Update README.md

README.md

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----
-license: mit
----
+---
+tags:
+- vllm
+- vision
+- audio
+- int8
+license: mit
+base_model: google/gemma-3n-E4B-it
+library_name: transformers
+---
+
+# RedHatAI/gemma-3n-E4B-it-quantized.w4a16
+
+## Model Overview
+- **Model Architecture:** gemma-3n-E4B-it
+  - **Input:** Audio-Vision-Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT4
+  - **Activation quantization:** INT16
+- **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 INT4 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-quantized.w4a16",
+    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.
+
+<details>
+  <summary>Model Creation Code</summary>
+  
+```python
+import requests
+import torch
+from PIL import Image
+from transformers import AutoProcessor, Gemma3nForConditionalGeneration
+
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.utils import dispatch_for_generation
+
+# 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)
+
+# Oneshot arguments
+DATASET_ID = "flickr30k"
+DATASET_SPLIT = {"calibration": "test[:512]"}
+NUM_CALIBRATION_SAMPLES = 512
+MAX_SEQUENCE_LENGTH = 2048
+
+
+# Define a oneshot data collator for multimodal inputs.
+def data_collator(batch):
+    assert len(batch) == 1
+    return {key: torch.tensor(value) for key, value in batch[0].items()}
+
+dampening_frac=0.01
+
+# Recipe
+recipe = [
+    GPTQModifier(
+        targets="Linear",
+        scheme="W4A16",
+        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",
+        ],
+        dampening_frac=dampening_frac
+    ),
+]
+
+SAVE_DIR = f"{model_id.split('/')[1]}-quantized.{recipe[0].scheme}"
+
+# Perform oneshot
+oneshot(
+    model=model,
+    tokenizer=model_id,
+    dataset=DATASET_ID,
+    splits=DATASET_SPLIT,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    trust_remote_code_model=True,
+    data_collator=data_collator,
+    # gemma3n has broken weight offloading which is required by the sequential pipeline
+    pipeline="basic",
+    # gemma3n does not support untying word embeddings
+    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)
+```
+</details>
+
+## 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:
+
+<details>
+<summary>Evaluation Commands</summary>
+
+### 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
+
+```
+</details>
+
+### Accuracy
+
+<table>
+  <thead>
+    <tr>
+      <th>Category</th>
+      <th>Metric</th>
+      <th>google/gemma-3n-E4B-it</th>
+      <th>RedHatAI/gemma-3n-E4B-it-quantized.w4a16</th>
+      <th>Recovery (%)</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td rowspan="7"><b>OpenLLM V1</b></td>
+      <td>arc_challenge</td>
+      <td>60.24</td>
+      <td>59.30</td>
+      <td>98.44%</td>
+    </tr>
+    <tr>
+      <td>gsm8k</td>
+      <td>60.12</td>
+      <td>65.13</td>
+      <td>108.34%</td>
+    </tr>
+    <tr>
+      <td>hellaswag</td>
+      <td>74.94</td>
+      <td>73.31</td>
+      <td>97.82%</td>
+    </tr>
+    <tr>
+      <td>mmlu</td>
+      <td>64.14</td>
+      <td>63.08</td>
+      <td>98.35%</td>
+    </tr>
+    <tr>
+      <td>truthfulqa_mc2</td>
+      <td>54.87</td>
+      <td>54.31</td>
+      <td>99.00%</td>
+    </tr>
+    <tr>
+      <td>winogrande</td>
+      <td>68.35</td>
+      <td>66.77</td>
+      <td>97.68%</td>
+    </tr>
+    <tr>
+      <td><b>Average</b></td>
+      <td>63.78</td>
+      <td>63.65</td>
+      <td><b>99.80%</b></td>
+    </tr>
+    <tr>
+      <td rowspan="7"><b>Leaderboard</b></td>
+      <td>bbh</td>
+      <td>55.46</td>
+      <td>54.89</td>
+      <td>98.97%</td>
+    </tr>
+    <tr>
+      <td>mmlu_pro</td>
+      <td>34.38</td>
+      <td>32.05</td>
+      <td>93.23%</td>
+    </tr>
+    <tr>
+      <td>musr</td>
+      <td>33.20</td>
+      <td>34.66</td>
+      <td>104.40%</td>
+    </tr>
+    <tr>
+      <td>ifeval</td>
+      <td>84.41</td>
+      <td>81.65</td>
+      <td>96.73%</td>
+    </tr>
+    <tr>
+      <td>gpqa</td>
+      <td>30.87</td>
+      <td>28.69</td>
+      <td>92.95%</td>
+    </tr>
+    <tr>
+      <td>math_hard</td>
+      <td>45.54</td>
+      <td>39.95</td>
+      <td>87.72%</td>
+    </tr>
+    <tr>
+      <td><b>Average</b></td>
+      <td>47.31</td>
+      <td>45.32</td>
+      <td><b>95.78%</b></td>
+    </tr>
+  </tbody>
+</table>