Instructions to use tepirale/gemma-4-12B-merge-coder40-agentic40-it20-dare_ties with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tepirale/gemma-4-12B-merge-coder40-agentic40-it20-dare_ties with Transformers:

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

pipe = pipeline("image-text-to-text", model="tepirale/gemma-4-12B-merge-coder40-agentic40-it20-dare_ties")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("tepirale/gemma-4-12B-merge-coder40-agentic40-it20-dare_ties")
model = AutoModelForMultimodalLM.from_pretrained("tepirale/gemma-4-12B-merge-coder40-agentic40-it20-dare_ties", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tepirale/gemma-4-12B-merge-coder40-agentic40-it20-dare_ties with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tepirale/gemma-4-12B-merge-coder40-agentic40-it20-dare_ties"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tepirale/gemma-4-12B-merge-coder40-agentic40-it20-dare_ties",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tepirale/gemma-4-12B-merge-coder40-agentic40-it20-dare_ties

SGLang

How to use tepirale/gemma-4-12B-merge-coder40-agentic40-it20-dare_ties 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 "tepirale/gemma-4-12B-merge-coder40-agentic40-it20-dare_ties" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tepirale/gemma-4-12B-merge-coder40-agentic40-it20-dare_ties",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "tepirale/gemma-4-12B-merge-coder40-agentic40-it20-dare_ties" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tepirale/gemma-4-12B-merge-coder40-agentic40-it20-dare_ties",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tepirale/gemma-4-12B-merge-coder40-agentic40-it20-dare_ties with Docker Model Runner:
```
docker model run hf.co/tepirale/gemma-4-12B-merge-coder40-agentic40-it20-dare_ties
```

merged-gemma4-12b

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using google/gemma-4-12B-it as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:


merge_method: dare_ties
base_model: google/gemma-4-12B-it
dtype: bfloat16
parameters:
  int8_mask: true
models:
  - model: tepirale/gemma-4-12B-coder-fable5-composer2.5-v1-safetensors-yuxinlu1
    parameters:
      weight: 0.40
      density: 0.90
  - model: tepirale/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-safetensors-yuxinlu1
    parameters:
      weight: 0.40
      density: 0.90
  - model: google/gemma-4-12B-it
    parameters:
      weight: 0.20
      density: 1.0