Instructions to use prithivMLmods/gemma-4-31B-it-qat-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/gemma-4-31B-it-qat-FP8 with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/gemma-4-31B-it-qat-FP8")
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("prithivMLmods/gemma-4-31B-it-qat-FP8")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/gemma-4-31B-it-qat-FP8")
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 prithivMLmods/gemma-4-31B-it-qat-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/gemma-4-31B-it-qat-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/gemma-4-31B-it-qat-FP8",
		"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/prithivMLmods/gemma-4-31B-it-qat-FP8

SGLang

How to use prithivMLmods/gemma-4-31B-it-qat-FP8 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 "prithivMLmods/gemma-4-31B-it-qat-FP8" \
    --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": "prithivMLmods/gemma-4-31B-it-qat-FP8",
		"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 "prithivMLmods/gemma-4-31B-it-qat-FP8" \
        --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": "prithivMLmods/gemma-4-31B-it-qat-FP8",
		"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 prithivMLmods/gemma-4-31B-it-qat-FP8 with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/gemma-4-31B-it-qat-FP8
```

gemma-4-31B-it-qat-FP8

google/gemma-4-31B-it-qat-q4_0-unquantized is a 31-billion-parameter instruction-tuned multimodal model from Google DeepMind, optimized using Quantization-Aware Training (QAT) and released in an unquantized Q4_0 checkpoint format for research, custom compilation, and downstream quantization workflows. The model supports text and image inputs with text generation outputs, features a 256K-token context window, native reasoning ("thinking") capabilities, function calling, multilingual support across 140+ languages, and strong performance in coding, reasoning, document understanding, and long-context tasks. Unlike the GGUF release, this checkpoint preserves the QAT-trained weights before final deployment quantization, making it particularly suitable for experimentation with custom inference engines, FP8/NVFP4 quantization, and production optimization frameworks while maintaining quality close to the original high-precision model.

recipe.yaml

default_stage:
  default_modifiers:
    QuantizationModifier:
      targets: [Linear]
      ignore: [lm_head, 're:.*vision_tower.*', 're:.*embed_vision.*']
      scheme: FP8_DYNAMIC
      bypass_divisibility_checks: false

llm-compressor

An open-source library developed by the vLLM team, designed to optimize Large Language Models (LLMs) for production deployment — https://github.com/vllm-project/llm-compressor