Instructions to use SASVAAI/gemma-4-26b-jUnitTestGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SASVAAI/gemma-4-26b-jUnitTestGeneration with Transformers:

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

pipe = pipeline("text-generation", model="SASVAAI/gemma-4-26b-jUnitTestGeneration")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("SASVAAI/gemma-4-26b-jUnitTestGeneration")
model = AutoModelForMultimodalLM.from_pretrained("SASVAAI/gemma-4-26b-jUnitTestGeneration", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use SASVAAI/gemma-4-26b-jUnitTestGeneration with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SASVAAI/gemma-4-26b-jUnitTestGeneration with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SASVAAI/gemma-4-26b-jUnitTestGeneration"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SASVAAI/gemma-4-26b-jUnitTestGeneration",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SASVAAI/gemma-4-26b-jUnitTestGeneration

SGLang

How to use SASVAAI/gemma-4-26b-jUnitTestGeneration 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 "SASVAAI/gemma-4-26b-jUnitTestGeneration" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SASVAAI/gemma-4-26b-jUnitTestGeneration",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "SASVAAI/gemma-4-26b-jUnitTestGeneration" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SASVAAI/gemma-4-26b-jUnitTestGeneration",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SASVAAI/gemma-4-26b-jUnitTestGeneration with Docker Model Runner:
```
docker model run hf.co/SASVAAI/gemma-4-26b-jUnitTestGeneration
```

gemma-4-26b-jUnitTestGeneration / model-00003-of-00011.safetensors

msingh-psl

aaron-psl

Add merged Gemma-4-26B-A4B JUnit test generator, LoRA adapter and model card (#1)

e077fc6 18 days ago

History Blame Contribute Delete

4.91 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: cf834f2aaf9e1c6f83e352747c97a77e348ad3acf030f091da07cbd4f4a1d6ab
Size of remote file:: 4.91 GB
SHA256:: cbc3f975c9b1157b4907a6cd5299d49c22a7568ad6b4922d09a4d43693678ce5

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.