phi3v_spatial 0.0.1

Spatial VLM for 3D Object Detection and Reasoning

---

license: mit license_link: https://huggingface.co/MbodiAI/phi3v-spatial/resolve/main/LICENSE

language:

• multilingual pipeline_tag: object-detection tags:
• nlp
• code
• vision
• multimodal
• robotics inference: parameters: temperature: 0.7 widget:
  • messages:
    • role: user content: <|image_1|> Can you describe what you see in the image?

---

Phi3V-Spatial

This model exists thanks to the brilliant work from teams at Microsoft, HuggingFace, and countless others.

---

Table of Contents

• Installation
• License

Installation

pip install phi3v-spatial

See the original MIT license here: README.md

Model Summary

Base Model

According to Microsoft:

Phi-3-Vision-128K-Instruct is a lightweight, state-of-the-art open multimodal model built upon datasets which include - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data both on text and vision. The model belongs to the Phi-3 model family, and the multimodal version comes with 128K context length (in tokens) it can support. The model underwent a rigorous enhancement process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures.

Intended Uses

Primary use cases

The model is intended for broad commercial and research use in English. The model provides uses for general purpose AI systems and applications with visual and text input capabilities which require

1. spatial reasoning
2. robotics instruction*
3. memory/compute constrained environments
4. latency bound scenarios
5. general image understanding
6. OCR
7. chart and table understanding

*Robotics control can benefit significantly from high level plans generated by LLMs, VLMs, and other types of multi-modal transformers.

Use case considerations

Phi3v-spatial is not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case.

Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.

How to Use

pip install -e .

Required packages

See pyproject.toml.

See the base Phi-3-Vision-128K-Instruct available in Azure AI Studio.

Chat Format

Given the nature of the training data, the Phi-3-Vision-128K-Instruct model is best suited for a single image input wih prompts using the chat format as follows. You can provide the prompt as a single image with a generic template as follow:

<|user|>\n<|image_1|>\n{prompt}<|end|>\n<|assistant|>\n 

where the model generates the text after <|assistant|> . In case of multi-turn conversation, the prompt can be formatted as follows:

<|user|>\n<|image_1|>\n{prompt_1}<|end|>\n<|assistant|>\n{response_1}<|end|>\n<|user|>\n{prompt_2}<|end|>\n<|assistant|>\n 

Sample inference code

This code snippets show how to get quickly started with running the model on a GPU:

from PIL import Image 
import requests 
from transformers import AutoModelForCausalLM 
from transformers import AutoProcessor 

model_id = "MbodiAI/phi3v-spatial" 

model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto")

processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) 

messages = [ 
    {"role": "user", "content": "<|image_1|>\nWhat is shown in this image?"}, 
    {"role": "assistant", "content": "The chart displays the percentage of respondents who agree with various statements about their preparedness for meetings. It shows five categories: 'Having clear and pre-defined goals for meetings', 'Knowing where to find the information I need for a meeting', 'Understanding my exact role and responsibilities when I'm invited', 'Having tools to manage admin tasks like note-taking or summarization', and 'Having more focus time to sufficiently prepare for meetings'. Each category has an associated bar indicating the level of agreement, measured on a scale from 0% to 100%."}, 
    {"role": "user", "content": "Provide insightful questions to spark discussion."} 
] 

url = "https://assets-c4akfrf5b4d3f4b7.z01.azurefd.net/assets/2024/04/BMDataViz_661fb89f3845e.png" 
image = Image.open(requests.get(url, stream=True).raw) 

prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

inputs = processor(prompt, [image], return_tensors="pt").to("cuda:0") 
generate_ids = model.generate(**inputs, eos_token_id=processor.tokenizer.eos_token_id, **generation_args) 

# remove input tokens 
generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] 

print(response) 

Attention Mechanisms

RoPE Scaling

Purpose: Extend the positional embedding capability of a model by scaling the standard embedding based on specified factors.

Parameters:

• type: Can be either "su" or "yarn".

• short_factor and long_factor: Lists containing scaling factors. The length of these lists should match hidden_size / num_attention_heads / 2.

Example:

rope_scaling = {
    "type": "su",
    "short_factor": [1.0, 1.5, 2.0],
    "long_factor": [1.0, 1.5, 2.0]
}

Here, RoPE scaling adjusts the base period of the rotary embeddings, potentially improving performance on different sequence lengths and tasks.

Sliding Window

Purpose: Implement attention on a moving window basis, which can help in processing long sequences while retaining contextual information within the window. Parameter: sliding_window: Size of the sliding window. Example:

sliding_window = 512
Here, the attention mechanism will focus on 512 tokens at a time, sliding over the sequence for broader context understanding.

Other Architectural Options

Tie Word Embeddings

Purpose: Ensures the input embeddings matrix and the output embeddings matrix share the same weights. Parameter: tie_word_embeddings: A boolean indicating whether to tie the word embeddings. Example:

tie_word_embeddings = True Here, both input and output embeddings will use the same weight matrix, which can reduce the number of parameters and might help in regularization and mitigation of overfitting.

Training

Model

• Architecture: Phi-3-Vision-128K-Instruct has 4.2B parameters and contains image encoder, connector, projector, and Phi-3 Mini language model.
• Inputs: Text and Image. It’s best suited for prompts using the chat format.
• Context length: 128K tokens
• GPUs: 1 H100-80G
• Training time: 1.5 days
• Training data: 500B vision and text tokens
• Outputs: Generated text in response to the input
• Dates: Our models were trained between February and April 2024
• Status: This is a static model trained on an offline text dataset with cutoff date Mar 15, 2024. Future versions of the tuned models may be released as we improve models.
• Release Type: Open weight release
• Release dates: The model weight is released on May 21, 2024.

Base Datasets

Microsoft's training data includes a wide variety of sources, and is a combination of

1. publicly available documents filtered rigorously for quality, selected high-quality educational data and code;
2. selected high-quality image-text interleave;
3. newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.), newly created image data, e.g., chart/table/diagram/slides;
4. high quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.

The data collection process involved sourcing information from publicly available documents, with a meticulous approach to filtering out undesirable documents and images. To safeguard privacy, we carefully filtered various image and text data sources to remove or scrub any potentially personal data from the training data.

More details can be found in the Phi-3 Technical Report.

Fine-Tuned Datasets

We further fine-tuned this model on:

1. vsr
2. vqa_synth
3. cauldron
4. newly created synthetic data using techniques that extend the work of Spatial VLM, ROSIE
5. newly created synthetic data by augmenting publicly available robotics datasets.

Benchmarks

To understand the capabilities, we compare Phi-3-Vision-128K-Instruct with a set of models over a variety of zero-shot benchmarks using our internal benchmark platform.

Benchmark	Phi-3 Vision-128K-In	LlaVA-1.6 Vicuna-7B	QWEN-VL Chat	Llama3-Llava-Next-8B	Claude-3 Haiku	Gemini 1.0 Pro V	GPT-4V-Turbo
MMMU	40.4	34.2	39.0	36.4	40.7	42.0	55.5
MMBench	80.5	76.3	75.8	79.4	62.4	80.0	86.1
ScienceQA	90.8	70.6	67.2	73.7	72.0	79.7	75.7
MathVista	44.5	31.5	29.4	34.8	33.2	35.0	47.5
InterGPS	38.1	20.5	22.3	24.6	32.1	28.6	41.0
AI2D	76.7	63.1	59.8	66.9	60.3	62.8	74.7
ChartQA	81.4	55.0	50.9	65.8	59.3	58.0	62.3
TextVQA	70.9	64.6	59.4	55.7	62.7	64.7	68.1
POPE	85.8	87.2	82.6	87.0	74.4	84.2	83.7

Software

• PyTorch
• Transformers
• Flash-Attention

Hardware

N

• NVIDIA A100
• NVIDIA A6000
• NVIDIA H100

Running on Windows or without flash attention

To enable the model on these enviroment here are steps that you may consider to follow:

Step 1: comment flash attention import code in modeling_phi3_v.py from line 52 to line 56.

# if is_flash_attn_2_available():
#     from flash_attn import flash_attn_func, flash_attn_varlen_func
#     from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa

#     _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)

Step 2: change _"attn_implementation" from "flash_attention_2" to "eager" in config.json or disable flash attention when you create the model as below.

model = AutoModelForCausalLM.from_pretrained('microsoft/Phi-3-vision-128k-instruct', device_map="cuda", trust_remote_code=True, torch_dtype="auto", _attn_implementation="eager")

Microsoft's Responsible AI Considerations

Like other models, the Phi family of models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:

• Quality of Service: The Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English.
• Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases.
• Inappropriate or Offensive Content: These models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case.
• Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated.
• Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.

Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include:

• Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques.
• High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context.
• Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG).
• Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case.
• Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.
• Identification of individuals: models with vision capabilities may have the potential to uniquely identify individuals in images. Safety post-training steers the model to refuse such requests, but developers should consider and implement, as appropriate, additional mitigations or user consent flows as required in their respective jurisdiction, (e.g., building measures to blur faces in image inputs before processing.