7b-
 | Build | File | Size | Free VRAM needed | In repo | Verdict |
 |---|---|---|---|---|---|
 | **Q8_0** | `gemma4_12b-tc.q8_0.gguf` | 11.80 GiB | 13.8 GiB | in this repo | **recommended**. Crashed on a 16 GiB card in our own testing at 8k context - if that happens, drop to Q6_K |
-| **Q6_K** | `gemma4_12b-tc.q6_k.gguf` | 9.11 GiB | 11.1 GiB | in this repo | fine — build used in the study below |
 | **Q4_K_M** | `gemma4_12b-tc.q4_k_m.gguf` | 6.87 GiB | 8.9 GiB | in this repo | not recommended |
 *Free VRAM* is the file plus roughly 2 GiB for the 8192-token context and runtime. Less than that
 and the runtime spills layers to system RAM: it still answers, but the speed figures below no
 longer apply. CPU-only and Apple unified memory work too — same arithmetic against system RAM,

 | Build | File | Size | Free VRAM needed | In repo | Verdict |
 |---|---|---|---|---|---|
 | **Q8_0** | `gemma4_12b-tc.q8_0.gguf` | 11.80 GiB | 13.8 GiB | in this repo | **recommended**. Crashed on a 16 GiB card in our own testing at 8k context - if that happens, drop to Q6_K |
+| **Q6_K** | `gemma4_12b-tc.q6_k.gguf` | 9.11 GiB | 11.1 GiB | being uploaded | fine — build used in the study below |
 | **Q4_K_M** | `gemma4_12b-tc.q4_k_m.gguf` | 6.87 GiB | 8.9 GiB | in this repo | not recommended |
+> **Still uploading:** Q6_K. If a download 404s that build has not landed yet — check the repo's *Files* tab, or take one marked *in this repo*.
 *Free VRAM* is the file plus roughly 2 GiB for the 8192-token context and runtime. Less than that
 and the runtime spills layers to system RAM: it still answers, but the speed figures below no
 longer apply. CPU-only and Apple unified memory work too — same arithmetic against system RAM,