--- tags: - vllm - vision - audio - fp8 license: mit base_model: google/gemma-3n-E4B-it library_name: transformers --- # 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](https://huggingface.co/google/gemma-3n-E4B-it). ### Model Optimizations This model was obtained by quantizing the weights of [google/gemma-3n-E4B-it](https://huggingface.co/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](https://docs.vllm.ai/en/latest/) backend, as shown in the example below. ```python 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](https://docs.vllm.ai/en/latest/) for more details. ## Creation This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.

Model Creation Code

```python 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](https://github.com/EleutherAI/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="",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="",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%