RedHatAI/gemma-3n-E4B-it-FP8-Dynamic

Model Overview

• Model Architecture: gemma-3n-E4B-it
  • Input: Audio-Vision-Text
  • Output: Text
• Model Optimizations:
  • Weight quantization: FP8
  • Activation quantization: FP8
• Release Date: 08/01/2025
• Version: 1.0
• Model Developers: RedHatAI

Quantized version of google/gemma-3n-E4B-it.

Model Optimizations

This model was obtained by quantizing the weights of google/gemma-3n-E4B-it to FP8 data type, ready for inference with vLLM >= 0.10.0

Deployment

Use with vLLM

This model can be deployed efficiently using the vLLM backend, as shown in the example below.

from vllm.assets.image import ImageAsset
from vllm import LLM, SamplingParams

# prepare model
llm = LLM(
    model="RedHatAI/gemma-3n-E4B-it-FP8-Dynamic",
    trust_remote_code=True,
    max_model_len=4096,
    max_num_seqs=2,
)

# prepare inputs
question = "What is the content of this image?"
inputs = {
    "prompt": f"<|user|>\n<|image_1|>\n{question}<|end|>\n<|assistant|>\n",
    "multi_modal_data": {
        "image": ImageAsset("cherry_blossom").pil_image.convert("RGB")
    },
}

# generate response
print("========== SAMPLE GENERATION ==============")
outputs = llm.generate(inputs, SamplingParams(temperature=0.2, max_tokens=64))
print(f"PROMPT  : {outputs[0].prompt}")
print(f"RESPONSE: {outputs[0].outputs[0].text}")
print("==========================================")

vLLM also supports OpenAI-compatible serving. See the documentation for more details.

Creation

This model was created with llm-compressor by running the code snippet below.

Model Creation Code

from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier
from transformers import AutoProcessor, Gemma3nForConditionalGeneration

# Load model.
model_id = "google/gemma-3n-E4B-it"
model = Gemma3nForConditionalGeneration.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

# Recipe
recipe = [
    QuantizationModifier(
        targets="Linear",
        scheme="FP8_DYNAMIC",
        ignore=[
            "re:.*embed_audio.*",
            "re:.*embed_vision.*",
            "re:.*audio_tower.*",
            "re:.*vision_tower.*",
            "re:.*altup.*",
            "re:.*lm_head.*",
            "re:.*laurel.*",
            "re:model\.language_model\.layers\.\d+\.per_layer_input_gate",
            "re:model\.language_model\.layers\.\d+\.per_layer_projection",
            "model.language_model.per_layer_model_projection",
        ],
    ),
]

SAVE_DIR = f"{model_id.split('/')[1]}-{recipe[0].scheme}"

# Perform oneshot
oneshot(
    model=model,
    tokenizer=model_id,
    recipe=recipe,
    trust_remote_code_model=True,
    tie_word_embeddings=True,
    output_dir=SAVE_DIR,
)

# Save to disk compressed.
model.save_pretrained(SAVE_DIR, save_compressed=True)
processor.save_pretrained(SAVE_DIR)

Evaluation

The model was evaluated using lm_evaluation_harness for OpenLLM V1 and V2 text-based benchmarks. The evaluations were conducted using the following commands:

Evaluation Commands

OpenLLM V1

lm_eval \
  --model vllm \
  --model_args pretrained="<model_name>",dtype=auto,add_bos_token=false,max_model_len=4096,gpu_memory_utilization=0.8,enable_chunked_prefill=True,enforce_eager=True,trust_remote_code=True \
  --tasks openllm \
  --batch_size auto \
  --apply_chat_template \
  --fewshot_as_multiturn

Leaderboard V2

lm_eval \
  --model vllm \
  --model_args pretrained="<model_name>",dtype=auto,add_bos_token=false,max_model_len=15000,gpu_memory_utilization=0.5,enable_chunked_prefill=True,enforce_eager=True,trust_remote_code=True \
  --tasks leaderboard \
  --batch_size auto \
  --apply_chat_template \
  --fewshot_as_multiturn

Accuracy

Category	Metric	google/gemma-3n-E4B-it	FP8 Dynamic	Recovery (%)
OpenLLM V1	arc_challenge	60.24	59.04	98.01%
	gsm8k	60.12	70.81	117.79%
	hellaswag	74.94	73.28	97.79%
	mmlu	64.14	64.82	101.06%
	truthfulqa_mc2	54.87	54.61	99.53%
	winogrande	68.35	67.72	99.08%
	Average	63.78	65.05	101.99%
Leaderboard	bbh	55.46	55.20	99.53%
	mmlu_pro	34.38	34.28	99.71%
	musr	33.20	34.26	103.19%
	ifeval	84.41	83.93	99.43%
	gpqa	30.87	31.38	101.65%
	math_hard	45.54	46.60	102.33%
	Average	47.31	47.61	100.63%