Meta launches 'Chameleon' to challenge GPT-4o, 34B parameters lead the multi-modal revolution! 10 trillion token training refreshes SOTA

The emergence of GPT-4o once again created a new paradigm for multi-modal model development!

Why do you say that?

OpenAI calls it the "first "native" multi-modal" model, which means that GPT-4o is different from all previous models.

Traditional multi-modal basic models usually use a specific "encoder" or "decoder" for each modality to convert different The modes are separated.

However, this approach limits the model’s ability to effectively fuse cross-modal information.

GPT-4o is the "first end-to-end" trained model that can span text, visual and audio modes. All inputs and outputs are handled by a single neural network deal with.

And now, the industry’s first model that dares to challenge GPT-4o has appeared!

Recently, researchers from the Meta team released the "Mixed Modal Base Model" - Chameleon.

Paper address: https://arxiv.org/pdf/2405.09818

With GPT- Like 4o, Chameleon adopts a unified Transformer architecture and uses text, image and code mixed modes to complete training.

In a manner similar to text generation, the image is discretely "tokenized" (tokenization), and finally generates and infers interleaved text and image sequences.

With this "early fusion" method, all pipelines are mapped to a common representation space from the beginning, so the model can be seamlessly Seam processing of text and images.

Chameleon generated multi-modal content

At the same time, such a design provides a model Training presents significant technical challenges.

In this regard, the Meta research team has introduced a series of architectural innovations and training technologies.

The results show that in plain text tasks, the performance of 34 billion parameter Chameleon (trained with 10 trillion multi-modal tokens) is equivalent to Gemini-Pro.

On visual question answering and image annotation benchmarks, it refreshes SOTA and its performance is close to GPT-4V.

However, both GPT-4o and Chameleon are early explorations of a new generation of "native" end-to-end multi-modal basic models.

At the GTC 2024 conference, Lao Huang described an important step towards the ultimate vision of AGI - the interoperability of various modes.

Is the next open source GPT-4o coming?

The release of Chameleon is simply the fastest response to GPT-4o.

Some netizens said that token goes in and token goes out, which is simply impossible to explain.

Some people even claim that OOS will catch up with the very solid research released after the birth of GPT-4o.

However, currently the Chameleon model supports generated modalities, mainly image text. The speech capabilities in GPT-4o are missing.

Netizens said that then just add another modality (audio), expand the training data set, and "cook" for a while, we will get GPT-4o...?

Meta’s Director of Product Management said, “I’m very proud to support this team. Let’s move towards making GPT-4o closer to the open source community. Take a step forward in that direction.”

Maybe it won’t be long before we get an open source version of GPT-4o.

Next, let’s take a look at the technical details of the Chameleon model.

Technical Architecture

Meta first stated in Chameleon's paper: Many recently released models still do not implement "multimodality" to the end.

Although these models adopt an end-to-end training method, they still model different modalities separately, using separate encoders or decoders.

As mentioned at the beginning, this approach limits the model's ability to cross-modal information, and makes it difficult to generate truly multi-modal documents containing any form of information.

In order to improve this shortcoming, Meta proposed a series of "mixed-modal" base models Chameleon - capable of generating content in which text and image content are arbitrarily intertwined.

Chameleon’s generation results, text and images are interlaced

The so-called "mixed modal" basis The base model means that Chameleon not only uses an end-to-end approach to train from scratch, but also interweaves and mixes information from all modalities during training and processes it using a unified architecture.

How to mix information from all modalities and represent it in the same model architecture?

The answer is still "token".

As long as it is all expressed as tokens, all modal information can be mapped into the same vector space, allowing Transformer to process it seamlessly.

However, this approach will bring technical challenges in terms of optimization stability and model scalability.

In order to solve these problems, the paper innovates the model architecture accordingly and uses some training techniques, including QK normalization and Zloss.

At the same time, the paper also proposes a method of fine-tuning plain text LLM into a multi-modal model.

Image "Token Breaker"

#To represent all modalities as tokens, you first need a powerful tokenizer.

To this end, Chameleon’s team developed a new image segmenter based on a previous paper in Meta. Based on a codebook of size 8192, the specification is 512×512 The image is encoded as 1024 discrete tokens.

The text segmenter is based on the sentencepiece open source library developed by Google, and trained a BPE segmentation containing 65536 text tokens and 8192 image tokens. device.

Pre-training

#In order to fully stimulate the potential of "mixed modalities", the training data is also divided into different modalities. Mixed presentations to the model include plain text, text-image pairs, and multi-modal documents in which text and images interleave.

Plain text data includes all pre-training data used by Llama 2 and CodeLlama, totaling 2.9 trillion tokens.

Text-image pairs contain some public data, totaling 1.4 billion pairs and 1.5 trillion tokens.

Regarding the intertwined data of text and images, the paper specifically emphasizes that it does not include data from Meta products, and completely uses public data sources to sort out a total of 400 billion tokens.

Chameleon’s pre-training is conducted in two separate phases, accounting for 80% and 20% of the total training ratio respectively.

The first stage of training is to let the model learn the above data in an unsupervised way. At the beginning of the second stage, first reduce the weights obtained in the first stage by 50%, and mix and update High-quality data allows the model to continue learning.

When the model expands to more than 8B parameters and 1T tokens, obvious instability problems will occur in the later stages of training.

Since all modalities share model weights, each modality seems to have a tendency to increase norm and "compete" with other modalities.

This will not cause much problems in the early stages of training, but as training progresses and the data exceeds the expression range of bf16, loss will diverge.

The researchers attribute this to the translational invariance of the softmax function, a phenomenon also known as "logit drift" (logit drift).

Therefore, the paper proposes some architectural adjustments and optimization methods to ensure stability:

-QK normalization (query-key normalization ): Apply layer norm to the query and key vectors in the attention module, thereby directly controlling the norm growth of the softmax layer input.

-Introduce dropout after the attention layer and feedforward layer

-Use Zloss regularization in the loss function

In addition to the data source and architecture, the paper also generously disclosed the scale of computing power used in pre-training.

The hardware model is NVIDIA A100 with 80GB memory. The 7B version uses 1024 GPUs in parallel to train for about 860,000 GPU hours. The number of GPUs used in the 34B model has been expanded by 3 times. Over 4.28 million hours.

As a company that once open sourced Llama 2, Meta’s research team is really generous. Compared to GPT-4o, which doesn’t even have a technical report, this article A paper with data and useful information can be said to be "the most benevolent and righteous".

Comprehensively surpassing Llama 2

In the specific experimental evaluation, the researchers divided it into manual evaluation, security testing, and baseline evaluation.

Benchmark Evaluation

Chameleon-34B uses four times more tokens than Llama 2 for training. In various single-modal Stunning results have been achieved in benchmark tests.

In the text-only task generation, the researchers compared the text-only features of the pre-trained (non-SFT) model with other leading text-only LLMs.

The assessment content includes common sense reasoning, reading comprehension, mathematical problems and world knowledge areas. The assessment results are shown in the table below.

-Common Sense Reasoning and Reading Comprehension

It can be observed that compared with Llama 2, Chameleon-7B and Chameleon-34B are more competitive. In fact, 34B even surpassed Llama-2 70B on 5/8 tasks, and its performance was equivalent to Mixtral-8x7B.

- Mathematics and World Knowledge

Despite being trained on other modalities, both Chameleon models Demonstrate strong mathematical skills.

On GSM8k, Chameleon-7B performs better than the Llama 2 model of corresponding parameter scale, and its performance is equivalent to Mistral-7B.

In addition, Chameleon-34B performs better than Llama 2-70B at maj@1 (61.4 vs 56.8) and Mixtral-8x7B at maj@32 (77.0 vs 75.1).

Likewise, in math operations, Chameleon-7B outperforms Llama 2, is on par with Mistral-7B on maj@4, and Chameleon-34B outperforms Llama 2 -70B, close to the performance of Mixtral-8x7B on maj@4 (24.7 vs 28.4).

Overall, the performance of Chameleon exceeds Llama 2 in all aspects and is close to Mistral-7B/8x7B on some tasks.

In the text-to-image task, the researchers specifically evaluated two specific tasks: visual question answering and image annotation.

Chameleon defeated models such as Flamingo and Llava-1.5 to become SOTA in visual question answering and image annotation tasks, and also competed with first-tier models such as Mixtral 8x7B and Gemini Pro in plain text tasks. Performance is comparable.

Manual evaluation and security testing

At the same time, in order to further evaluate the model to generate multi-modal content Regarding quality, the paper also introduced human evaluation experiments in addition to benchmark tests, and found that Chameleon-34B performed far better than Gemini Pro and GPT-4V.

Compared to GPT-4V and Gemini Pro, human judges scored 51.6% and 60.4 preference rates respectively.

The following figure shows the performance comparison of Chameleon and baseline models in understanding and generating content for a diverse set of prompts from human annotators.

Each question is answered by three different human annotators, with the majority vote being the final answer.

To understand the quality of the human annotators and whether the questions were well designed, the researchers also examined the degree of agreement between different annotators.

Table 5 is a security test conducted on 20,000 crowdsourced prompts and 445 red team interactions, Causes the model to produce unsafe content.

Compared with Gemini and GPT-4V, Chameleon is very competitive when dealing with cues that require interleaved, mixed-modal responses.

As you can see from the example, when completing the question and answer task, Chameleon can not only understand the input text + image, but also add appropriate "pictures" to the model output content.

Moreover, the images generated by Chameleon are usually contextual, making the output of this interlaced content very attractive to users.

Contribution team

At the end of the paper, the contributors who participated in this research are also listed.

Includes pre-training, alignment and safety, reasoning and evaluation, and participants for all projects.

Among them, * indicates a joint author, † indicates a key contributor, ‡ indicates the workflow leader, and ♯ indicates the project leader.

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