--- tags: - vllm - vision - audio - fp8 license: mit base_model: google/gemma-3n-E2B-it library_name: transformers --- # RedHatAI/gemma-3n-E2B-it-FP8-Dynamic ## Model Overview - **Model Architecture:** gemma-3n-E2B-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-E2B-it](https://huggingface.co/google/gemma-3n-E2B-it). ### Model Optimizations This model was obtained by quantizing the weights of [google/gemma-3n-E2B-it](https://huggingface.co/google/gemma-3n-E2B-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-E2B-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-E2B-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-E2B-it FP8 Dynamic Recovery (%)
OpenLLM V1 arc_challenge 50.60 50.09 99.00%
gsm8k 48.07 54.51 113.40%
hellaswag 67.78 65.67 96.89%
mmlu 59.92 60.16 100.40%
truthfulqa_mc2 49.98 49.48 99.00%
winogrande 65.11 63.85 98.06%
Average 56.91 57.29 100.67%
Leaderboard bbh 53.32 52.99 99.38%
mmlu_pro 29.76 29.36 98.66%
musr 34.52 35.85 103.85%
ifeval 80.22 80.58 100.45%
gpqa 30.54 29.36 96.14%
math_hard 34.52 34.97 101.30%
Average 43.81 43.85 100.09%