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Model: RedHatAI/gemma-3-1b-it-quantized.w8a8
Source: Original Platform

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+

README.md

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+---
+tags:
+- vllm
+- vision
+- w8a8
+license: gemma
+base_model: google/gemma-3-1b-it
+library_name: transformers
+---
+
+# gemma-3-1b-it-quantized.w8a8
+
+## Model Overview
+- **Model Architecture:** google/gemma-3-1b-it
+  - **Input:** Vision-Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT8
+  - **Activation quantization:** INT8
+- **Release Date:** 6/4/2025
+- **Version:** 1.0
+- **Model Developers:** RedHatAI
+
+Quantized version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it).
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) to INT8 data type, ready for inference with vLLM >= 0.8.0.
+
+## 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-3-1b-it-quantized.w8a8",
+    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 base64
+from io import BytesIO
+import torch
+from datasets import load_dataset
+from transformers import AutoProcessor, Gemma3ForCausalLM
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.transformers import oneshot
+
+
+# Load model.
+model_id = "google/gemma-3-1b-it"
+model = Gemma3ForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype="auto",
+)
+processor = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+
+# Oneshot arguments
+DATASET_ID = "neuralmagic/calibration"
+DATASET_SPLIT = {"LLM": "train[:512]"}
+NUM_CALIBRATION_SAMPLES = 512
+MAX_SEQUENCE_LENGTH = 2048
+
+# Load dataset and preprocess.
+ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
+ds = ds.shuffle(seed=42)
+
+dampening_frac=0.01
+
+def data_collator(batch):
+    assert len(batch) == 1, "Only batch size of 1 is supported for calibration"
+    item = batch[0]
+    collated = {}
+    import torch
+
+
+    for key, value in item.items():
+        if isinstance(value, torch.Tensor):
+            collated[key] = value.unsqueeze(0)
+        elif isinstance(value, list) and isinstance(value[0][0], int):
+            # Handle tokenized inputs like input_ids, attention_mask
+            collated[key] = torch.tensor(value)
+        elif isinstance(value, list) and isinstance(value[0][0], float):
+            # Handle possible float sequences
+            collated[key] = torch.tensor(value)
+        elif isinstance(value, list) and isinstance(value[0][0], torch.Tensor):
+            # Handle batched image data (e.g., pixel_values as [C, H, W])
+            collated[key] = torch.stack(value)  # -> [1, C, H, W]
+        elif isinstance(value, torch.Tensor):
+            collated[key] = value
+        else:
+            print(f"[WARN] Unrecognized type in collator for key={key}, type={type(value)}")
+    
+    return collated
+   
+
+
+# Recipe
+recipe = [
+    GPTQModifier(
+        targets="Linear",
+        ignore=["re:.*lm_head.*", "re:.*embed_tokens.*", "re:vision_tower.*", "re:multi_modal_projector.*"],
+        sequential_update=True,
+        sequential_targets=["Gemma3DecoderLayer"],
+        dampening_frac=dampening_frac,
+    )
+]
+
+SAVE_DIR=f"{model_id.split('/')[1]}-quantized.w8a8"
+
+# Perform oneshot
+oneshot(
+    model=model,
+    tokenizer=model_id,
+    dataset=ds,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    trust_remote_code_model=True,
+    data_collator=data_collator,
+    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-3-1b-it</th>
+      <th>RedHatAI/gemma-3-1b-it-quantized.w8a8</th>
+      <th>Recovery (%)</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td rowspan="7"><b>OpenLLM V1</b></td>
+      <td>ARC Challenge</td>
+      <td>36.86%</td>
+      <td>36.43%</td>
+      <td>98.84%<td>
+    </tr>
+    <tr>
+      <td>GSM8K</td>
+      <td>25.17%</td>
+      <td>24.87%</td>
+      <td>98.80%</%</td>
+    </tr>
+    <tr>
+      <td>Hellaswag</td>
+      <td>56.03%</td>
+      <td>55.62%</td>
+      <td>99.25%</td>
+    </tr>
+    <tr>
+      <td>MMLU</td>
+      <td>39.99%</td>
+      <td>39.35%</td>
+      <td>98.38%</td>
+    </tr>
+    <tr>
+      <td>Truthfulqa (mc2)</td>
+      <td>38.54%</td>
+      <td>38.22%</td>
+      <td>99.17%</td>
+    </tr>
+    <tr>
+      <td>Winogrande</td>
+      <td>58.88%</td>
+      <td>58.96%</td>
+      <td>100.13%</td>
+    </tr>
+    <tr>
+      <td><b>Average Score</b></td>
+      <td><b>42.58%</b></td>
+      <td><b>42.24%</b></td>
+      <td><b>99.20%</b></td>
+    </tr>
+  </tbody>
+</table>

added_tokens.json

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+{
+  "<image_soft_token>": 262144
+}

config.json

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configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+{
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "do_sample": true,
+  "eos_token_id": [
+    1,
+    106
+  ],
+  "pad_token_id": 0,
+  "top_k": 64,
+  "top_p": 0.95,
+  "transformers_version": "4.51.3"
+}

model.safetensors

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recipe.yaml

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+quant_stage:
+  quant_modifiers:
+    GPTQModifier:
+      dampening_frac: 0.01
+      ignore: ['re:.*lm_head.*', 're:.*embed_tokens.*', 're:vision_tower.*', 're:multi_modal_projector.*']
+      sequential_update: true
+      config_groups:
+        group_0:
+          targets: [Linear]
+          weights: {num_bits: 8, type: int, symmetric: true, strategy: channel, observer: mse}
+          input_activations: {num_bits: 8, type: int, symmetric: true, strategy: token, dynamic: true,
+            observer: memoryless}

special_tokens_map.json

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+{
+  "boi_token": "<start_of_image>",
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eoi_token": "<end_of_image>",
+  "eos_token": {
+    "content": "<eos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "image_token": "<image_soft_token>",
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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tokenizer.model

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