neuralmagic/Phi-3-vision-128k-instruct-W4A16-G128

Phi-3-vision-128k-instruct-quantized.w4a16

Model Overview

• Model Architecture: Phi-3-vision-128k-instruct
  • Input: Vision-Text
  • Output: Text
• Model Optimizations:
  • Weight quantization: INT4
  • Activation quantization: FP16
• Release Date: 1/31/2025
• Version: 1.0
• Model Developers: Neural Magic

Quantized version of microsoft/Phi-3-vision-128k-instruct.

Model Optimizations

This model was obtained by quantizing the weights of microsoft/Phi-3-vision-128k-instruct to INT4 data type, ready for inference with vLLM >= 0.5.2.

Deployment

Use with vLLM

This model can be deployed efficiently using the vLLM backend, as shown in the example below.

from vllm.assets.image import ImageAsset
from vllm import LLM, SamplingParams

# prepare model
llm = LLM(
    model="neuralmagic/Phi-3.5-vision-instruct-W4A16-G128",
    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 for more details.

Creation

This model was created with llm-compressor by running the code snippet below as part a multimodal announcement blog.

import torch
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoProcessor

from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.transformers import oneshot

# Load model.
model_id = "microsoft/Phi-3-vision-128k-instruct"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto",
    trust_remote_code=True,
    _attn_implementation="eager",
)
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
processor.chat_template = processor.tokenizer.chat_template

# Calibration dataset arguments
DATASET_ID = "lmms-lab/flickr30k"
DATASET_SPLIT = "test[: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).select(range(NUM_CALIBRATION_SAMPLES))

# Apply chat template and tokenize inputs.
def preprocess_and_tokenize(example):
    messages = [{"role": "user", "content": "<|image_1|>\nWhat does the image show?"}]
    text = processor.apply_chat_template(
        messages,
        add_generation_prompt=True,
    )
    images = example["image"]

    return processor(
        text=text,
        images=images,
        padding=False,
        max_length=MAX_SEQUENCE_LENGTH,
        truncation=True,
    )

ds = ds.map(preprocess_and_tokenize, writer_batch_size=1, remove_columns=ds.column_names)

# 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()}


# Recipe
recipe = GPTQModifier(
    targets="Linear",
    scheme="W4A16",
    sequential_targets=["Phi3DecoderLayer"],
    ignore=["lm_head", "re:model.vision_embed_tokens.*"],
)

# Perform oneshot
SAVE_DIR = model_id.split("/")[1] + "-W4A16-G128"

oneshot(
    model=model,
    processor=processor,
    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
)

License

The model is licensed under the MIT license.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.