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Meta Open Sources Code Llama 70B

CodeL lama 70B, the latest and most powerful iteration of our open-source language model for code generation. That’s right, we’re not only pushing the boundaries of AI-powered coding, but making it freely accessible. With improved performance over previous iterations, Code Llama 70B is now available under the same permissive license as prior Code Llama releases.

Notably, Code Llama 70B achieves over 67.8% on the HumanEval benchmark, reaching performance on par with GPT-4.

This isn’t just a bigger engine under the hood – it’s a leap forward in code-wrangling capabilities. Code Llama 70B boasts significant performance improvements on key benchmarks, meaning you can say goodbye to tedious boilerplate and hello to lightning-fast generation, smarter autocompletion, and even tackling diverse programming languages.

Code LLama 70B comes in three distinct versions:

  • CodeLlama-70B: Your all-around powerhouse for general code generation across multiple languages.
  • CodeLlama-70B-Python: Tailored for the Python-specific tasks.
  • CodeLlama-70B-Instruct: Fine-tuned instruct version.

So, ready to unleash the Code Llama 70B in your projects? Buckle up, grab your access key, and prepare to experience the future of coding, where the only limit is your imagination. Dive deeper in the following sections to explore the model’s capabilities, access instructions, and see how Code Llama 70B can turbocharge your workflow. The future of code is open, and it’s here to stay.

Get ready to code smarter, not harder, with Code Llama 70B.

Decoding Code Llama 70B: Under the Hood

Let’s examine the engine driving Code Llama 70B. This section dives into the technical details, giving you a peek at the brains behind the magic.

Model Core:

  • Parameter Powerhouse: This version boasts a whopping 70 billion parameters, allowing it to process and generate complex code structures with stunning accuracy. Reportedly trained on 500B tokens.
  • Input/Output Dance: It takes plain text as input, weaving its insights into eloquent lines of code as output. Think of it as your AI translator for turning natural language into programming magic. Code llama 70B has a 16384 context window.
  • Transformer Tango: The model’s architecture leans on the optimized transformer approach, a well-established technique in the LLM world known for its flexibility and power.

Fine-Tuning for Focus:

  • Scale Matters: Code Llama 70B was fine-tuned on massive datasets with up to 16,000 tokens, ensuring it’s familiar with diverse coding structures and styles.
  • Supersized Inference: During inference, it can handle even larger chunks of code, up to 100,000 tokens, making it ideal for tackling complex projects and intricate problems.
  • Model Dates Code Llama and its variants have been trained between January 2023 and January 2024.

A Look Ahead:

  • Static Stability: This is a static model, trained on a carefully curated dataset. This ensures consistency and predictability in its outputs.
  • Community-Driven Evolution: Future versions of Code Llama – Instruct will focus on enhancing safety and refining its abilities. We’ll be actively incorporating community feedback into this process, so stay tuned for even more exciting developments!

Testing the model

Thanks to Perplexity, we can test the open source model through the Perplexity Labs feature. Here I wanted to ask Code Llama a random hard Leetcode question, one that hopefully is not in its trainnig data. I asked it question 2179. Count Good Triplets in an Array. Here is the prompt I gave it:

You are given two 0-indexed arrays nums1 and nums2 of length n, both of which are permutations of [0, 1, ..., n - 1].

A good triplet is a set of 3 distinct values which are present in increasing order by position both in nums1 and nums2. In other words, if we consider pos1v as the index of the value v in nums1 and pos2v as the index of the value v in nums2, then a good triplet will be a set (x, y, z) where 0 <= x, y, z <= n - 1, such that pos1x < pos1y < pos1z and pos2x < pos2y < pos2z.

class Solution:
    def goodTriplets(self, nums1: List[int], nums2: List[int]) -> int:

Notice I gave the prompt the main part of the question along with the start code, I did not mention the Leetcode number or question.

This the code result:

class Solution:
    def goodTriplets(self, nums1: List[int], nums2: List[int]) -> int:
        n = len(nums1)
        count = 0
        for i in range(n):
            for j in range(i + 1, n):
                for k in range(j + 1, n):
                    if nums1[i] < nums1[j] < nums1[k] and nums2[i] < nums2[j] < nums2[k]:
                        count += 1
        return count

The code compiled, but it did not pass all the tests.. I then gave it a follow up question, “try again, think step by step”. It still gave the wrong answer. I then gave the same prompt to Mistral Medium and returned this.

class Solution:
    def goodTriplets(self, nums1: List[int], nums2: List[int]) -> int:
        n = len(nums1)
        # Create two arrays to store the position of each element in nums1 and nums2
        pos1 = [0] * n
        pos2 = [0] * n
        for i in range(n):
            pos1[nums1[i]] = i
            pos2[nums2[i]] = i

        # Initialize the count of good triplets to 0
        count = 0
        # Iterate through all possible triplets
        for i in range(n):
            for j in range(i+1, n):
                for k in range(j+1, n):
                    # Check if the triplet is good
                    if pos1[i] < pos1[j] < pos1[k] and pos2[i] < pos2[j] < pos2[k]:
                        count += 1
        return count

Still, neither got the question right,, maybe with better prompting or showing examples might have helped. But let’s also note that Leetcode isn’t the best way to determine a language model’s coding abilities.

Code LLama 70B licensing

Code Llama 70B is free and open source as well as available for commercial use.

Nous-Hermes-2 Mixtral 8x7B: New Flagship LLM

Nous Research has just unveiled its latest and most impressive creation to date—the Nous-Hermes-2 Mixtral 8x7B. This groundbreaking flagship Large Language Model (LLM) represents a significant leap forward, being the company’s first model to be fine-tuned using Reinforcement Learning from Human Feedback (RLHF). It’s also the first to surpass the renowned Mixtral Instruct across a wide array of popular benchmarks, setting a new standard for AI performance.

Today marks the release of two distinct configurations of Nous-Hermes-2: the SFT (Supervised Fine-Tuning) only model and the enhanced SFT+DPO (Decentralized Policy Optimization) model, alongside a qlora adapter designed specifically for the DPO variant. Both models are now available to the public via HuggingFace, offering users the unique opportunity to test and determine the best fit for their specific applications.

Advancements in Nous-Hermes-2

Benchmarks

Here’s how it compares to Mixtral Instruct.

From Twitter, an example of the model writing code for data visualization:

Model Configurations

The Mixtral 8x7B model, Nous-Hermes-2, comes in two variants: SFT+DPO and SFT-Only. The SFT only model refers to the model with only the Sparse Fine-Tuning (SFT) technique applied, while the SFT+DPO model includes both the Sparse Fine-Tuning and the Data Parallelism Optimization (DPO) techniques. These two configurations allow users to choose between the SFT only or the combined SFT+DPO model based on their specific requirements and performance preferences

They also we released a QLoRA Adapter that can be attached or merged to any Mixtral Based model to potentially get the benefits of our DPO Training on other Mixtral Finetunes maybe even he base model. This likely means you can potentially improve the performance of fine-tuning other Mixtral models by adding the QLoRA Adapter, even if you’re not using the SFT+DPO variant of the Mixtral 8x7B model.

Conclusion

The advent of Nous-Hermes-2 Mixtral 8x7B marks a milestone in the progress of open-source AI, illustrating the rapid advancements being made each day. This significant release from the Nous team not only meets but surpasses the capabilities of the best open-source model on the market. With its superior performance in 10-shot MMLU, it sets a new bar for the industry, and while showcasing 5-shot MMLU would have been a valuable addition, the current achievements are no less impressive. In my experience, the DPO version seems better.

The model’s use of ChatML as the prompt format and the integration of system prompts for steerability highlight the forward-thinking approach of Nous Research. This not only enhances the model’s versatility but also makes it incredibly user-friendly. The seamless transition for developers and researchers currently using OpenAI APIs to Nous-Hermes-2 is a testament to the thoughtful engineering and user-centric design of the new model.

It’s clear that the gap between proprietary and open-source AI solutions is narrowing with each passing day. The Nous team’s commitment to innovation and openness is not just commendable but a driving force in the democratization of AI technology. Users across the globe can now harness the power of cutting-edge language models, thanks to the relentless efforts of researchers and developers in pushing the boundaries and expanding what’s possible in the realm of AI. With Nous-Hermes-2, the future of open-source AI looks brighter than ever.

Mixtral 8x7B outperforms or matches Llama 2 70B and GPT-3.5 across various benchmarks.

In the ever-evolving landscape of natural language processing, the pursuit of more powerful and versatile language models has led to remarkable breakthroughs. Among these, Mixtral 8x7B stands tall as a Sparse Mixture of Experts (SMoE) language model, showcasing a paradigm shift in performance and efficiency. This cutting-edge model, built upon the foundation of Mistral 7B, introduces a novel architecture with eight feedforward blocks (experts) per layer, revolutionizing the way tokens are processed.

With a keen focus on optimizing parameter usage, Mixtral 8x7B provides each token access to an impressive 47 billion parameters, all while utilizing a mere 13 billion active parameters during inference. Its unique approach, where a router network dynamically selects two experts for each token at every layer, allows for unparalleled adaptability and responsiveness.

Under the Hood: Mixtral 8x7B Architecture

Mixtral 8x7B is a Sparse Mixture of Experts (SMoE) language model that has the same architecture as Mistral 7B, with the difference that each layer is composed of 8 feedforward blocks (experts). For every token, at each layer, a router network selects two experts to process the current state and combine their outputs. Even though each token only sees two experts, the selected experts can be different at each timestep. As a result, each token has access to 47B parameters, but only uses 13B active parameters during inference.

Mixtral was trained with a context size of 32k tokens and it outperforms or matches Llama 2 70B and GPT-3.5 across all evaluated benchmarks. In particular, Mixtral vastly outperforms Llama 2 70B on mathematics, code generation, and multilingual benchmarks. The model architecture parameters are summarized in Table 1, and a comparison of Mixtral with Llama is provided in Table 2. Mixtral outperforms or matches Llama 2 70B performance on almost all popular benchmarks while using 5x fewer active parameters during inference.

Here’s why Mixtral is special:

  • It’s very good at different tasks like math, coding, and languages.
  • It uses less power than other similar models because it doesn’t have to use all its experts all the time.
  • This makes it faster and more efficient.

Think of it like this:

  • You need to solve a math problem and a coding problem.
  • Mixtral picks the math expert for the math problem and the coding expert for the coding problem.
  • They both work on their tasks and give you the answers, but you only talk to them one at a time.
  • Even though you don’t see all 8 experts all the time, they’re all ready to help if needed.

Benchmark Performances

The benchmark performances of the Mixtral 8x7B model, a Sparse Mixture of Experts (SMoE) language model, are compared to Llama 2 70B and GPT-3.5 across various tasks. Mixtral outperforms or matches Llama 2 70B and GPT-3.5 on most benchmarks, particularly in mathematics, code generation, and multilingual understanding.

It uses a subset of its parameters for every token, allowing for faster inference speed at low batch-sizes and higher throughput at large batch-sizes. Mixtral’s performance is reported on tasks such as commonsense reasoning, world knowledge, reading comprehension, math, and code generation. It is observed that Mixtral largely outperforms Llama 2 70B on all benchmarks, except on reading comprehension benchmarks, while using 5x fewer active parameters. Detailed results for Mixtral, Mistral 7B, Llama 2 7B/13B/70B, and Llama 1 34B2 are provided, showing that Mixtral outperforms or matches Llama 2 70B performance on almost all popular benchmarks while using 5x fewer active parameters during inference.

Impressive Retrieval

The retrieval accuracy of the Mixtral model is reported to be 100% regardless of the context length or the position of the information in the sequence. The model is able to successfully retrieve information from its context window of 32k tokens, regardless of the sequence length and the location of the information in the sequence.

Licensing and Open Source Community

Mixtral 8x7B is a Sparse Mixture of Experts (SMoE) language model that is licensed under the Apache 2.0 license, making it free for academic and commercial usage, ensuring broad accessibility and potential for diverse applications. The model is released with open weights, allowing the community to run Mixtral with a fully open-source stack. The French startup recently raised $415M in venture funding and has one of the fastest-growing open-source communities.

It’s worth noting that the details regarding the data used for pre-training and the specific loss function employed are conspicuously absent from the available information. This omission leaves a gap in our understanding of the model’s training process. There is no mention of whether any additional loss for load balancing is being utilized, which could provide valuable insights into the model’s optimization strategy and robustness. Despite this gap, the outlined architectural and performance characteristics of Mixtral 8x7B offer a compelling glimpse into its capabilities and potential impact on the field of natural language processing.

Microsoft Fully Open Sources Phi-2

Microsoft has announced that Phi-2, its highly regarded Transformer model, will now be completely open source under the MIT License. This is a groundbreaking development that promises to usher in a new era of innovation and exploration within the field.

What is Phi-2?

Phi-2 is a state-of-the-art Transformer model boasting a whopping 2.7 billion parameters. It’s built for handling a variety of NLP tasks and was trained with an extensive dataset comprising 250 billion tokens, sourced from a combination of NLP synthetic data and carefully filtered web data.

Key Features of Phi-2:

  • Transformer Model: Phi-2 operates on the transformer architecture, renowned for its effectiveness in processing sequential data and powering major advancements in natural language processing. Despite having only 2.7 billion parameters, Phi-2 has demonstrated strong performance on various benchmarks, often surpassing larger models. This suggests that it might offer a good balance of performance and efficiency.
  • Massive Dataset: Phi-2 was trained on a massive dataset of 250 billion tokens, which includes both synthetic and real-world data. This diversity of data helps the model learn a broader range of language patterns and styles.
  • **QA, Chat, and Code**: Specifically designed to perform well with QA formats, chat formats, and code generation, Phi-2 is versatile in its application.
  • Research-Oriented: The model has not been fine-tuned with reinforcement learning from human feedback, positioning it as an ideal candidate for pure research purposes.

A Leap Towards Open Innovation

The recent shift to an MIT License for Phi-2 signifies a momentous occasion for developers, researchers, and hobbyists alike. Open-source licensing removes barriers to access, allowing for greater collaboration and transparency in research and development efforts.

What the MIT License Means for Phi-2:

  • Unrestricted Access: Developers can use, modify, and distribute the model with fewer legal implications, fostering an environment of open innovation.
  • Community Contributions: The open-source community can now contribute to Phi-2’s development, potentially accelerating improvements and enhancements.
  • Wider Adoption: With fewer restrictions, Phi-2 could see increased utilization across various projects and domains, leading to a better understanding of its capabilities and limitations.

Outperforming the Competitors

In my weeks of exploration, it’s become evident that Phi-2 stands out among its peers. Compared with smaller models like the Gemini Nano 2, Phi-2 has shown superior performance on common benchmarks such as MMLU (MultiModal Language Understanding) and BBH (Beyond the Benchmark Hub).

As the AI community starts to leverage the now open-sourced Phi-2, the potential to bridge the performance gap with larger models on complex tasks and reasoning becomes more tangible. The added MIT License is set to catalyze innovation, paving the way for new breakthroughs in the utility and efficiency of AI models like Phi-2.

Conclusion: A New Chapter for AI Research

The decision by Microsoft to fully open source Phi-2 under the MIT License marks a pivotal point in AI research. By lowering the barriers to entry, Microsoft is not only promoting transparency but also empowering a broad range of researchers and developers to contribute to the advancement of AI.

Stay tuned, as I continue to delve into Phi-2’s capabilities and prepare to release an extensive guide that will complement our series of publications. The future of AI research has never looked brighter, and with tools like Phi-2 readily available, the possibilities are endless. Join us in exploring this remarkable model and become a part of the next wave of AI innovation!

Midjourney releases v6

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The world of AI art generation takes a leap forward with Midjourney’s latest release. Version 6 of this popular tool provides creators with greater control, detail, and creativity. In this groundbreaking update, Midjourney empowers users with longer prompt lengths, finer control over elements like color and shading, the ability to incorporate text, and more conversational fine-tuning.

v6 represents a major milestone for Midjourney as it aims to stay ahead of stiff competition from the likes of DALL-E 3 and other AI image generators. While these alternatives offer impressive features, Midjourney’s focus remains on artistic quality and user experience. This update even allows Midjourney to comprehend nuanced differences in punctuation and grammar to render prompts more accurately.

v6 gives creators the improved tools they need to bring their imaginative visions to life. With enhanced understanding of prompts and an expanded set of artistic capabilities, the possibilities are brighter than ever for Midjourney users to push boundaries in AI-assisted art. We can’t wait to see the beautiful, weird, and wonderful images this latest innovation inspires.

Midjourney takes leap forward with latest release

MidJourney has taken a significant leap forward with its latest release, version 6. This new release includes several notable improvements, such as a longer prompt length, more granular control over color and shading, the ability to add text to images, and the capability to fine-tune the output through a conversation with the AI. One of the most striking updates is the AI’s improved understanding of prompts, including nuances in punctuation and grammar. Additionally, MidJourney v6 is available through Discord, and access to a web version is being opened for users who have generated more than 10,000 pictures. The images generated by MidJourney v6 exhibit greater detail and realism compared to the previous version, showcasing the significant progress made in image generation capabilities.The latest release of MidJourney, version 6, brings several advancements, including:

  • Longer prompt length
  • More granular control over color and shading
  • Ability to add text to images
  • Improved understanding of prompts, including nuances in punctuation and grammar
  • Accessible through Discord, with the possibility of a web version for users who have generated more than 10,000 pictures

The images generated by MidJourney v6 demonstrate enhanced detail and realism compared to the previous version, reflecting a substantial advancement in image generation capabilities.

Midjourney v6 provides Greater Control, Detail & Creativity

The Midjourney v6 model offers several improvements over its predecessor, v5. These include much more accurate prompt following, longer prompts, improved coherence, and model knowledge. Additionally, v6 features improved image prompting and remix mode, as well as minor text drawing ability. The upscalers in v6 have both ‘subtle’ and ‘creative’ modes, which increase resolution by 2x. The model also supports various features and arguments at launch, such as –ar, –chaos, –weird, –tile, –stylize, and –style raw. However, some features are not yet supported but are expected to be added in the coming month.

Prompting with v6 is significantly different than v5, as the model is much more sensitive to the prompt. Users are advised to be explicit about what they want, as v6 is now much better at understanding explicit prompts. Lower values of –stylize (default 100) may have better prompt understanding, while higher values (up to 1000) may have better aesthetics. The model is available for alpha testing and is expected to be available for billable subscribers soon. It’s important to note that v6 is an alpha test, and things will change frequently and without notice as the model is taken to full release. The engineering team has also increased the moderation systems to enforce community standards with increased strictness and rigor. Overall, v6 represents a significant advancement in the capabilities of the Midjourney model, offering greater control, detail, and creativity in generating imagery.

Midjourney v6 Can Now do Text

Here is a tweet of a side-by-side comparison with DALL-E 3, which debuted earlier this year with the ability to add text

Final Thoughts

MidJourney’s latest marvel, is undeniable that v6 stands as a massively impressive leap in AI-powered image generation. Although v6’s rollout took longer than previous iterations of MidJourney, the patience of its user base has been rewarded with a suite of robust features that solidify the platform’s place at the forefront of digital artistry.

It’s important to note that despite this release being groundbreaking, it is still in its Alpha phase. This means that what we see today is merely the beginning of v6’s journey. The platform is ripe for further refinement and enhancements, promising an even more polished and versatile tool for creators in the near future.

Currently, MidJourney continues to operate primarily through Discord, maintaining its unique approach. Also access is exclusive to those with a subscription, emphasizing its premium position in a market where the democratization of AI art is becoming increasingly significant.

MidJourney’s v6 stands not only as a testament to the progress of AI technology but also as an invitation to artists and enthusiasts alike to engage with the future of creativity. Its delayed but substantial delivery hints at a thoughtful developmental process, one that prioritizes quality and user experience. As the platform continues to evolve and respond to user feedback, we can anticipate v6 to mature into an even more refined version, further revolutionizing the way we conceive, interact with, and ultimately manifest our creative ideas into visual realities.

Stable Zero123: Pushing the Boundaries of 3D Object Generation

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Stability AI has unveiled its latest breakthrough in AI-generated 3D imagery – Stable Zero123. This new model sets a new high bar for creating photorealistic 3D renderings of objects from a single input image.

Stable Zero123 leverages three key innovations to achieve superior image quality compared to previous state-of-the-art models like Zero123-XL. First, the team curated a high-quality dataset from Objaverse, filtering out low-quality 3D objects and re-rendering the remaining objects with enhanced realism. Second, the model is provided with estimated camera angle data during training and inference, allowing it to generate images with greater precision. Finally, optimizations like pre-computed latents and an improved dataloader enabled much more efficient training, with a 40X speed-up over Zero123-XL.

Early tests show Stable Zero123 generates remarkably vivid and consistent 3D renderings across various object categories. Its ability to extrapolate realistic 3D structure from limited 2D image cues highlights the rapid progress in this blossoming field. With further advancements, AI-assisted 3D model creation could soon become indispensable across industries like gaming, VR, and 3D printing.

Enhanced Training Dataset

The Enhanced Training Dataset for the Stable Zero123 model is based on renders from the Objaverse dataset, utilizing an enhanced rendering method. The model is a latent diffusion model and was trained on the Stability AI cluster on a single node with 8 A100 80GBs GPUs. The training dataset and infrastructure used are specific to the development of the Stable Zero123 model.

Applications and Impact

The enhancements unveiled in Stable Zero123 could have wide-ranging impacts across several industries that rely on 3D digital content. Sectors like gaming and VR are constantly pushing the boundaries of realism in asset creation, and Stable Zero123’s ability to extrapolate intricate 3D models from basic 2D sketches could significantly accelerate development timelines. More consumer-focused applications like 3D printing may also benefit, as users can quickly iterate through design ideas without intensive modeling expertise.

Perhaps most promising is Stable Zero123’s potential to democratize advanced 3D creation capabilities. While photorealistic CGI rendering currently requires specialized skills and tools, Stable Zero123 provides a glimpse of more automated workflows. If ongoing research continues to enhance these generative AI systems, nearly anyone may soon possess the powers of professional 3D artists at their fingertips. Brand-new creative possibilities could emerge when designers and artists of all skill levels can experiment rapidly with 3D concepts that once seemed unattainable. In the near future, Stable Zero123’s innovations could unlock newfound productivity and imagination across industries.

Conclusion

With the launch of Stable Zero123, Stability AI continues its relentless pace of innovation in AI-generated media. Coming on the heels of breakthroughs like Stable Diffusion for image generation and Stable Diffusion Video for text-to-video creation, Stability AI is establishing itself as a leading force in this rapidly evolving landscape. Stable Zero123 delivers their most impressive achievement yet in photorealistic 3D model generation from limited 2D inputs.

The enhancements in data curation, elevation conditioning, and training efficiency have enabled unprecedented image quality leaps over previous state-of-the-art models. As Stability AI continues to push boundaries, applications spanning gaming, VR, 3D printing, and more may see transformative productivity gains from AI-assisted content creation. If progress maintains this velocity, the future looks bright for next-generation creative tools that capture imaginations and unlock new possibilities. Stable Zero123 provides a glimpse into this exciting frontier, where AI equips people across skill levels with once-unfathomable 3D creation superpowers. You can check out the weights on Huggingface.

Mistral AI Shares Open Source LLM via Torrent Link

In a move that turned heads and sparked instant debate, open-source model startup Mistral AI released their latest LLM not with a splashy launch event or polished press release, but with a simple tweet containing a single link: a magnet URL for a massive torrent file.

This audacious approach stands in stark contrast to the carefully orchestrated media blitz that accompanied Google’s recent Gemini launch, or the “over-rehearsed professional release video talking about a revolution in AI” that OpenAI’s Andrej Karpathy mocked on social media. While other companies were busy crafting narratives and highlighting their technological prowess, Mistral simply dropped the mic with a torrent link.

The LLM in question, MoE 8x7B, has generated immediate buzz within the AI community. Described by some as a “scaled-down GPT-4,” it’s believed to be a Mixture of Experts model with 8 individual experts, each possessing 7 billion parameters. This architecture mirrors what we know of GPT-4, albeit with significantly fewer parameters.

This bare-bones release, devoid of any formal documentation or promotional materials, is characteristic of Mistral AI. As AI consultant and community leader Uri Eliabayev noted, “Mistral is well-known for this kind of release, without any paper, blog, code or press release.” While some may find this approach unconventional, it has undoubtedly generated a significant amount of attention and speculation. As open source AI advocate Jay Scambler aptly put it, “It’s definitely unusual, but it has generated quite a bit of buzz, which I think is the point.”

Whether this unorthodox approach marks a new era of open-source AI development remains to be seen. However, one thing is certain: Mistral AI has succeeded in capturing the imagination of the AI community, and their enigmatic release has sparked important conversations about transparency, accessibility, and the future of large language models.

Details of the Release

In a tweet Mistral drops a torrent link containing 8x 7B MoE model but, there were no further details.

Torrent Info

Mistral AI provided minimal details about the release of MoE 8x7B, opting for a cryptic tweet containing only a torrent link. However, some insights can be gleaned from the limited information available.

Key Takeaways:

  • Model Parameters: The params.json file reveals several key parameters:
    • Hidden dimension: 14336 (3.5x expansion)
    • Dimension: 4096
    • Number of heads: 32 (4x multiquery)
    • Number of KV heads: 8
    • Mixture of Experts (MoE): 8 experts, top 2 used for inference
  • Related Code: While no official code for MoE 8x7B is available, the GitHub repository for megablocks-public likely contains relevant code related to the model’s architecture.
  • Noticeably Absent: Unlike many other LLM releases, MoE 8x7B was not accompanied by a polished launch video or press release.

These details suggest that MoE 8x7B is a powerful LLM with a unique architecture. The MoE approach allows for efficient inference by utilizing only the top 2 experts for each token, while still maintaining high performance. The 3.5x expansion of the hidden dimension and 4x multiquery further enhance the model’s capabilities.

The timing of the release, just before the NeurIPS conference, suggests that Mistral AI may be aiming to generate interest and discussion within the AI community. The absence of a traditional launch event is likely intentional, as it aligns with Mistral’s more open-source and community-driven approach.

While the lack of detailed information may leave some wanting more, it also fosters a sense of mystery and excitement. This unorthodox approach has undoubtedly captured the attention of the AI community, and we can expect to see further analysis and experimentation with MoE 8x7B in the coming weeks and months.

Potential Parameters

A JSON file containing parameters for a large language model (LLM) called MoE 8x7B.

The parameters in the JSON file provide some insights into the architecture of MoE 8x7B. The hidden dimension is 14336, which is 3.5 times larger than the dimension of 4096. This suggests that MoE 8x7B is a very powerful LLM with a high degree of complexity.

The number of heads is 32 and the number of KV heads is 8. This indicates that MoE 8x7B uses a multiquery attention mechanism, which allows it to process multiple input sequences simultaneously.

The MoE architecture is a type of Mixture of Experts model, which means that it consists of multiple experts, each specialized to perform a different task. In the case of MoE 8x7B, there are 8 experts. Only the top 2 experts are used for inference, which allows for efficient computing and high performance.

Basically, the parameters in the JSON file suggest that MoE 8x7B is a cutting-edge LLM with a unique architecture. It is still too early to say how well MoE 8x7B will perform on real-world tasks, but it is certainly a promising development in the field of AI.

What could this mean for the future of AI?

The release of MoE 8x7B demonstrates a few important trends in the field of AI:

  • The increasing importance of open source software. MoE 8x7B is an open-source LLM, which means that anyone can download and use it for free. This is a significant development, as it democratizes access to powerful AI technology.
  • The rise of new LLM architectures. MoE 8x7B is a Mixture of Experts model, which is a relatively new type of LLM architecture. This suggests that the field of LLM research is still evolving and that there is still significant room for innovation.
  • The increasing focus on efficiency and performance. MoE 8x7B is designed to be efficient and performant, even when used on resource-constrained devices. This is important for enabling the use of LLMs in real-world applications.

This release of MoE 8x7B is a positive development for the future of AI. It demonstrates the power of open source software, the rise of new LLM architectures, and the increasing focus on efficiency and performance. It is likely that we will see more and more innovative and powerful LLMs being released in the coming years, and MoE 8x7B is a clear example of this trend.

Google Releases Gemini now Powered in Bard

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Move over, ChatGPT, there’s a new AI in town, and it’s packing serious heat. Google has officially released Gemini, its latest and most powerful language model, and it’s already powering the beloved Bard chatbot. This marks a significant turning point in the world of AI, with Gemini poised to revolutionize the way we interact with machines and unlock new possibilities for creativity, productivity, and understanding.

Imagine an AI that can effortlessly translate languages, write captivating stories, decode complex code, and answer your most burning questions with insightful accuracy. That’s the promise of Gemini, and it’s a promise that’s finally become reality. This isn’t just another incremental upgrade; it’s a quantum leap forward, pushing the boundaries of what AI can achieve.

So, buckle up and prepare for a thrilling ride as we explore the exciting world of Google’s Gemini and its transformative potential for the future. Get ready to discover how Bard, now fueled by this revolutionary technology, is poised to become the ultimate AI companion, empowering you to unlock your own creativity and achieve the seemingly impossible.

Exploring Gemini

What is Gemini?

Google Gemeni is an AI model developed by Google DeepMind. Gemini is designed for multimodality, allowing it to reason across text, images, video, audio, and code. It has been evaluated on various tasks and has surpassed the performance of previous state-of-the-art models. It is described as the most capable and largest model for highly complex tasks, with the ability to generate code, combine text and images, and reason visually across languages. It is also available in three sizes: Ultra, Pro, and Nano, each suited for different types of tasks. The search result provides a visual and descriptive representation of Gemini’s functionality and invites users to explore its prompting techniques and capabilities further.

Key features and models

  1. Multimodality: Gemini is built from the ground up for multimodality, allowing it to reason seamlessly across text, images, video, audio, and code.
  2. Performance: Gemini has surpassed the state-of-the-art (SOTA) performance on all multimodal tasks, making it one of the most capable AI models available.
  3. Capability Benchmark: Gemini has been evaluated on various benchmarks, including general language understanding, reasoning, reading comprehension, commonsense reasoning, math, code generation, and natural language understanding, among others.
  4. Multimodal Capabilities: Gemini’s multimodal capabilities include representation of questions in various subjects, diverse set of challenging tasks requiring multi-step reasoning, reading comprehension, commonsense reasoning for everyday tasks, math problems, code generation, and natural language understanding, among others.
  5. Different Sizes: Gemini comes in three sizes – Ultra, Pro, and Nano, each catering to different use cases. The Ultra model is the most capable and largest model for highly complex tasks, the Pro model is best for scaling across a wide range of tasks, and the Nano model is the most efficient for on-device tasks.
  6. Native Multimodality: Gemini is natively multimodal, which means it has the potential to transform any type of input into any type of output, making it a versatile and powerful AI model.

Gemini is a highly advanced AI model with unmatched multimodal capabilities, performance, and versatility, making it a significant advancement in the field of artificial intelligence.

Different Sizes

ModelDescription
UltraOur most capable and largest model for highly-complex tasks.
ProOur best model for scaling across a wide range of tasks.
NanoOur most efficient model for on-device tasks.

Ultra: The most powerful and sophisticated model, designed for highly complex tasks. It serves as the benchmark for our other models and pushes the boundaries of AI performance. As far as we know there is still no official release date yet.

Pro: This versatile model excels at scaling across a broad spectrum of tasks, making it the backbone of Bard. It delivers a powerful AI experience for Bard users today.

Nano: A smaller model that is optimized for mobile use.

Gemini in Action

Benchmarks

Google’s new Gemini AI releases benchmarks The big deal is that it appears to be the first model to beat GPT-4. The fascinating thing is that it does it by just a tiny bit. It is now integrated into Bard now but I haven’t seen an immediate difference. More when I can test it

Testing

For testing I asked Bard a relatively recent Leetcode question, this way we can avoid it being in the training data. I asked it Leetcode question 2859, Sum of Values at Indices With K Set Bits.

Here is the solution it gave me, albeit it was an easy question

Final Thoughts

Google’s release of Bard powered by the conversational AI model Gemini shows their continued commitment to pushing the boundaries of artificial intelligence technology. While there are still open questions around when the more advanced Gemini Ultra could be available and whether it will be free to use, Bard already demonstrates impressive language capabilities.

The launch comes at an interesting time as Google aims to regain some of its reputation as an AI leader after open source libraries like PyTorch, Llama, XGBoost have challenged the dominance of Google’s TensorFlow and Keras. With companies like Anthropic, Meta, and OpenAI also showcasing powerful new AI models recently, the competition in the space keeps heating up.

Ultimately, this increased competition should drive more innovation which is a win for consumers. Google is betting that Bard and the underlying Gemini framework will allow them to deliver more helpful, safe, and grounded AI applications compared to alternatives. While only time will tell if Bard becomes a breakthrough in AI, Google’s willingness to keep pushing boundaries even in a crowded field shows their ambition has not slowed. If Bard lives up to its promise, this launch could mark Google’s comeback as the pacesetter in AI.

The Magic of Animation: How MagicAnimate Advances Human Image Animation

Human image animation aims to create realistic videos of people by animating a single reference image. However, existing techniques often struggle with maintaining fidelity to the original image and smooth, consistent motions over time. Enter MagicAnimate – an open source image animation AI that leverages the power of diffusion models to overcome these challenges.

Created by a team of researchers seeking to enhance video generation quality, MagicAnimate incorporates novel techniques to improve temporal consistency, faithfully preserve reference image details, and increase overall animation fidelity. At its core is a video diffusion model that encodes temporal data to boost coherence between frames. This works alongside an appearance encoder that retains intricate features of the source image so identity and quality are not lost. Fusing these pieces enables remarkably smooth extended animations.

Early testing shows tremendous promise – significantly outperforming other methods on benchmarks for human animation. On a collection of TikTok dancing videos, MagicAnimate boosted video fidelity over the top baseline by an impressive 38%! With performance like this on complex real-world footage, it’s clear these open source models could soon revolutionize the creation of AI-generated human animation.

We’ll dive deeper into how MagicAnimate works and analyze the initial results. We’ll also explore what capabilities like this could enable in the years to come as the technology continues to advance.

How MagicAnimate Works

MagicAnimate is a diffusion-based framework designed for human avatar animation with a focus on temporal consistency. It effectively models temporal information to enhance the overall temporal coherence of the animation results. The appearance encoder not only improves single-frame quality but also contributes to enhanced temporal consistency. Additionally, the integration of a video frame fusion technique enables seamless transitions across the animation video. MagicAnimate demonstrates state-of-the-art performance in terms of both single-frame and video quality, and it has robust generalization capabilities, making it applicable to unseen domains and multi-person animation scenarios.

Diagram the MagicAnimate paper, Given a reference image and the target DensePose motion sequence, MagicAnimate employs a video diffusion model and an appearance encoder for temporal modeling and identity preserving, respectively (left panel). To support long video animation, we devise a simple video fusion strategy that produces smooth video transition during inference (right panel).

Comparing to Animate Anyone

MagicAnimate and Animate Anyone both belong to the realm of text-to-image and text-to-video generation, leveraging diffusion models to achieve superior results. However, they exhibit distinctive approaches in their methodologies and applications.

1. Image Generation Backbone:

  • MagicAnimate: The framework predominantly employs Stable Diffusion as its image generation backbone, emphasizing the generation of 2D optical flow for animation. It utilizes ControlNet to condition the animation process on OpenPose keypoint sequences and adopts CLIP to encode the reference image into a semantic-level text token space, guiding the image generation process through cross-attention.
  • Animate Anyone: In contrast, Animate Anyone focuses on the diffusion model for image generation and highlights the effectiveness of diffusion-based methods in achieving superior results. It explores various models, such as Latent Diffusion Model, ControlNet, and T2I-Adapter, to strike a balance between effectiveness and efficiency. It delves into controllability by incorporating additional encoding layers for controlled generation under various conditions.

2. Temporal Information Processing:

  • MagicAnimate: While MagicAnimate produces visually plausible results, it tends to process each video frame independently, potentially neglecting the temporal information in animation videos.
  • Animate Anyone: Animate Anyone draws inspiration from diffusion models’ success in text-to-image applications and integrates inter-frame attention modeling to enhance temporal information processing. It explores the augmentation of temporal layers for video generation, with approaches like Video LDM and AnimateDiff introducing motion modules and training on large video datasets.

3. Controllability and Image Conditions:

  • MagicAnimate: MagicAnimate conditions the animation process on OpenPose keypoint sequences and utilizes pretrained image-language models like CLIP for encoding the reference image into a semantic-level text token space, enabling controlled generation.
  • Animate Anyone: Animate Anyone explores controllability extensively, incorporating additional encoding layers to facilitate controlled generation under various conditions such as pose, mask, edge, depth, and even content specified by a given image prompt. It proposes diffusion-based image editing methods like ObjectStitch and Paint-by-Example under specific image conditions.

While both MagicAnimate and Animate Anyone harness diffusion models for text-to-image and text-to-video generation, they differ in their choice of image generation backbones, their treatment of temporal information, and the extent to which they emphasize controllability and image conditions. MagicAnimate puts a strong emphasis on Stable Diffusion and cross-attention guided by pretrained models, while Animate Anyone explores a broader range of diffusion models and integrates additional encoding layers for enhanced controllability and versatility in image and video generation.

Closing Thoughts

As AI-powered image animation continues advancing rapidly, the applications are becoming increasingly versatile but may also raise ethical concerns. While MagicAnimate demonstrates promising results, generating custom videos of people requires careful consideration.

Compared to recent sensation AnimateAnyone which produced very realistic animations, MagicAnimate does not yet achieve the same level of fidelity. However, the results here still showcase meaningful improvements in consistency and faithfulness to the source.

As the code and models have not yet been opened sourced, the degree to which the demo videos reflect average performance remains unclear. It is common for research to highlight best-case examples, and real-world results vary. As with any machine learning system, MagicAnimate likely handles some examples better than others.

Nonetheless, between AnimateAnyone, MagicAnimate and other recent papers on AI-powered animation, the pace of progress is staggering. It’s only a matter of time before generating hyper-realistic animations of people on demand becomes widely accessible. And while that enables creative new applications, it also poses risks for misuse and mistreatment that could violate ethics or consent.

As this technology matures, maintaining open and thoughtful conversations around implementation will be critical. But with multiple strong approaches now proven, high-quality human animation powered by AI appears inevitable.

Bringing Still Images to Life: How Animate Anyone Uses Diffusion Models

Creating life-like character animation from simple still images is an alluring concept and a challenging niche within visual generation research. As we continue to unlock the robust generative capabilities of diffusion models, the door to this fascinating frontier opens wider. Yet, even as we step across the threshold, we find ourselves confronted with persistent hurdles; primarily, the daunting task of maintaining temporal consistency with intricate detailed information from an individual character. Despite the challenges, the potential of this revolutionary technology is undeniable.

This paper explores a revolutionary approach that harnesses the power of diffusion models to animate any character from a static image, ensuring a level of detail and controllability previously unattainable. Herein, we introduce a novel framework, the ReferenceNet, designed to preserve intricate appearance features from the reference image and an innovative pose guider to direct character movements. Paired with an efficient temporal modeling method for seamless inter-frame transitions, the resulting framework promises remarkable progress in character animation. Empirically tested and evaluated on fashion video and human dance synthesis benchmarks, our innovation demonstrates superior results and sets a new precedent for image-to-video methodologies.

Animate Anyone Method

The crux of the method, aptly named ‘Animate Anyone’, is its unique approach that embodies an intricate system of steps to generate video from still images while maintaining character-specific details. To provide a tangible understanding of its operation, let’s illustrate the process with an example.

Consider a scenario where they aim to animate a character from a still image to perform a dance sequence. The first stage involves encoding the desired pose sequence using our innovative Pose Guider. This encoded pose is then fused with multi-frame noise, a necessary step to introduce the dynamic aspects of movement into an otherwise static reference.

As they proceed, the fused data undergoes a denoising process managed by the Denoising UNet. The UNet contains a computational block consisting of Spatial-Attention, Cross-Attention, and Temporal-Attention mechanisms—a vital triad that ensures the quality of the resultant video creation.

At this point, they integrate crucial features from the reference image in two-fold. First is through the Spatial-Attention mechanism, where detailed features from the reference image are extracted using our specially constructed ReferenceNet. It’s akin to capturing the essence of our character from the given still image. These extracted details then bolster the Spatial-Attention functionality of the UNet, ensuring the preservation of unique elements from the original image.

Secondly, it employs the services of a CLIP image encoder to extract semantic features for the Cross-Attention mechanism. This step makes sure that the broader context and underlying meaning inherent to the reference image are not lost in the animation process.

Meanwhile, the Temporal-Attention mechanism works its magic in the temporal dimension, accounting for the flow of time and seamless transitions necessary for a convincing video output.

Finally, the Variable AutoEncoder (VAE) decoder comes into play, decoding the processed result and successfully converting it into a video clip that has transformed our static character into a dancing figure, alive with motion and retaining its characteristic details.

In sum, ‘Animate Anyone’ method is like a maestro conducting an orchestra, each instrument playing its part in perfect harmony to produce a beautiful symphony—in this case, a dynamic video that breathes life into a still image.

Application and Testing

Discussion of the challenges of providing smooth inter-frame transitions

The challenges of providing smooth inter-frame transitions in character animation are significant. One of the key difficulties is maintaining temporal stability and consistency with detailed information from the character throughout the video. This challenge has been addressed in recent research, which leverages the power of diffusion models and proposes a novel framework tailored for character animation. The proposed framework, called Animate Anyone, aims to preserve consistency of intricate appearance features from a reference image, ensure controllability and continuity, and employ an effective temporal modeling approach to ensure smooth inter-frame transitions between video frames.

The Animate Anyone framework introduces several components to address the challenges of smooth inter-frame transitions in character animation. These components include:

  1. ReferenceNet: This component is designed to merge detail features via spatial attention, allowing the model to capture spatial details of the reference image and integrate them into the denoising process using spatial attention. This helps the model preserve appearance consistency and intricate details from the reference image.
  2. Pose Guider: A lightweight pose guider is devised to efficiently integrate pose control signals into the denoising process, ensuring pose controllability throughout the animation.
  3. Temporal Modeling: The framework introduces a temporal layer to model relationships across multiple frames, preserving high-resolution details in visual quality while simulating a continuous and smooth temporal motion process.

By expanding the training data, the Animate Anyone framework can animate arbitrary characters, yielding superior results in character animation compared to other image-to-video methods. The framework has been evaluated on benchmarks for fashion video and human dance synthesis, achieving state-of-the-art results.

How effective temporal modeling approach addresses the issue?

The effectiveness of the temporal modeling approach in addressing the issue is demonstrated in the context of character animation synthesis. The approach involves the integration of supplementary temporal layers into text-to-image (T2I) models to capture the temporal dependencies among video frames. This design facilitates the transfer of pre-trained image generation capabilities from the base T2I model. The temporal layer is integrated after the spatial-attention and cross-attention components within the Res-Trans block. It involves reshaping the feature map and performing temporal attention, which refers to self-attention along the time dimension. The feature from the temporal layer is then incorporated into the original feature through a residual connection. This design, when applied within the Res-Trans blocks of the denoising UNet, ensures temporal smoothness and continuity of appearance details, obviating the need for intricate motion modeling. Therefore, the temporal modeling approach effectively addresses the issue of temporal smoothness and continuity of appearance details in character animation synthesis.

Video Demo of Animate Anyone

Final Thoughts

The innovative ‘Animate Anyone‘ approach breaks new ground by isolating and animating characters within still images. It echoes the traditional animation workflow, which separates the background from the characters, but brings it into the world of AI. This, in essence, is a pure character animation process. The fact that one can add any desired background behind the animated figure opens a limitless world of creative possibilities.

As we ponder on the future of this technology, curiosity fuels our desire to understand the intricate code that powers it. It’s the mystery behind the scenes, the magic behind the curtain. It’s the complex dance of algorithms that transforms a static image into a lively, animated character.

To say we are impressed by this development would be an understatement. The progress within this field has been astonishing and we find the borders between technology and magic increasingly blurring. The ‘Animate Anyone’ method stands as a testament to the incredible strides we are making in visual generation research. It serves as a beacon, illuminating what’s possible and inspiring us to push those boundaries even further.

We are not only on the edge of innovation – we are actively leaping over it, propelled by the magic of diffusion models, and landing in a world where static images can, truly, come to life. Such is the allure and the power of character animation in the realm of artificial intelligence.