Instructions to use AtomicChat/gemma-4-E2B-it-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AtomicChat/gemma-4-E2B-it-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="AtomicChat/gemma-4-E2B-it-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("AtomicChat/gemma-4-E2B-it-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("AtomicChat/gemma-4-E2B-it-NVFP4", 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 AtomicChat/gemma-4-E2B-it-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AtomicChat/gemma-4-E2B-it-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": "AtomicChat/gemma-4-E2B-it-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AtomicChat/gemma-4-E2B-it-NVFP4

SGLang

How to use AtomicChat/gemma-4-E2B-it-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 "AtomicChat/gemma-4-E2B-it-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": "AtomicChat/gemma-4-E2B-it-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 "AtomicChat/gemma-4-E2B-it-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": "AtomicChat/gemma-4-E2B-it-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AtomicChat/gemma-4-E2B-it-NVFP4 with Docker Model Runner:
```
docker model run hf.co/AtomicChat/gemma-4-E2B-it-NVFP4
```

Base model: google/gemma-4-E2B-it

Gemma 4 E2B, self-quantized to NVFP4 by Atomic Chat. Built straight from Google's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.

Highlights

2.3B effective (5.1B with embeddings) parameters: the weights this repo quantizes.
Context length: 128K tokens, as published by Google.
35 layers: Dense decoder, hybrid sliding-window (512) and global attention.
Modalities: Text, Image, Audio.
Full imatrix ladder: every quant is calibrated with an importance matrix.
Reasoning: All models in the family are designed as highly capable reasoners, with configurable thinking modes.
Diverse & Efficient Architectures: Offers Dense and Mixture-of-Experts (MoE) variants of different sizes for scalable deployment.

These NVFP4s are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

Model Overview

Property	Value
Base model	`google/gemma-4-E2B-it`
Parameters	2.3B effective (5.1B with embeddings)
Layers	35
Sliding window	512 tokens
Context length	128K tokens
Vocabulary	262K
Modalities	Text, Image, Audio
Architecture	Dense decoder, hybrid sliding-window (512) and global attention, 8 attention heads over 1 KV head, `Gemma4ForConditionalGeneration`
This repo	NVFP4 weights

Benchmarks

Benchmark	Score
MMLU Pro	60.0%
AIME 2026 no tools	37.5%
LiveCodeBench v6	44.0%
Codeforces ELO	633
GPQA Diamond	43.4%
Tau2 (average over 3)	24.5%
BigBench Extra Hard	21.9%
MMMLU	67.4%
MMMU Pro	44.2%
OmniDocBench 1.5 (average edit distance, lower is better)	0.290
MATH-Vision	52.4%
MedXPertQA MM	23.5%
CoVoST	33.47
FLEURS (lower is better)	0.09
MRCR v2 8 needle 128k (average)	19.1%

Scores are Google's published results for the base google/gemma-4-E2B-it, not our own measurements. Quantization preserves the large majority of this; Q4_K_M and up stay close to full precision.

Get started

Atomic Chat: search AtomicChat/gemma-4-E2B-it-NVFP4 and hit Use this model.
vLLM: vllm serve AtomicChat/gemma-4-E2B-it-NVFP4 --max-model-len 8192

Best practices

Parameter	Value
temperature	1.0
top_p	0.95
top_k	64

Google's recommended sampling configuration for google/gemma-4-E2B-it.

How these were made

Download google/gemma-4-E2B-it (original weights).
Quantize to NVFP4 with llm-compressor over a calibration corpus.

License

Original model by Google, released under the Apache 2.0 license. Full terms: Apache 2.0. Quantized by Atomic Chat.

Downloads last month: 48

Safetensors

Model size

5B params

Tensor type

F32

BF16

F8_E4M3

Model tree for AtomicChat/gemma-4-E2B-it-NVFP4

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Quantized

(298)

this model

Collection including AtomicChat/gemma-4-E2B-it-NVFP4

Gemma 4

Collection

Atomic Chat GGUF builds of Google Gemma 4 • 20 items • Updated 9 days ago • 1