Instructions to use kushalpatil/jevify-gemma4-26b-a4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kushalpatil/jevify-gemma4-26b-a4b with Transformers:

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

pipe = pipeline("image-text-to-text", model="kushalpatil/jevify-gemma4-26b-a4b")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("kushalpatil/jevify-gemma4-26b-a4b")
model = AutoModelForMultimodalLM.from_pretrained("kushalpatil/jevify-gemma4-26b-a4b", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kushalpatil/jevify-gemma4-26b-a4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kushalpatil/jevify-gemma4-26b-a4b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kushalpatil/jevify-gemma4-26b-a4b",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/kushalpatil/jevify-gemma4-26b-a4b

SGLang

How to use kushalpatil/jevify-gemma4-26b-a4b 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 "kushalpatil/jevify-gemma4-26b-a4b" \
    --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": "kushalpatil/jevify-gemma4-26b-a4b",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "kushalpatil/jevify-gemma4-26b-a4b" \
        --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": "kushalpatil/jevify-gemma4-26b-a4b",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use kushalpatil/jevify-gemma4-26b-a4b with Docker Model Runner:
```
docker model run hf.co/kushalpatil/jevify-gemma4-26b-a4b
```

kushalpatil/jevify-gemma4-26b-a4b

google/gemma-4-26B-A4B-it fine-tuned (LoRA, merged) to give honest probabilities when asked typed questions about a piece of state — the model behind jevify, a local, Jev-compatible probabilistic decision API.

Nothing is generated: one prefill, read the next-token distribution over the answer labels, done.

Use

pip install "jevify[transformers] @ git+https://github.com/kushalpatil07/jevify"
jevify serve --model kushalpatil/jevify-gemma4-26b-a4b                     # POST /v1/systemone, Jev wire format

from jevify import Jevify, Noul, Choice, Score
jev = Jevify.from_transformers("kushalpatil/jevify-gemma4-26b-a4b")        # or serve it with vLLM and use Jevify.from_runtime("vllm", "kushalpatil/jevify-gemma4-26b-a4b")
jev.system_one("Help! My payouts have been failing for 3 days.", {
    "urgent": Noul("Does this convey urgency?"),
    "team":   Choice("Which team should handle this?", {"billing": None, "technical": None, "sales": None}),
    "mood":   Score("How frustrated is the customer?", ["calm", "frustrated", "furious"]),
})

Training

LoRA r=64 on attention projections, 2 epochs over ~47k (state, question, target-distribution) items from 16 sources: hard-labeled classification sets with randomized option subsets and order, multi-annotator sets with real human label distributions, and constructed long states (up to 24k tokens) with answers known by construction. Loss = KL(target || label distribution). No teacher model. Recipe: train/ in the jevify repo.

Results

Held-out, out-of-distribution (6 datasets not used in training; 307 items), no post-hoc scaling:
ood_massive      n=  50  acc=0.860 nll=0.349 brier=0.179 ece=0.102 conf=0.939
ood_trec         n=  50  acc=0.940 nll=0.202 brier=0.102 ece=0.064 conf=0.922
ood_paws         n=  50  acc=0.580 nll=0.971 brier=0.599 ece=0.288 conf=0.855
ood_sms_spam     n=  50  acc=0.960 nll=0.112 brier=0.056 ece=0.038 conf=0.946
ood_app_reviews  n=  50  acc=0.700 nll=0.939 brier=0.451 ece=0.166 conf=0.717
ood_subj         n=  50  acc=0.940 nll=0.183 brier=0.090 ece=0.149 conf=0.862
spike            n=   7  acc=1.000 nll=0.064 brier=0.024 ece=0.057 conf=0.943
ALL              n= 307  acc=0.834 nll=0.450 brier=0.241 ece=0.061 conf=0.875

In-distribution held-out (800 items), raw -> trained:
  step 0  acc=0.757 conf=0.991 ece=0.234 nll=3.179 brier=0.425
  step 1215  acc=0.821 conf=0.829 ece=0.032 nll=0.422 brier=0.188

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Safetensors

Model size

26B params

Tensor type

BF16

Model tree for kushalpatil/jevify-gemma4-26b-a4b

Base model

google/gemma-4-26B-A4B

Finetuned

google/gemma-4-26B-A4B-it

Adapter

(88)

this model

Adapters

1 model