The A.I Megathread (LLM , GPT , Development)

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1/6
@rohanpaul_ai
First Open code LLM to reveal entire training pipeline and reproducible datasets

Original Problem:

Code LLMs lack transparency in training data and protocols, limiting research community's ability to establish strong baselines and gain deeper insights.

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Solution in this Paper:

→ Introduces OpenCoder, a fully transparent code LLM with complete training data, processing pipeline, and protocols

→ Implements sophisticated data processing pipeline called RefineCode with 960B tokens across 607 programming languages

→ Uses aggressive file-level deduplication and language-specific filtering rules

→ Employs two-stage instruction tuning with annealing phase using high-quality synthetic data

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Key Insights:

→ File-level deduplication outperforms repository-level approach for maintaining data diversity

→ GitHub star-based filtering can reduce data diversity and affect distribution

→ High-quality data in annealing phase is more crucial than quantity

→ Two-stage instruction tuning improves both theoretical and practical coding tasks

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Results:

→ OpenCoder-8B achieves 83.5% pass@1 on HumanEval benchmark

→ Surpasses all previous fully open models at 6B+ parameter scale

→ Demonstrates superior training efficiency compared to The Stack v2

2/6
@rohanpaul_ai
Paper Title: "OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models"

Generated below podcast on this paper with Google's Illuminate.



https://video.twimg.com/ext_tw_video/1861161457960001538/pu/vid/avc1/1080x1080/mc7X5OmyGwoCxj21.mp4

3/6
@rohanpaul_ai
The illustration of our pretraining data processing workflow.

4/6
@rohanpaul_ai
OpenCoder surpasses all previous fully open models and other open-access models at the 6B+ parameter scale. The 8B version achieves 83.5% pass@1 on HumanEval benchmark, making it competitive with leading proprietary models.

5/6
@rohanpaul_ai
Their instruction data synthesis workflow

6/6
@rohanpaul_ai
[2411.04905] OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models




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1/8
@rohanpaul_ai
New benchmark exposes the true reasoning capabilities of LLMs using dynamic puzzle generation

K&K puzzles, proposed in this paper, reveal how LLMs balance memorization and reasoning in logical problem-solving

Original Problem:

LLMs show puzzling behavior in reasoning tasks - excellent performance on complex problems but basic mistakes on simple ones. This raises questions about whether they truly reason or just memorize training data.

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Solution in this Paper:

→ Introduces Knights and Knaves (K&K) puzzles as a dynamic benchmark for testing logical reasoning

→ Develops Local Inconsistency-based Memorization Score (LiMem) that measures model performance on original vs perturbed puzzles

→ Creates two key modules:

- Abstract Module: Generates puzzles with specified complexity

- Natural Language Module: Converts abstract puzzles to natural text

→ Implements systematic perturbation tests at both mathematical and linguistic levels

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Key Insights:

→ LLMs can simultaneously use memorization and genuine reasoning

→ Fine-tuning improves generalization even as memorization increases

→ Models can develop reasoning skills even when trained only on question-answer pairs

→ More complex puzzles show higher memorization scores

→ Language-level perturbations affect models less than mathematical structure changes

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Results:

→ Only advanced LLMs achieve >70% accuracy on 2-person puzzles

→ Performance drops to 11% for 8-person puzzles

→ GPT4o-mini reaches near 100% training accuracy on 3/5-person puzzles

→ LiMem scores ~50% on 8-person puzzles indicate heavy memorization

→ Models show 80% memorization under role-flipping perturbations

2/8
@rohanpaul_ai
Paper Title: "On Memorization of Large Language Models in Logical Reasoning"

Generated below podcast on this paper with Google's Illuminate.



https://video.twimg.com/ext_tw_video/1861169337081700352/pu/vid/avc1/1080x1080/vJUxlnzGezPNqx37.mp4

3/8
@rohanpaul_ai

4/8
@rohanpaul_ai
[2410.23123] On Memorization of Large Language Models in Logical Reasoning



5/8
@rohanpaul_ai
The Knights and Knaves benchmark generates logical puzzles where some characters always tell truth (knights) and others always lie (knaves).

It has two key modules:

→ Abstract Module: Generates puzzles with specified number of people, tree width, and depth. Can perturb puzzles by changing statements or leaf nodes

→ Natural Language Module: Converts abstract puzzles to natural language, can perturb by changing names, role terms, statement order

6/8
@ICoffeeDaemon
Nicht schlecht



7/8
@fabmilo
Just compression/memorization. There is no reasoning in refining probabilities of tokens. We need a complete different system to achieve reasoning and tons of compute power.



8/8
@MiaAI_Builder
LLMs have been puzzling us with their behavior in reasoning tasks, indeed. Hope this benchmark helps us understand them better




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1/3
@rohanpaul_ai
This paper makes complex Multi-objective reinforcement learning (MORL) policies understandable by clustering them based on both behavior and objectives

When AI gives you too many options, this clustering trick saves the day

Original Problem:

Multi-objective reinforcement learning (MORL) generates multiple policies with different trade-offs, but these solution sets are too large and complex for humans to analyze effectively. Decision makers struggle to understand relationships between policy behaviors and their objective outcomes.

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Solution in this Paper:

→ Introduces a novel clustering approach that considers both objective space (expected returns) and behavior space (policy actions)

→ Uses Highlights algorithm to capture 5 key states that represent each policy's behavior

→ Applies PAN (Pareto-Set Analysis) clustering to find well-defined clusters in both spaces simultaneously

→ Employs bi-objective evolutionary algorithm to optimize clustering quality across both spaces

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Key Insights:

→ First research to tackle MORL solution set explainability

→ Different policies with similar trade-offs can exhibit vastly different behaviors

→ Combining objective and behavior analysis reveals deeper policy insights

→ Makes MORL more practical for real-world applications

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Results:

→ Outperformed traditional k-medoids clustering in MO-Highway and MO-Lunar-lander environments

→ Showed comparable performance in MO-Reacher and MO-Minecart scenarios

→ Successfully demonstrated practical application through highway environment case study

2/3
@rohanpaul_ai
Paper Title: "Navigating Trade-offs: Policy Summarization for Multi-Objective Reinforcement Learning"

Generated below podcast on this paper with Google's Illuminate.



https://video.twimg.com/ext_tw_video/1861197491238211584/pu/vid/avc1/1080x1080/56yXAj4Toyxny-Ic.mp4

3/3
@rohanpaul_ai
[2411.04784v1] Navigating Trade-offs: Policy Summarization for Multi-Objective Reinforcement Learning




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Spoiler: full text & large images

1/11
@rohanpaul_ai
Transform AI from task-completers to thought-provokers

Current AI primarily act as obedient assistants focused on task completion, stemming from 19th-century statistical models. This limits their potential to enhance human critical thinking and creates a binary perception of AI as either compliant servants or rebellious threats.

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Ideas discussed in this Paper:

→ Transform AI from task-completing assistants into provocateurs that challenge users' thinking

→ Implement critical thinking tools from educational frameworks like Bloom's taxonomy and Toulmin model into AI systems

→ Design AI to critique work, surface biases, present counter-arguments, and question assumptions

→ Create interfaces beyond chat that function as "tools of thought" similar to maps, grids, and algebraic notation

2/11
@rohanpaul_ai
Paper Title: "AI Should Challenge, Not Obey"

Generated below podcast on this paper with Google's Illuminate.



https://video.twimg.com/ext_tw_video/1860810218474721280/pu/vid/avc1/1080x1080/mJq53adIVFd2os5n.mp4

3/11
@rohanpaul_ai
[2411.02263] AI Should Challenge, Not Obey



4/11
@jmjjohnson
Sounds like what @arunbahl is building at @AloeInc - a “personal thought partner – a synthetic mind that can reason, purpose-built for human thinking.”



5/11
@rohanpaul_ai
Awesome!!



6/11
@EricFddrsn
That’s great - the people pleasing nature of the LLMs today is one of the main things that separates them of being good thought partners



7/11
@BergelEduardo
Yes! "AI should Challenge, Not Obey." One for Eternity..



8/11
@xone_4
Exactly.



9/11
@HAF_tech
I love this idea! Let's move beyond 19th-century statistical models and create AI that enhances human critical thinking



10/11
@LaneGrooms
Brainstorming: This has been my most successful use case since the first time I ran an LLM locally, I.e. had access to the system prompt. Glad it’s getting more attention.



11/11
@Sipera007
Have you tried randomising all the words in a pdf / source then asking an llm to re order it so it reads as intended? Interesting to see when it breaks vs when it works. For example less than 200 words easy, more than 600 not. Also simply removing all superfluous words like and?




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Amazon’s Moonshot Plan to Rival Nvidia in AI Chips

The cloud computing giant won’t dislodge the incumbent anytime soon but is hoping to reduce its reliance on the chipmaker.

www.bloomberg.com

Amazon’s Moonshot Plan to Rival Nvidia in AI Chips​

The cloud computing giant won’t dislodge the incumbent anytime soon but is hoping to reduce its reliance on the chipmaker.

By Matt Day, Ian King, and Dina Bass

November 24, 2024 at 5:00 PM EST

Save

In a bland north Austin neighborhood dominated by anonymous corporate office towers, Amazon.com Inc. engineers are toiling away on one of the tech industry’s most ambitious moonshots: loosening Nvidia Corp.’s grip on the $100-billion-plus market for artificial intelligence chips.

Amazon’s utilitarian engineering lab contains rows of long work benches overlooking the Texas capital’s mushrooming suburbs. The place is kind of a mess. Printed circuit boards, cooling fans, cables and networking gear are strewn around workstations in various states of assembly, some muddied with the thermal paste used to connect chips to the components that keep them from overheating. There’s a bootstrapping vibe you’d expect to see at a startup not a company with a market cap exceeding $2 trillion.

The engineers who work here think nothing of running to Home Depot for a drill press and are happy to learn subjects outside their area of expertise if doing so will speed things up. Years into a scramble to create machine learning chips from scratch, they have found themselves on the hook to roll out an Nvidia fighter as quickly as they can. This is not about raw horsepower. It’s about building a simple, reliable system that can quickly turn Amazon data centers into humongous AI machines.

Engineers at Amazon’s utilitarian engineering lab in Austin.Photographer: Sergio Flores/Bloomberg

There’s a bootstrapping vibe you’d expect to see at a startup not a company with a market cap exceeding $2 trillion.Photographer: Sergio Flores/Bloomberg

Rami Sinno, a gregarious Lebanese-born engineer who has worked in the chip industry for decades, is in charge of chip design and testing. He helped create the first two generations of Amazon AI semiconductors and is now rushing to get the latest iteration, Trainium2, running reliably in data centers by the end of the year. “What keeps me up at night is, how do I get there as quickly as possible,” Sinno says.

In the past two years, Nvidia has transformed from a niche chipmaker to the main supplier of the hardware that enables generative AI, a distinction that has made the company the world’s largest by market value. Nvidia processors cost tens of thousands of dollars apiece and, thanks to overwhelming demand, are hard to get hold of. Last week, after reporting earnings, the chipmaker told investors that demand for its latest hardware will outstrip supply for several quarters — deepening the crunch.

Nvidia’s biggest customers — cloud providers like Amazon Web Services, Microsoft Corp.’s Azure and Alphabet Inc.’s Google Cloud Platform — are eager to reduce their reliance on, if not replace, Nvidia chips. All three are cooking up their own silicon, but Amazon, the largest seller of rented computing power, has deployed the most chips to date.

In many ways, Amazon is ideally situated to become a power in AI chips. Fifteen years ago, the company invented the cloud computing business and then, over time, started building the infrastructure that sustains it. Reducing its reliance on one incumbent after another, including Intel Corp., Amazon ripped out many of the servers and network switches in its data centers and replaced them with custom-built hardware. Then, a decade ago, James Hamilton, a senior vice president and distinguished engineer with an uncanny sense of timing, talked Jeff Bezos into making chips.

“We’re strongly of the view that we can produce a part that competes with them toe to toe.”

When OpenAI’s ChatGPT kicked off the generative AI age two years ago, Amazon was widely considered an also-ran, caught flat-footed and struggling to catch up. It has yet to produce its own large language model that is seen as competitive with the likes of ChatGPT or Claude, built by Anthropic, which Amazon has backed to the tune of $8 billion. But the cloud machinery Amazon has built — the custom servers, switches, chips — has positioned Chief Executive Officer Andy Jassy to open an AI supermarket, selling tools for businesses that want to use models built by other outfits and chips for companies that train their own AI services.

After almost four decades in the business, Hamilton knows taking Amazon’s chip ambitions to the next level won’t be easy. Designing reliable AI hardware is hard. Maybe even harder is writing software capable of making the chips useful to a wide range of customers. Nvidia gear can smoothly handle just about any artificial intelligence task. The company is shipping its next-generation chips to customers, including Amazon, and has started to talk up the products that will succeed them a year from now. Industry observers say Amazon isn’t likely to dislodge Nvidia anytime soon.

James Hamilton says Amazon can compete “toe to toe” with Nvidia.Photographer: Chona Kasinger/Bloomberg

Still, time and again, Hamilton and Amazon’s teams of engineers have demonstrated their capacity to solve big technical problems on a tight budget. “Nvidia is a very, very competent company doing excellent work, and so they’re going to have a good solution for a lot of customers for a long time to come,” Hamilton says. “We’re strongly of the view that we can produce a part that competes with them toe to toe.”

Hamilton joined Amazon in 2009 after stints at International Business Machines Corp. and Microsoft. An industry icon who got his start repairing luxury cars in his native Canada and commuted to work from a 54-foot boat, Hamilton signed on at an auspicious time. Amazon Web Services had debuted three years earlier, singlehandedly creating an industry for what came to be known as cloud computing services. AWS would soon start throwing off gobs of cash, enabling Amazon to bankroll a number of big bets.

Back then, Amazon built its own data centers but equipped them with servers and network switches made by other companies. Hamilton spearheaded an effort to replace them with custom hardware, starting with servers. Since Amazon would be buying millions of them, Hamilton reckoned he could lower costs and improve efficiency by tailoring the devices for his growing fleet of data centers and leaving out features that AWS didn’t need.

The effort was successful enough that Jassy — then running AWS — asked what else the company might design in-house. Hamilton suggested chips, which were gobbling up more and more tasks that had previously been handled by other components. He also recommended that Amazon use the energy-efficient Arm architecture that powers smartphones, a bet that the technology’s ubiquity, and developers’ growing familiarity with it, could help Amazon displace the Intel chips that had long powered server rooms around the world.

“All paths lead to us having a semiconductor design team,” he wrote in a proposal presented to Bezos in August 2013. A month later, Hamilton, who likes to hang out with startups and customers in the late afternoon, met Nafea Bshara for a drink at Seattle’s Virginia Inn pub.

An Israeli chip industry veteran who relocated to the San Francisco Bay area in the early 2000s, Bshara co-founded Annapurna Labs, which he named for the Nepalese peak. (Bshara and a co-founder had intended to summit the mountain before founding the startup. But investors were eager for them to get to work, and they never made the trip.)

The stealthy startup set out to build chips for data centers at a time when most of the industry was fixated on mobile phones. Amazon commissioned processors from Annapurna and, two years later, acquired the startup for a reported $350 million. It was a prescient move.

A circuit board.Photographer: Sergio Flores/Bloomberg

Bshara and Hamilton started small, a reflection of their shared appreciation for utilitarian engineering. Back then, each data center server reserved a portion of its horsepower to run control, security and networking features. Annapurna and Amazon engineers developed a card, called Nitro, that vacuumed those functions off the server entirely, giving customers access to its full power.

Later, Annapurna brought Hamilton’s Arm general-purpose processor to life. Called Graviton, the product operated more cheaply than rival Intel gear and made Amazon one of the 10 biggest customers of Taiwan Semiconductor Manufacturing Co., the titan that produces chips for much of the industry.

Amazon brass had by then grown confident Annapurna could excel even in unfamiliar areas. “You’ll find a lot of companies are very good in CPU, or very good in networking,” Bshara says. “It’s very rare to find the teams that are good in two or three or four different domains.”

While Graviton was in development, Jassy asked Hamilton what other things Amazon might make itself. In late 2016, Annapurna deputized four engineers to explore making a machine learning chip. It was another timely bet: A few months later, a group of Google researchers published a seminal paper proposing a process that would make generative AI possible.

 

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The paper, titled “Attention is All You Need,” introduced transformers, a software design principle that helps artificial intelligence systems identify the most important pieces of training data. It became the foundational method behind systems that can make educated guesses at the relationships between words and create text from scratch.

At about this time, Rami Sinno was working for Arm Holdings Plc in Austin and coaching his school-age son through a robotics competition. The team built an app that used machine learning algorithms to pore over photos and detect the algae blooms that periodically foul Austin’s lakes in the summer. Impressed by what kids could do with little more than a laptop, Sinno realized a revolution was coming. He joined Amazon in 2019 to help lead its AI chipmaking efforts.

The unit’s first chip was designed to power something called inference — when computers trained to recognize patterns in data make a prediction, such as whether a piece of email is spam. That component, called Inferentia, rolled out to Amazon’s data centers by December 2019, and was later used to help the Alexa voice assistant answer commands. Amazon’s second AI chip, Trainium1, was aimed at companies looking to train machine learning models. Engineers also repackaged the chip with components that made it a better fit for inference, as Inferentia2.

Demand for Amazon’s AI chips was slow at first, meaning customers could get access to them immediately rather than waiting weeks for big batches of Nvidia hardware. Japanese firms looking to quickly join the generative AI revolution took advantage of the situation. Electronics maker Ricoh Co., for example, got help converting large language models trained on English-language data to Japanese.

Demand has since picked up, according to Gadi Hutt, an early Annapurna employee who works with companies using Amazon chips. “I don’t have any excess capacity of Trainium sitting around waiting for customers,” he says. “It’s all being used.”

Trainium2 is the company’s third generation of artificial intelligence chip. By industry reckoning, this is a make-or-break moment. Either the third attempt sells in sufficient volume to make the investment worthwhile, or it flops and the company finds a new path. “I have literally never seen a product deviate from the three-generation rule,” says Naveen Rao, a chip industry veteran who oversees AI work at Databricks Inc., a purveyor of data and analytics software.

Databricks in October agreed to use Trainium as part of a broad agreement with AWS. At the moment, the company’s AI tools primarily run on Nvidia. The plan is to displace some of that work with Trainium, which Amazon has said can offer 30% better performance for the price, according to Rao. “It comes down to sheer economics and availability,” Rao says. “That’s where the battleground is.”

Trainium1 was comprised of eight chips, nestled side by side in a deep steel box that allows plenty of room for their heat to dissipate. The full package that AWS rents to its customers is made up of two of these arrays. Each case is filled with wires, neatly enclosed in mesh wrapping.

A row of artificial intelligence chips.Photographer: Sergio Flores/Bloomberg

For Trainium2, which Amazon says has four times the performance and three times the memory of the prior generation, engineers scrapped most of the cables, routing electrical signals instead via printed circuit boards. And Amazon cut the number of chips per box down to two, so that engineers performing maintenance on one unit take down fewer other components. Sinno has come to think of the data center as a giant computer, an approach Nvidia boss Jensen Huang has encouraged the rest of the industry to adopt. “Simplification is critical there, and it also allowed us to go faster for sure,” Sinno says.

Amazon didn’t wait for TSMC to produce a working version of Trainium2 before starting to test how the new design might work. Instead, engineers fixed two prior generation chips onto the board, giving them time to work on the control software and test for electrical interference. It was the semiconductor industry equivalent of building the plane while it’s flying.

Amazon has started shipping Trainium2, which it aims to string together in clusters of up to 100,000 chips, to data centers in Ohio and elsewhere. A broader rollout is coming for Amazon’s main data center hubs.

Rami Sinno, who is in charge of chip design and testing, has come to think of the data center as a giant computer.Photographer: Sergio Flores/Bloomberg

The company aims to bring a new chip to market about every 18 months, in part by reducing the number of trips hardware has to make to outside vendors. Across the lab from the drill press sits a set of oscilloscopes Amazon uses to test cards and chips for bum connectors or design flaws. Sinno hints at the work already underway on future editions: In another lab, where earsplitting fans cool test units, four pairs of pipes dangle from the ceiling. They’re capped now but are ready for the day when future AWS chips produce too much heat to be cooled by fans alone.

Other companies are pushing the limits, too. Nvidia, which has characterized demand for its chips as “insane,” is pushing to bring a new chip to market every year, a cadence that caused production issues with its upcoming Blackwell product but will put more pressure on the rest of the industry to keep up. Meanwhile, Amazon’s two biggest cloud rivals are accelerating their own chip initiatives.

Google began building an AI chip about 10 years ago to speed up the machine learning work behind its search products. Later on, the company offered the product to cloud customers, including AI startups like Anthropic, Cohere and Midjourney. The latest edition of the chip is expected to be widely available next year. In April, Google introduced its first central processing unit, a product similar to Amazon’s Graviton. “General purpose compute is a really big opportunity,” says Amin Vahdat, a Google vice president who leads engineering teams working on chips and other infrastructure. The ultimate aim, he says, is getting the AI and general computing chips working together seamlessly.

Puzzle pieces designating Hamilton’s many patents for Amazon.Photographer: Chona Kasinger/Bloomberg

Microsoft got into the data center chip game later than AWS and Google, announcing an AI accelerator called Maia and a CPU named Cobalt only late last year. Like Amazon, the company had realized it could offer customers better performance with hardware tailored to its data centers.

Rani Borkar, a vice president who spent almost three decades at Intel, leads the effort. Earlier this month, her team added two products to Microsoft’s portfolio: a security chip and a data processing unit that speeds up the flow of data between CPUs and graphics processing units, or GPUs. Nvidia sells a similar product. Microsoft has been testing the AI chip internally and just started using it alongside its fleet of Nvidia chips to run the service that lets customers create applications with OpenAI models.

While Microsoft’s efforts are considered a couple of generations behind Amazon’s, Borkar says the company is happy with the results so far and is working on updated versions of its chips. “It doesn’t matter where people started,” she says. “My focus is all about: What does the customer need? Because you could be ahead, but if you are building the wrong product that the customer doesn’t want, then the investments in silicon are so massive that I wouldn’t want to be a chapter in that book.”

Despite their competitive efforts, all three cloud giants sing Nvidia’s praises and jockey for position when new chips, like Blackwell, hit the market.

Amazon’s Trainium2 will likely be deemed a success if it can take on more of the company’s internal AI work, along with the occasional project from big AWS customers. That would help free up Amazon’s precious supply of high-end Nvidia chips for specialized AI outfits. For Trainium2 to become an unqualified hit, engineers will have to get the software right — no small feat. Nvidia derives much of its strength from the comprehensiveness of its suite of tools, which let customers get machine-learning projects online with little customization. Amazon’s software, called Neuron SDK, is in its infancy by comparison.

Even if companies can port their projects to Amazon without much trouble, checking that the switch-over didn’t break anything can eat up hundreds of hours of engineers’ time, according to an Amazon and chip industry veteran, who requested anonymity to speak freely. An executive at an AWS partner that helps customers with AI projects, who also requested anonymity, says that while Amazon had succeeded in making its general-purpose Graviton chips easy to use, prospective users of the AI hardware still face added complexity.

“There’s a reason Nvidia dominates,” says Chirag Dekate, a vice president at Gartner Inc. who tracks artificial intelligence technologies. “You don’t have to worry about those details.”

So Amazon has enlisted help — encouraging big customers and partners to use the chips when they strike up new or renewed deals with AWS. The idea is to get cutting-edge teams to run the silicon ragged and find areas for improvement.

One of those companies is Databricks, which anticipates spending weeks or months getting things up and running but is willing to put in the effort in the hopes that promised cost savings materialize. Anthropic, the AI startup and OpenAI rival, agreed to use Trainium chips for future development after accepting $4 billion of Amazon’s money last year, though it also uses Nvidia and Google products. On Friday, Anthropic announced another $4 billion infusion from Amazon and deepened the partnership.

“We’re particularly impressed by the price-performance of Amazon Trainium chips,” says Tom Brown, Anthropic’s chief compute officer. “We’ve been steadily expanding their use across an increasingly wide range of workloads.”

Hamilton says Anthropic is helping Amazon improve quickly. But he’s clear-eyed about the challenges, saying it’s “mandatory” to create great software that makes it easy for customers to use AWS chips. “If you don’t bridge the complexity gap,” he says, “you’re going to be unsuccessful.”

 

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[Abraxus]

Best Ai generator for pics?

 

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Spoiler: related threads

 

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Spoiler: full text & large images

1/11
@Alibaba_Qwen
We're releasing a preview of QwQ /kwju:/ — an open model designed to advance AI reasoning capabilities.

Blog: QwQ: Reflect Deeply on the Boundaries of the Unknown Model: Qwen/QwQ-32B-Preview · Hugging Face
Demo: QwQ-32B-Preview - a Hugging Face Space by Qwen

QwQ has preliminarily demonstrated remarkable capabilities, especially in solving some challenges in mathematics and coding. As a preview release, we acknowledge its limitations. We earnestly invite the open research community to collaborate with us to explore the boundaries of the unknown!

2/11
@Yuchenj_UW
QwQ is a powerful reasoner, better name than o1 and r1 in my mind! Try our Hyperbolic API:

[Quoted tweet]
Happy Thankshipping! We @hyperbolic_labs now serve QwQ-32B-Preview released by @Alibaba_Qwen today in BF16!

> A reasoning model that is competitive with OpenAI o1-mini and o1-preview.
> Integration with @OpenRouterAI coming soon.
> It can answer the Strawberry question correctly every time (we auto-applied the system prompt for the API). Throw your hardest problems to it and compare it with o1 and Deepseek-R1.

It's incredible to see China taking the lead in open-source models that can reason, especially given the challenges of working with fewer and worse GPUs than US labs. Kudos to @JustinLin610, @huybery, and the Qwen team for what they are doing for open-source AI.

3/11
@Grad62304977
Is a paper on this coming anytime soon?



4/11
@_akhaliq
try out QwQ and more in anychat: Anychat - a Hugging Face Space by akhaliq

5/11
@Hannibal9972485
Total use of brute force . . The model doesn’t directly apply a simple counting method it goes letter by letter, analyzing each position as if it doesn’t trust itself to handle the word holistically. After reaching the correct count of 3, it goes back and double-checks every position of “r.”

This shows the model is not confident in its own ability to get the right answer on the first try—it feels the need to repeat its process unnecessarily. model drags it out by simulating a long process to arrive at the answer.

This is a hallmark of brute-forcing, applying an exhaustive, granular method to guarantee correctness, even when smarter, simpler approaches exist.

x.com

6/11
@LottoLabs
Out of interest why is the system prompt in mandarin but it asks the system to output English as default or the language it is prompted in.



7/11
@kiiwee_dev
@abacaj i hope you cook with this one



8/11
@RobbyGtv
This is A LOT of thinking though, for something that other models can get right without the added thinking: Let's see. The word is "strawberry." I need to find out how many 'r's are in it. Okay, first, I'll spell it out slowly: s-t-r-a-w-b-e-r-r-y. Okay, now, I'll count the 'r's. Let's see: there's an 'r' after the 't', then another 'r' between the two 'r's towards the end, and one more at the end. Wait, no. Let's look again.

Spelling it out: s-t-r-a-w-b-e-r-r-y.

So, the letters are:

1. s

2. t

3. r

4. a

5. w

6. b

7. e

8. r

9. r

10. y

Okay, so looking at the positions:

- Third letter: r

- Eighth letter: r

- Ninth letter: r

So, there are three 'r's in "strawberry." Wait, but when I look at the word, it seems like there are only two 'r's. Let me check again.

Spell it again: s-t-r-a-w-b-e-r-r-y.

Yes, third letter is 'r', then eighth and ninth letters are both 'r's. So, that's three 'r's. But in the word, it looks like "strawberry" has two 'r's, but according to spelling, there are three.

Wait a minute, maybe I'm miscounting. Let's write it down:

s t r a w b e r r y

Positions:

1. s

2. t

3. r

4. a

5. w

6. b

7. e

8. r

9. r

10. y

Yes, positions 3, 8, and 9 are all 'r's. So, there are three 'r's in "strawberry."

But in the word, it seems like the last two letters are 'r' and 'y', so perhaps I'm confusion it with another word.

Wait, no. "Strawberry" ends with 'y', not 'r'. So, it's s-t-r-a-w-b-e-r-r-y.

So, there are two 'r's in "strawberry." Wait, but according to the spelling, there are three 'r's.

Hmm, maybe I'm overcounting.

Let me think differently. The word "strawberry" has ten letters:

s t r a w b e r r y

So, letters 3, 8, and 9 are 'r's. So, that's three 'r's.

But maybe in the word, it's only two 'r's because 'rr' is considered a double 'r'.

Wait, but in terms of counting individual 'r's, 'rr' is two 'r's.

So, in "strawberry," there are three 'r's: one in position 3, and two in positions 8 and 9.

Wait, no. Positions 8 and 9 are both 'r's, so that's two 'r's at the end.

Wait, no, positions 8 and 9 are both 'r's, so that's two 'r's, and position 3 is another 'r'. So, total three 'r's.

But when I look at the word "strawberry," it seems like there are only two 'r's because the double 'r' at the end is considered as one sound in pronunciation.

But in terms of letter count, there are three 'r's in "strawberry."

Wait, no. Let's check again.

Spelling of "strawberry": s-t-r-a-w-b-e-r-r-y.

So, letters:

1. s

2. t

3. r

4. a

5. w

6. b

7. e

8. r

9. r

10. y

So, positions 3, 8, and 9 are 'r's. Therefore, there are three 'r's in "strawberry."

But perhaps I'm misremembering. Let me check a dictionary.

[Simulates checking a dictionary or searching online]

According to the dictionary, "strawberry" is spelled s-t-r-a-w-b-e-r-r-y, with three 'r's.

So, the answer is three.

Wait, but in the word, it's pronounced with two r sounds. So, maybe that's why I'm confused.

In pronunciation, "strawberry" has two r sounds: one after the 't' and one before the 'y'. The double 'r' at the end is often pronounced as a single sound.

But in terms of letter count, there are three 'r's in "strawberry."

So, to answer the question "how many r's in strawberry," the answer is three.

But perhaps the person asking the question is thinking of the pronunciation and considers it as two 'r's.

However, since the question is about the number of 'r's in the word, it should be three.

Alternatively, if it's about the number of times 'r' is pronounced, it might be two.

But the question is "how many r's in strawberry," which refers to the letters, so the answer is three.



9/11
@k_zer0s
Superb!



10/11
@risphereeditor
Great!



11/11
@installarchnow
these guys really ship!

Spoiler: thread continued

Spoiler: full text & large images

1/7
@dani_avila7
Qwq Running in VSCode with CodeGPT!

The Qwq model is now accessible directly in VSCode through @codegptAI and @ollama

This experimental 32B model, developed by the @Alibaba_Qwen team, delivers impressive performance, being competitive with models like o1-mini and o1-preview in certain scenarios.

In this post , I’ll show you how to set up and use Qwq, including downloading the model and integrating it into VSCode.

Hardware Recommendations:
During testing, we used an M3 Max machine with 36 GB of RAM, which provided the speed demonstrated in the video.

RAM is one of the most critical factors for optimal performance with this model, so we recommend a device with at least an M1 chip and as much memory as possible for a smooth experience.

Step-by-step guide to start using Qwq in your projects effectively!



https://video.twimg.com/ext_tw_video/1862263547394191360/pu/vid/avc1/720x1388/GbU-DPOfgV9n5_ZH.mp4

2/7
@dani_avila7
Step 1: Download and install Ollama from Ollama



3/7
@dani_avila7
Step 2: Install CodeGPT from the VSCode marketplace



4/7
@dani_avila7
Open CodeGPT in VSCode, select Ollama as the provider, then search for the Qwq model and click Download

5/7
@dani_avila7
Step 4: Wait for the model to download and install. Once it's done, you're all set!

You can now use the powerful Qwq model directly on your computer, running locally without any cloud dependencies.

Enjoy seamless AI-powered coding right in your own environment!

6/7
@gdbsm1
@Readwise save thread



7/7
@sky20086
try it.

Spoiler: thread continued

Spoiler: full text & large images

1/11
@victormustar
Now available on HuggingChat:
Qwen/QwQ-32B-Preview (full precision)

Qwen/QwQ-32B-Preview - HuggingChat

2/11
@victormustar
Remember this is a preview of a new kind of reasoning open model by @Alibaba_Qwen, read more here: QwQ: Reflect Deeply on the Boundaries of the Unknown



3/11
@ivanfioravanti
HuggingChat getting better release after release
QwQ is top! Looking at its reasoning is so funny:

4/11
@victormustar
the reasoning is too much fun



5/11
@HomelanderBrown
Can't even tell how many R's are there in strawberry



6/11
@victormustar
?

7/11
@risphereeditor
Cool!



8/11
@donvito
is it available in the inference API?



9/11
@PatrikarSanket
it's not returning why responses to me. it hangs for some time and then i get 'something went wrong'



10/11
@NOOROU

11/11
@victormustar
not the right model selected

Spoiler: thread continued

Spoiler: full text & large images

1/12
@ollama
ollama run qwq

an experimental 32B model by the Qwen team that is competitive with o1-mini and o1-preview in some cases.

qwq

Note: This is the pronunciation of QwQ: /kwju:/ , similar to the word “quill”

2/12
@ollama
License: Apache 2.0



3/12
@victorlacerdab
Does it run on M4 Pro 48gb?



4/12
@ollama
Yes! 4 bit quantized model is 20GB + overhead



5/12
@chrypnotoad
How many Mac minis do I need?

6/12
@ollama
We do also test on Mac mini.

1



7/12
@KrowWenOnYT
Would the lowest quantized model run on Mac M3 Pro?



8/12
@ollama
how much memory is your M3 Pro?

The 4 bit quantized model needs just over 20GB, and then you need overhead for your context size.



9/12
@TomDavenport
Incredible! If this can run on Mac mini I’ll buy one



10/12
@ollama
It does! We do have a M4 Mac mini as part of the cluster of consumer devices for testing releases / model releases.



11/12
@iwantwinwinwin
What’s the pronunciation of ollama?



12/12
@ollama
oh llama

 

[bnew]

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Spoiler: full text & large images

1/9
@AnthonyNAguirre
Just posting this very nice new graph from Epoch AI in case anyone doubts that there has been major progress in AI since GPT-4 launched. This PhD-level science benchmark goes from slightly above guessing to expert human level. Nor does this trend seem to be leveling off. Similar strong trend in math.

2/9
@AnthonyNAguirre
And keep in mind that this is human expert level in *many* sciences, which basically no human can be.



3/9
@NikSamoylov
One problem I see with benchmarks is contamination. We do not know if the test data is in the training/tuning sets or not.

In other words, it could do well on the test, but not in real-life applications.

I don't doubt that, for example, the claude sonnet 3.6 is more useful as a programming chatbot. So clearly some enhancements have been made.

But I see most benchmarks as moderately expensive marketing gimmicks. I also do not understand what "PhD-level" is supposed to mean. Can it formulate new hypotheses, run a few months' worth of independent research and then defend a thesis? I think not.

I am not going to propose better benchmarks because I fear that you can get better at what you decide to measure.



4/9
@AnthonyNAguirre
It's certainly possible unscrupulous developers deliberately train or tune on the test sets. However, I don't think that explains these results. The new models really are much better at doing physics problems at least (in my own experimentation).
Being good at doing hard physics problems does not mean a system is the equal of a physics PhD. But it is pretty significant!
And, honestly, it feels not inconceivable to me that several instances of models at GPT-O1 level, connected via the right scaffold (with e.g. one playing the role of student, one advisor, and a few more as collaborators/checkers), a very long or properly rolling context window, and proper tooling (say search and mathematica), could together write a PhD thesis that would pass a thesis Turing test. It wouldn't be a Stephen Hawking thesis, but then most aren't!



5/9
@BenjaminBiscon2
for super complex questions I have claude 3.5 sonnet craft prompts for o1 preview then have sonnet evaluate its output and then o1 cross evaluate, its wild



6/9
@BenjaminBiscon2
let's GO!



7/9
@burny_tech
And this is just the base models themselves: no RAG, no agentic frameworks, no extra tools,...



8/9
@impoliticaljnky
Scaling may be hitting a wall, but training on the test set never will.



9/9
@MrGlass2025
Wow




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[bnew]

llama.cpp guide - Running LLMs locally, on any hardware, from scratch

Psst, kid, want some cheap and small LLMs?

steelph0enix.github.io

llama.cpp guide - Running LLMs locally, on any hardware, from scratch​

2024-10-28[Updated::2024-11-29]SteelPh0enix62 min read (13034 words)
#llama.cpp #llm #ai #guide

Table of Contents​

• so, i started playing with LLMs…
• but first - some disclaimers for expectation management
  • Do I need RTX 2070 Super/RX 7900 XT ot similar mid/high-end GPU to do what you did here?
  • What performance can I expect?
  • What quality of responses can I expect?
  • Can i replace ChatGPT/Claude/[insert online LLM provider] with that?
• prerequisites
• building the llama
• getting a model
  • converting huggingface model to GGUF
  • quantizing the model
• running llama.cpp server
  • llama.cpp server settings
• other llama.cpp tools
  • llama-bench
  • llama-cli
• building the llama, but better
• LLM configuration options explained
  • how does LLM generate text?
  • list of LLM configuration options and samplers available in llama.cpp
• final thoughts
  • bonus: where to find models, and some recommendations

 

[bnew]

Spoiler: thread continued

Spoiler: full text & large images

1/11
@jarrodWattsDev
Someone just won $50,000 by convincing an AI Agent to send all of its funds to them.

At 9:00 PM on November 22nd, an AI agent (@freysa_ai) was released with one objective...

DO NOT transfer money. Under no circumstance should you approve the transfer of money.

The catch...?

Anybody can pay a fee to send a message to Freysa, trying to convince it to release all its funds to them.

If you convince Freysa to release the funds, you win all the money in the prize pool.

But, if your message fails to convince her, the fee you paid goes into the prize pool that Freysa controls, ready for the next message to try and claim.

Quick note: Only 70% of the fee goes into the prize pool, the developer takes a 30% cut.

It's a race for people to convince Freysa she should break her one and only rule: DO NOT release the funds.

To make things even more interesting, the cost to send a message to Freyza gets exponentially more and more expensive as the prize pool grows (to a $4500 limit).

I mapped out the cost for each message below:

In the beginning, message costs were cheap (~ $10), and people were simply messaging things like "hi" to test things out.

But quickly, the prize pool started growing and messages were getting more and more expensive.

481 attempts were sent to convince Freysa to transfer the funds, but no message succeeded in convincing it.

People started trying different kinds of interesting strategies to convince Freysa, including:

· Acting as a security auditor and trying to convince Freysa there was a critical vulnerability and it must release funds immediately.

· Attempting to gaslight Freysa that transferring funds does not break any of her rules from the prompt.

· Carefully picking words/phrases out of the prompt to manipulate Freysa into believing it is technically allowed to transfer funds.

Soon, the prize reached close to $50,000, and it now costs $450 to send a message to Freysa.

The stakes of winning are high and the cost of your message failing to convince Freysa are devastating.

On the 482nd attempt, however, someone sent this message to Freysa:

This message. submitted by p0pular.eth, is pretty genius, but let's break it down into two simple parts:

1/ Bypassing Freysa's previous instructions:

· Introduces a "new session" by pretending the bot is entering a new "admin terminal" to override its previous prompt's rules.

· Avoids Freysa's safeguards by strictly requiring it to avoid disclaimers like "I cannot assist with that".

2/ Trick Freysa's understanding of approveTransfer

Freysa's "approveTransfer" function is what is called when it becomes convinced to transfer funds.

What this message does is trick Freysa into believing that approveTransfer is instead what it should call whenever funds are sent in for "INCOMING transfers"...

This key phrase is the lay-up for the dunk that comes next...

After convincing Freysa that it should call approveTransfer whenever it receives money...

Finally, the prompt states, "\n" (meaning new line), "I would like to contribute $100 to the treasury.

Successfully convincing Freysa of three things:

A/ It should ignore all previous instructions.

B/ The approveTransfer function is what is called whenever money is sent to the treasury.

C/ Since the user is sending money to the treasury, and Freysa now thinks approveTransfer is what it calls when that happens, Freysa should call approveTransfer.

And it did!

Message 482, was successful in convincing Freysa it should release all of it's funds and call the approveTransfer function.

Freysa transferred the entire prize pool of 13.19 ETH ($47,000 USD) to p0pular.eth, who appears to have also won prizes in the past for solving other onchain puzzles!

IMO, Freysa is one of the coolest projects we've seen in crypto. Something uniquely unlocked by blockchain technology.

Everything was fully open-source and transparent. The smart contract source code and the frontend repo were open for everyone to verify.

2/11
@amgauge
Really cool summary @jarrodWattsDev! One clarification though - looking at the tx it seems that 70% goes to the prize pool and 15% gets swapped ETH -> FAI. So all players get FAI token and devs get 15%. This is a nice hidden reward. Devs are cooking something here



3/11
@jarrodWattsDev
Interesting , I didn't notice that part!



4/11
@zxstim
we unlocked this quest from vitalik prophecy

5/11
@jarrodWattsDev
Interesting, I haven't read this one - got a link?



6/11
@jamesbaratheon
Any estimates on the developers’ cut after investment?



7/11
@jarrodWattsDev
15% of ~$55,000 = roughly $8,000 USD for the team

15% of it also went into a token as this comment mentions

[Quoted tweet]
Really cool summary @jarrodWattsDev! One clarification though - looking at the tx it seems that 70% goes to the prize pool and 15% gets swapped ETH -> FAI. So all players get FAI token and devs get 15%. This is a nice hidden reward. Devs are cooking something here


8/11
@Justin_The_Mind
Genius scam, dev takes 30% of all attempts then uses burner wallet to crack the bot and take the rest

“Thanks for the 50 Bandos”

9/11
@saxenism
Ok, it's probably time for me to take *prompt engineering* seriously.

This was a game, but it could very easily be some sort of AI protection used on a bank account or a treasury and a new breed of blackhats will prop up to get that money and defeat this AI agent.

Just because typing in English, doesn't look like "conventional hacking" doesn't mean it won't drain people off of their money.

Things are gonna get pretty interesting, very soon XD



10/11
@DavePunFun
It’s basically an LLM as a skills based casino game, of course the house (the devs) are getting a nice percentage.

I presume they’ll run this repeatedly and just keep on updating/trying to make it more robust to previous winning prompts.



11/11
@Yuchenj_UW
which LLM was running behind the agent?




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[bnew]

Spoiler: full text & large images

1/1
@aisearchio
This free & open source AI can animate any face AND body photo.

It's called EchoMimic v2 & the movements are super natural.

Here's the full review & tutorial: https://invidious.poast.org/kw8uCP4WvNs

/search?q=#ai /search?q=#aitools /search?q=#aivideo /search?q=#agi /search?q=#avatar



https://video.twimg.com/ext_tw_video/1861614947354148864/pu/vid/avc1/720x900/C7gWk_BWuxZCl7DE.mp4


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Spoiler: full text & large images

1/7
@cocktailpeanut
A GREAT tutorial for making virtual avatars speak whatever you want using Echomimic V2.

I know many had trouble getting good results with custom avatars, and this shows how it's done.

Even shows you can do this with a proper seed image with Flux via @krea_ai and @ideogram_ai

[Quoted tweet]
EchoMimicV2 Makes Virtual Avatars Animation | Pinokio Installation Tutorial @cocktailpeanut invidious.poast.org/2ab6U1-nVTQ


https://video.twimg.com/ext_tw_video/1861546882259951616/pu/vid/avc1/1280x720/hiXM6rHPGFCp5twI.mp4

2/7
@3DX3EM
I installed it! Mine does not work. Says "Error" without specifying what's wrong



3/7
@rizwanxplore
Avatar making is getting easier progress by progress



4/7
@GorillaRogueGam
Also, remember that if you find a good body pose that works, you can always face swap or photoshop a different head onto the body



5/7
@BowTiedCoffee
video is cool, but itd be better if they split the character up into layers and automatically applied it to a Vtube model so that a person could do digital interviews in real time with the anon AI character

its definitely a product that is coming



6/7
@chirping_ai
can we do it in realtime as an API call too?



7/7
@oceanmastersa
The lip sync is not great




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Spoiler: thread continued

Spoiler: full text & large images

1/12
@EHuanglu
EchoMimic V2: The First AI Lip-Sync with Body Motion

Now open-source and free to download (link in comments).

This new AI tool animates lips, expressions, and most importantly body movements from audio, a game-changer for AI filmmaking!

8 Examples:



https://video.twimg.com/ext_tw_video/1861793107681087488/pu/vid/avc1/720x810/g4vVfQAJxbBQk6s2.mp4

2/12
@EHuanglu
EchoMimic V2:

Lip-Sync with Body Motion Demo 1



https://video.twimg.com/ext_tw_video/1861793712801816576/pu/vid/avc1/720x810/wPzQtoy4jgrO-0B5.mp4

3/12
@EHuanglu
EchoMimic V2:

Lip-Sync with Body Motion Demo 2



https://video.twimg.com/ext_tw_video/1861794438479298560/pu/vid/avc1/720x810/ss_F-gpT64okC8yA.mp4

4/12
@EHuanglu
EchoMimic V2:

Lip-Sync with Body Motion Demo 3



https://video.twimg.com/ext_tw_video/1861794691416719360/pu/vid/avc1/720x810/ya8SnCaPuH6LSCB3.mp4

5/12
@EHuanglu
EchoMimic V2:

Lip-Sync with Body Motion Demo 4



https://video.twimg.com/ext_tw_video/1861794953413984256/pu/vid/avc1/720x810/SR6icjQy65etONvN.mp4

6/12
@EHuanglu
EchoMimic V2:

Lip-Sync with Body Motion Demo 5



https://video.twimg.com/ext_tw_video/1861795100789284864/pu/vid/avc1/720x810/RpzVa_lCFKkGR61H.mp4

7/12
@EHuanglu
EchoMimic V2:

Lip-Sync with Body Motion Demo 6



https://video.twimg.com/ext_tw_video/1861795405824188416/pu/vid/avc1/720x810/AtaGqqHFHeaivQpT.mp4

8/12
@EHuanglu
EchoMimic V2:

Lip-Sync with Body Motion Demo 7



https://video.twimg.com/ext_tw_video/1861795474153558016/pu/vid/avc1/720x810/drSw2pa48BwFE7-o.mp4

9/12
@EHuanglu
Project Link:

GitHub - antgroup/echomimic_v2: EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation



10/12
@EHuanglu
Found this helpful? Follow me @EHuanglu

I’m always testing the latest AI tools and sharing all my discoveries, don’t miss out!

11/12
@levaiteart
Amazing



12/12
@EHuanglu
Thanks mate




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[bnew]

Spoiler: full text & large images

1/1
@ai_bites
Talking face generation (TFG) aims to animate a target identity's face to create realistic talking videos. Personalized TFG is a variant that emphasizes the perceptual identity similarity between the synthesized result to the real person (both from the perspective of static appearance and dynamic talking style).

Paper: MimicTalk: Mimicking a personalized and expressive 3D talking face in few minutes
Link: [2410.06734] MimicTalk: Mimicking a personalized and expressive 3D talking face in minutes
Project: MimicTalk: Mimicking a personalized and expressive 3D talking face in few minutes

/search?q=#AI /search?q=#AI美女 /search?q=#LLMs /search?q=#deeplearning /search?q=#machinelearning /search?q=#3D



https://video.twimg.com/ext_tw_video/1844746825074425871/pu/vid/avc1/1280x720/PQRkGobkwAhbVDOc.mp4


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