---
license: mit
base_model: microsoft/Phi-4-multimodal-instruct
quantization_method: bitsandbytes
quantization_config:
  load_in_4bit: true
  bnb_4bit_quant_type: nf4
  bnb_4bit_compute_dtype: torch.bfloat16
  bnb_4bit_use_double_quant: true
tags:
- phi
- phi-4
- phi-4-multimodal
- multimodal
- quantized
- 4bit
- bitsandbytes
- bubblspace
- Automatic Speech Recognition
language:
- ar
- en
- pl
- zh
- fr
- de
- hu
- sv
- es
- ko
- 'no'
---

# Bubbl-P4-multimodal-instruct (4-bit Quantized)

This repository contains a 4-bit quantized version of the `microsoft/Phi-4-multimodal-instruct` model.

Quantization was performed using the `bitsandbytes` library integrated with `transformers`.

## Model Description

*   **Original Model:** [microsoft/Phi-4-multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct)
*   **Quantization Method:** `bitsandbytes` Post-Training Quantization (PTQ)
*   **Precision:** 4-bit
*   **Quantization Config:**
    *   `load_in_4bit=True`
    *   `bnb_4bit_quant_type="nf4"` (NormalFloat 4-bit)
    *   `bnb_4bit_compute_dtype=torch.bfloat16` (Computation performed in BF16 for compatible GPUs like A100)
    *   `bnb_4bit_use_double_quant=True` (Enables nested quantization for potentially more memory savings)

This version was created to provide the capabilities of Phi-4-multimodal with a significantly reduced memory footprint, making it suitable for deployment on GPUs with lower VRAM.

## Intended Use

This quantized model is primarily intended for scenarios where VRAM resources are constrained, but the advanced multimodal reasoning, language understanding, and instruction-following capabilities of `Phi-4-multimodal-instruct` are desired.

Refer to the [original model card](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) for the full range of intended uses and capabilities of the base model.

## How to Use

You can load this 4-bit quantized model directly using the `transformers` library. Ensure you have `bitsandbytes` and `accelerate` installed (`pip install transformers bitsandbytes accelerate torch torchvision pillow soundfile scipy sentencepiece protobuf`).

```python
from transformers import AutoModelForCausalLM, AutoProcessor
import torch

model_id = "bubblspace/Bubbl-P4-multimodal-instruct"

# Load the processor (requires trust_remote_code)
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

# Load the model with 4-bit quantization enabled
# The quantization config is loaded automatically from the model's config file
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True, # Essential for Phi-4 models
    load_in_4bit=True,     # Explicitly activate 4-bit loading (though config should handle it)
    device_map="auto"      # Automatically map model layers to available GPU(s)
    # torch_dtype=torch.bfloat16 # Often not needed here as bnb_4bit_compute_dtype is handled
)

print("4-bit quantized model loaded successfully!")

# --- Example: Text Inference ---
prompt = "<|user|>\nExplain the benefits of model quantization.<|end|>\n<|assistant|>"
inputs = processor(text=prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=150)
response_text = processor.batch_decode(outputs)[0]
print(response_text)

# --- Example: Image Inference Placeholder ---
# from PIL import Image
# import requests
# url = "your_image_url.jpg"
# image = Image.open(requests.get(url, stream=True).raw)
# image_prompt = "<|user|>\n<|image_1|>\nDescribe this image.<|end|>\n<|assistant|>"
# inputs = processor(text=image_prompt, images=image, return_tensors="pt").to(model.device)
# outputs = model.generate(**inputs, max_new_tokens=100)
# response_text = processor.batch_decode(outputs, skip_special_tokens=True)[0]
# print(response_text)

# --- Example: Audio Inference Placeholder ---
# import soundfile as sf
# audio_path = "your_audio.wav"
# audio_array, sampling_rate = sf.read(audio_path)
# audio_prompt = "<|user|>\n<|audio_1|>\nTranscribe this audio.<|end|>\n<|assistant|>"
# inputs = processor(text=audio_prompt, audios=[(audio_array, sampling_rate)], return_tensors="pt").to(model.device)
# # ... generate and decode ...

```

**Important:** Remember to always pass `trust_remote_code=True` when loading both the processor and the model for Phi-4 architectures.

## Hardware Requirements

*   Requires a CUDA-enabled GPU.
*   The 4-bit quantization significantly reduces VRAM requirements compared to the original BF16 model (approx. 11-12GB). This version should fit comfortably on GPUs with ~10GB VRAM, and potentially less depending on context length and batch size (evaluation recommended).
*   Performance gains (inference speed) compared to the original are most noticeable on GPUs that efficiently handle lower-precision operations (e.g., NVIDIA Ampere, Ada Lovelace series like A100, L4, RTX 30/40xx).

## Limitations and Considerations

*   **Potential Accuracy Impact:** While 4-bit quantization aims to preserve performance, there might be a slight degradation in accuracy compared to the original BF16 model. Users should evaluate the model's performance on their specific tasks to ensure the trade-off is acceptable.
*   **Inference Speed:** Memory usage is significantly reduced. Inference speed may or may not be faster than the original BF16 model; it depends heavily on the hardware, batch size, sequence length, and specific implementation details. Test on your target hardware.
*   **Multimodal Evaluation:** Quantization primarily affects the model weights. Thorough evaluation on specific vision and audio tasks is recommended to confirm performance characteristics for multimodal use cases.
*   **Inherited Limitations:** This model inherits the limitations, biases, and safety considerations of the original `microsoft/Phi-4-multimodal-instruct` model. Please refer to its model card for detailed information on responsible AI practices.

## License

The model is licensed under the [MIT License](LICENSE), consistent with the original `microsoft/Phi-4-multimodal-instruct` model.

## Citation

Please cite the original work if you use this model:

```bibtex
@misc{phi4multimodal2025,
      title={Phi-4-multimodal: A Compact Multimodal Model for Recommendation, Recognition, and Reasoning},
      author={Microsoft},
      year={2025},
      eprint={2503.01743},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

```

Additionally, if you use this specific 4-bit quantized version, please acknowledge **Bubblspace** ([bubblspace.com](https://bubblspace.com)) and **AIEDX** ([aiedx.com](https://aiedx.com)) for providing this quantized model. You could add a note such as:

> *"We used the 4-bit quantized version of Phi-4-multimodal-instruct provided by Bubblspace/AIEDX, available at huggingface.co/bubblspace/Bubbl-P4-multimodal-instruct."*