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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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README.md
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---
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tags:
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- vllm
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- vision
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- w8a8
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license: gemma
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base_model: google/gemma-3-1b-it
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library_name: transformers
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---
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# gemma-3-1b-it-quantized.w8a8
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## Model Overview
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- **Model Architecture:** google/gemma-3-1b-it
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- **Input:** Vision-Text
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- **Output:** Text
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- **Model Optimizations:**
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- **Weight quantization:** INT8
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- **Activation quantization:** INT8
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- **Release Date:** 6/4/2025
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- **Version:** 1.0
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- **Model Developers:** RedHatAI
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Quantized version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it).
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### Model Optimizations
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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.
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## Deployment
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### Use with vLLM
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This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
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```python
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from vllm.assets.image import ImageAsset
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from vllm import LLM, SamplingParams
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# prepare model
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llm = LLM(
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model="RedHatAI/gemma-3-1b-it-quantized.w8a8",
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trust_remote_code=True,
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max_model_len=4096,
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max_num_seqs=2,
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)
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# prepare inputs
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question = "What is the content of this image?"
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inputs = {
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"prompt": f"<|user|>\n<|image_1|>\n{question}<|end|>\n<|assistant|>\n",
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"multi_modal_data": {
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"image": ImageAsset("cherry_blossom").pil_image.convert("RGB")
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},
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}
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# generate response
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print("========== SAMPLE GENERATION ==============")
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outputs = llm.generate(inputs, SamplingParams(temperature=0.2, max_tokens=64))
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print(f"PROMPT : {outputs[0].prompt}")
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print(f"RESPONSE: {outputs[0].outputs[0].text}")
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print("==========================================")
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```
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vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
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## Creation
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This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below:
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<details>
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<summary>Model Creation Code</summary>
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```python
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import base64
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from io import BytesIO
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import torch
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from datasets import load_dataset
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from transformers import AutoProcessor, Gemma3ForCausalLM
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from llmcompressor.modifiers.quantization import GPTQModifier
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from llmcompressor.transformers import oneshot
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# Load model.
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model_id = "google/gemma-3-1b-it"
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model = Gemma3ForCausalLM.from_pretrained(
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model_id,
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device_map="auto",
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torch_dtype="auto",
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)
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processor = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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# Oneshot arguments
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DATASET_ID = "neuralmagic/calibration"
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DATASET_SPLIT = {"LLM": "train[:512]"}
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NUM_CALIBRATION_SAMPLES = 512
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MAX_SEQUENCE_LENGTH = 2048
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# Load dataset and preprocess.
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ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
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ds = ds.shuffle(seed=42)
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dampening_frac=0.01
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def data_collator(batch):
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assert len(batch) == 1, "Only batch size of 1 is supported for calibration"
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item = batch[0]
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collated = {}
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import torch
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for key, value in item.items():
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if isinstance(value, torch.Tensor):
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collated[key] = value.unsqueeze(0)
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elif isinstance(value, list) and isinstance(value[0][0], int):
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# Handle tokenized inputs like input_ids, attention_mask
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collated[key] = torch.tensor(value)
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elif isinstance(value, list) and isinstance(value[0][0], float):
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# Handle possible float sequences
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collated[key] = torch.tensor(value)
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elif isinstance(value, list) and isinstance(value[0][0], torch.Tensor):
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# Handle batched image data (e.g., pixel_values as [C, H, W])
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collated[key] = torch.stack(value) # -> [1, C, H, W]
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elif isinstance(value, torch.Tensor):
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collated[key] = value
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else:
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print(f"[WARN] Unrecognized type in collator for key={key}, type={type(value)}")
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return collated
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# Recipe
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recipe = [
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GPTQModifier(
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targets="Linear",
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ignore=["re:.*lm_head.*", "re:.*embed_tokens.*", "re:vision_tower.*", "re:multi_modal_projector.*"],
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sequential_update=True,
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sequential_targets=["Gemma3DecoderLayer"],
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dampening_frac=dampening_frac,
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)
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]
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SAVE_DIR=f"{model_id.split('/')[1]}-quantized.w8a8"
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# Perform oneshot
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oneshot(
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model=model,
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tokenizer=model_id,
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dataset=ds,
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recipe=recipe,
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max_seq_length=MAX_SEQUENCE_LENGTH,
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num_calibration_samples=NUM_CALIBRATION_SAMPLES,
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trust_remote_code_model=True,
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data_collator=data_collator,
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output_dir=SAVE_DIR
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)
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```
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</details>
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## Evaluation
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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:
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<details>
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<summary>Evaluation Commands</summary>
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### OpenLLM v1
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```
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lm_eval \
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--model vllm \
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--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 \
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--tasks openllm \
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--batch_size auto
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```
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</details>
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### Accuracy
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<table>
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<thead>
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<tr>
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<th>Category</th>
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<th>Metric</th>
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<th>google/gemma-3-1b-it</th>
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<th>RedHatAI/gemma-3-1b-it-quantized.w8a8</th>
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<th>Recovery (%)</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td rowspan="7"><b>OpenLLM V1</b></td>
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<td>ARC Challenge</td>
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<td>36.86%</td>
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<td>36.43%</td>
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<td>98.84%<td>
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</tr>
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<tr>
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<td>GSM8K</td>
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<td>25.17%</td>
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<td>24.87%</td>
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<td>98.80%</%</td>
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</tr>
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<tr>
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<td>Hellaswag</td>
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<td>56.03%</td>
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<td>55.62%</td>
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<td>99.25%</td>
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</tr>
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<tr>
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<td>MMLU</td>
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<td>39.99%</td>
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<td>39.35%</td>
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<td>98.38%</td>
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</tr>
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<tr>
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<td>Truthfulqa (mc2)</td>
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<td>38.54%</td>
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<td>38.22%</td>
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<td>99.17%</td>
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</tr>
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<tr>
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<td>Winogrande</td>
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<td>58.88%</td>
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<td>58.96%</td>
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<td>100.13%</td>
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</tr>
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<tr>
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<td><b>Average Score</b></td>
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<td><b>42.58%</b></td>
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<td><b>42.24%</b></td>
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<td><b>99.20%</b></td>
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</tr>
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</tbody>
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</table>
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