gemma-3-1b-it-quantized.w8a8

942
1.0B
—
by
RedHatAI
Language Model
OTHER
1B params
New
942 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
3GB+ RAM
Mobile
Laptop
Server
Quick Summary

Model Overview - Model Architecture: google/gemma-3-1b-it - Input: Vision-Text - Output: Text - Model Optimizations: - Weight quantization: INT8 - Activation qu...

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
1GB+ RAM

Training Data Analysis

🟡 Average (4.3/10)

Researched training datasets used by gemma-3-1b-it-quantized.w8a8 with quality assessment

Specialized For

general
science
multilingual
reasoning

Training Datasets (3)

common crawl
🔴 2.5/10
general
science
Key Strengths
  • •Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
  • •Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
  • •Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...
Considerations
  • •Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
  • •Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...
wikipedia
🟡 5/10
science
multilingual
Key Strengths
  • •High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
  • •Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
  • •Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...
Considerations
  • •Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
  • •Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...
arxiv
🟡 5.5/10
science
reasoning
Key Strengths
  • •Scientific Authority: Peer-reviewed content from established repository
  • •Domain-Specific: Specialized vocabulary and concepts
  • •Mathematical Content: Includes complex equations and notation
Considerations
  • •Specialized: Primarily technical and mathematical content
  • •English-Heavy: Predominantly English-language papers

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Deploymentpythonvllm
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("==========================================")
Deploymentpythonvllm
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("==========================================")
Deploymentpythonvllm
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("==========================================")
Deploymentpythonvllm
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("==========================================")
Deploymentpythonvllm
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("==========================================")
Deploymentpythonvllm
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("==========================================")
Deploymentpythonvllm
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("==========================================")
Deploymentpythonvllm
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("==========================================")
Deploymentpythonvllm
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("==========================================")
Deploymentpythonvllm
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("==========================================")
Deploymentpythonvllm
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("==========================================")
Deploymentpythonvllm
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("==========================================")
Deploymentpythonvllm
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("==========================================")
Deploymentpythonvllm
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("==========================================")
Deploymentpythonvllm
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("==========================================")
Deploymentpythonvllm
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("==========================================")
Creationpythontransformers
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
)
Creationpythontransformers
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
)
Creationpythontransformers
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
)
Creationpythontransformers
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
)
Creationpythontransformers
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
)
Creationpythontransformers
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
)
Creationpythontransformers
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
)
Creationpythontransformers
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
)
Creationpythontransformers
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
)
Creationpythontransformers
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
)
Creationpythontransformers
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
)
Creationpythontransformers
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
)
Creationpythontransformers
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
)
Creationpythontransformers
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
)
Creationpythontransformers
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
)
Creationpythontransformers
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
)

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