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

stcoder-gemma4-12b-gguf / lora_adapter /adapter_config.json

Mikrodev

Upload folder using huggingface_hub

15c6a8b verified 23 days ago

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	{
	"alora_invocation_tokens": null,
	"alpha_pattern": {},
	"arrow_config": null,
	"auto_mapping": {
	"base_model_class": "Gemma4UnifiedForConditionalGeneration",
	"parent_library": "transformers.models.gemma4_unified.modeling_gemma4_unified",
	"unsloth_fixed": true
	},
	"base_model_name_or_path": "unsloth/gemma-4-12b-it",
	"bias": "none",
	"corda_config": null,
	"ensure_weight_tying": false,
	"eva_config": null,
	"exclude_modules": null,
	"fan_in_fan_out": false,
	"inference_mode": true,
	"init_lora_weights": true,
	"layer_replication": null,
	"layers_pattern": null,
	"layers_to_transform": null,
	"loftq_config": {},
	"lora_alpha": 32,
	"lora_bias": false,
	"lora_dropout": 0.0,
	"lora_ga_config": null,
	"megatron_config": null,
	"megatron_core": "megatron.core",
	"modules_to_save": null,
	"peft_type": "LORA",
	"peft_version": "0.19.1",
	"qalora_group_size": 16,
	"r": 32,
	"rank_pattern": {},
	"revision": null,
	"target_modules": "(?:.?(?:language\|text).?(?:self_attn\|attention\|attn\|mixer\|mlp\|feed_forward\|ffn\|dense\|mixer).*?(?:q_proj\|k_proj\|v_proj\|o_proj\|gate_proj\|up_proj\|down_proj))\|(?:\\bmodel\\.layers\\.[\\d]{1,}\\.(?:self_attn\|attention\|attn\|mixer\|mlp\|feed_forward\|ffn\|dense\|mixer)\\.(?:(?:q_proj\|k_proj\|v_proj\|o_proj\|gate_proj\|up_proj\|down_proj)))",
	"target_parameters": null,
	"task_type": "CAUSAL_LM",
	"trainable_token_indices": null,
	"use_bdlora": null,
	"use_dora": false,
	"use_qalora": false,
	"use_rslora": false
	}