Update README.md

README.md

@@ -1,4 +1,213 @@
 ---
-base_model:
-- google/gemma-3-4b-it
----
+tags:
+- vllm
+- vision
+- fp8
+license: apache-2.0
+license_link: >-
+  https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md
+language:
+- en
+base_model: google/gemma-3-4b-it
+library_name: transformers
+---
+
+# gemma-3-4b-it-FP8-Dynamic
+
+## Model Overview
+- **Model Architecture:** gemma-3-4b-it
+  - **Input:** Vision-Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** FP8
+  - **Activation quantization:** FP8
+- **Release Date:** 2/24/2025
+- **Version:** 1.0
+- **Model Developers:** Neural Magic
+
+Quantized version of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it).
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it) to FP8 data type, ready for inference with vLLM >= 0.5.2.
+
+## 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 import LLM, SamplingParams
+from vllm.assets.image import ImageAsset
+from transformers import AutoProcessor
+
+# Define model name once
+model_name = "RedHatAI/gemma-3-4b-it-FP8-dynamic"
+
+# Load image and processor
+image = ImageAsset("cherry_blossom").pil_image.convert("RGB")
+processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+
+# Build multimodal prompt
+chat = [
+    {"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "What is the content of this image?"}]},
+    {"role": "assistant", "content": []}
+]
+prompt = processor.apply_chat_template(chat, add_generation_prompt=True)
+
+# Initialize model
+llm = LLM(model=model_name, trust_remote_code=True)
+
+# Run inference
+inputs = {"prompt": prompt, "multi_modal_data": {"image": [image]}}
+outputs = llm.generate(inputs, SamplingParams(temperature=0.2, max_tokens=64))
+
+# Display result
+print("RESPONSE:", outputs[0].outputs[0].text)
+
+```
+
+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 as part a multimodal announcement blog.
+
+<details>
+  <summary>Model Creation Code</summary>
+  
+```python
+import requests
+import torch
+from PIL import Image
+from transformers import AutoProcessor, Gemma3ForConditionalGeneration
+from llmcompressor.transformers import oneshot
+from llmcompressor.modifiers.quantization import QuantizationModifier
+
+# Load model.
+model_id = google/gemma-3-4b-it
+model = Gemma3ForConditionalGeneration.from_pretrained(
+    model_id, device_map="auto", torch_dtype="auto"
+)
+processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+
+# Recipe
+recipe = [
+    QuantizationModifier(
+        targets="Linear",
+        scheme="FP8_DYNAMIC",
+        sequential_targets=["Gemma3DecoderLayer"],
+        ignore=["re:.*lm_head", "re:vision_tower.*", "re:multi_modal_projector.*"],
+    ),
+]
+
+SAVE_DIR=f"{model_id.split('/')[1]}-FP8-Dynamic"
+
+# Perform oneshot
+oneshot(
+    model=model,
+    recipe=recipe,
+    trust_remote_code_model=True,
+    output_dir=SAVE_DIR
+)
+
+
+```
+</details>
+
+## Evaluation
+
+The model was evaluated using [lm_evaluation_harness](https://github.com/neuralmagic/lm-evaluation-harness) for OpenLLM v1 text benchmark. 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=True,max_model_len=4096,tensor_parallel_size=<n>,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True,enforce_eager=True \
+  --tasks openllm \
+  --batch_size auto
+```
+</details>
+
+### Accuracy
+
+<table>
+  <thead>
+    <tr>
+      <th>Category</th>
+      <th>Metric</th>
+      <th>google/gemma-3-4b-it</th>
+      <th>RedHatAI/gemma-3-4b-it-FP8-Dynamic</th>
+      <th>Recovery (%)</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td rowspan="7"><b>OpenLLM V1</b></td>
+      <td>ARC Challenge</td>
+      <td>56.57%</td>
+      <td>57.08%</td>
+      <td>100.90%</td>
+    </tr>
+    <tr>
+      <td>GSM8K</td>
+      <td>76.12%</td>
+      <td>75.51%</td>
+      <td>99.20%</td>
+    </tr>
+    <tr>
+      <td>Hellaswag</td>
+      <td>74.96%</td>
+      <td>74.92%</td>
+      <td>99.95%</td>
+    </tr>
+    <tr>
+      <td>MMLU</td>
+      <td>58.38%</td>
+      <td>57.98%</td>
+      <td>99.32%</td>
+    </tr>
+    <tr>
+      <td>Truthfulqa (mc2)</td>
+      <td>51.87%</td>
+      <td>51.62%</td>
+      <td>99.52%</td>
+    </tr>
+    <tr>
+      <td>Winogrande</td>
+      <td>70.32%</td>
+      <td>71.03%</td>
+      <td>101.01%%%%</td>
+    </tr>
+    <tr>
+      <td><b>Average Score</b></td>
+      <td><b>64.70%</b></td>
+      <td><b>64.69%</b></td>
+      <td><b>99.98%</b></td>
+    </tr>
+    <tr>
+      <td rowspan="3"><b>Vision Evals</b></td>
+      <td>MMMU (val)</td>
+      <td>%</td>	
+      <td>%</td>
+      <td>%</td>
+    </tr>
+    <tr>
+      <td>ChartQA</td>
+      <td>%</td>
+      <td>%</td>
+      <td>%</td>
+    </tr>
+    <tr>
+      <td><b>Average Score</b></td>
+      <td><b>%</b></td>
+      <td><b>%</b></td>
+      <td><b>%</b></td>
+    </tr>
+  </tbody>
+</table>
+