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
```

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

by aaron-psl - opened 6 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+4118

-1

aaron-psl

SASVA org 6 days ago

Merged bf16 weights (11 x <=5 GB shards) at the root, the QLoRA adapter plus training state and all 332 validation predictions under adapter/, and the model card. Same layout as SASVAAI/Gemma-4-26B-A4B-sigma-rules. Source: AutoCatalyst run 14, trial 1 (pass@5 0.503, greedy pass@1 0.247 on 332 harness-verified methods2test rows).

Add merged Gemma-4-26B-A4B JUnit test generator, LoRA adapter and model card95324478

msingh-psl changed pull request status to merged 6 days ago

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