Instructions to use AEON-7/Gemma-4-E4B-DECKARD-HERETIC-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AEON-7/Gemma-4-E4B-DECKARD-HERETIC-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="AEON-7/Gemma-4-E4B-DECKARD-HERETIC-NVFP4")
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("AEON-7/Gemma-4-E4B-DECKARD-HERETIC-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("AEON-7/Gemma-4-E4B-DECKARD-HERETIC-NVFP4")
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 AEON-7/Gemma-4-E4B-DECKARD-HERETIC-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AEON-7/Gemma-4-E4B-DECKARD-HERETIC-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AEON-7/Gemma-4-E4B-DECKARD-HERETIC-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AEON-7/Gemma-4-E4B-DECKARD-HERETIC-NVFP4

SGLang

How to use AEON-7/Gemma-4-E4B-DECKARD-HERETIC-NVFP4 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 "AEON-7/Gemma-4-E4B-DECKARD-HERETIC-NVFP4" \
    --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": "AEON-7/Gemma-4-E4B-DECKARD-HERETIC-NVFP4",
		"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 "AEON-7/Gemma-4-E4B-DECKARD-HERETIC-NVFP4" \
        --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": "AEON-7/Gemma-4-E4B-DECKARD-HERETIC-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AEON-7/Gemma-4-E4B-DECKARD-HERETIC-NVFP4 with Docker Model Runner:
```
docker model run hf.co/AEON-7/Gemma-4-E4B-DECKARD-HERETIC-NVFP4
```

Gemma-4-E4B-DECKARD-HERETIC-NVFP4

NVFP4-quantized EAGLE-style speculative-decoding drafter for the standard (non-uncensored) Gemma 4 31B DECKARD HERETIC. Pair with the matching target model for accelerated single-stream decode on Blackwell-class GPUs (DGX Spark / RTX PRO 6000 / RTX 5090 / B100 / B200).

For the abliterated/uncensored variant of this drafter, see AEON-7/Gemma-4-E4B-DECKARD-HERETIC-Uncensored-NVFP4. For the target it accelerates, see the gemma-4-31B-it-speculator.eagle3-NVFP4 collection on this profile.

Files

model.safetensors — NVFP4-quantized weights
hf_quant_config.json — modelopt quant config
chat_template.jinja — Gemma 4 chat template
config.json / generation_config.json / tokenizer.* / processor_config.json

Quick start (vLLM, as drafter)

vllm serve <target-model-id> \
  --speculative-config '{"method":"eagle3","model":"AEON-7/Gemma-4-E4B-DECKARD-HERETIC-NVFP4","num_speculative_tokens":3}' \
  --trust-remote-code