Researchers released the NuminaMath datasets: the largest collection of ~1M math competition problem-solution pairs, ranging in difficulty from junior challenge to Math Olympiad preselection.
These datasets were used to win the 1st Progress Prize of the AI Math Olympiad and consist of two subsets:
1. Chain of Thought (CoT): 860k problem-solution pairs templated with CoT to enhance mathematical reasoning in natural language
2. Tool-integrated reasoning (TIR): 73k synthetic solutions derived from GPT-4 with code-execution feedback to decompose hard problems into simpler subproblems that can be solved with Python
Models trained on NuminaMath achieve best-in-class performance among open weight models and approach or surpass proprietary models on math competition benchmarks.
Tech report along with the training and inference code.
These datasets were used to win the 1st Progress Prize of the AI Math Olympiad and consist of two subsets:
1. Chain of Thought (CoT): 860k problem-solution pairs templated with CoT to enhance mathematical reasoning in natural language
2. Tool-integrated reasoning (TIR): 73k synthetic solutions derived from GPT-4 with code-execution feedback to decompose hard problems into simpler subproblems that can be solved with Python
Models trained on NuminaMath achieve best-in-class performance among open weight models and approach or surpass proprietary models on math competition benchmarks.
Tech report along with the training and inference code.
huggingface.co
NuminaMath - a AI-MO Collection
Datasets and models for training SOTA math LLMs. See our GitHub for training & inference code: https://github.com/project-numina/aimo-progress-prize
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An article on how you can dynamically spawn and place your content objects on specific surface types using Meta XR Mixed Reality Utility Kit (MRUK), based on Scene Understanding's semantically labeled surfaces.
Mixed Reality Now โ ARโVRโMRโXR Design & Development Stories
Building MR apps using physical surfaces with Meta MR Utility Kit โ Mixed Reality Now
Being able to use the real-life physical environment as a canvas is one of the most exciting parts of Mixed Reality for us designers, developers, and creators. With Meta Quest's various sophisticated spatial awareness capabilities such as Scene Understandingโฆ
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OpenAI CEO Sam Altman low-income people $1,000/month for three years, no strings attached. Hereโs What Happened.
Now, the results of one of the largest guaranteed-basic-income studies are in.
Back in 2019, 3,000 Texas and Illinois residents were enrolled in this guaranteed-basic-income study.
The experiment was funded by Sam Altman, who raised $60 million for the study ($14 million of which was his own money).
All participants had incomes below $28,000.
1/3 got $1,000/month for three years while the remaining control group members got $50 per month.
For those who received the $1,000 payments, overall spending increased by ~$310/month.
Most of that money went toward food, transportation, and rent.
There was also an increase in offering financial support to others.
However, that doesn't mean those who received the $1,000 payments saw improvements across the board.
There was no "direct evidence of improved access to healthcare or improvements to physical and mental health," researchers found.
While there was an increase in life satisfaction for a short time at the start of the study, it didn't last.
"Cash alone cannot address challenges such as chronic health conditions, lack of childcare, or the high cost of housing."
But there were certainly some benefits that can't be ignored.
For those receiving $1,000/month, their total individual savings spiked by almost 25%.
And incomes rose significantly for all groups.
Recipients of the $1,000/month saw incomes rise from ~$30,000 to $45,710, on average.
Incomes for those in the control group rose even more, to $50,970.
"Cash offers flexibility and may increase agency to make employment decisions that align with recipients' individual circumstances, goals, and values," according to researchers.
As for what the study participants themselves felt, most couldn't believe their luck when selected to participate.
"Looking back, I regret that I didn't save more of it," one said.
"It's almost like a miracle," another said.
Now, the results of one of the largest guaranteed-basic-income studies are in.
Back in 2019, 3,000 Texas and Illinois residents were enrolled in this guaranteed-basic-income study.
The experiment was funded by Sam Altman, who raised $60 million for the study ($14 million of which was his own money).
All participants had incomes below $28,000.
1/3 got $1,000/month for three years while the remaining control group members got $50 per month.
For those who received the $1,000 payments, overall spending increased by ~$310/month.
Most of that money went toward food, transportation, and rent.
There was also an increase in offering financial support to others.
However, that doesn't mean those who received the $1,000 payments saw improvements across the board.
There was no "direct evidence of improved access to healthcare or improvements to physical and mental health," researchers found.
While there was an increase in life satisfaction for a short time at the start of the study, it didn't last.
"Cash alone cannot address challenges such as chronic health conditions, lack of childcare, or the high cost of housing."
But there were certainly some benefits that can't be ignored.
For those receiving $1,000/month, their total individual savings spiked by almost 25%.
And incomes rose significantly for all groups.
Recipients of the $1,000/month saw incomes rise from ~$30,000 to $45,710, on average.
Incomes for those in the control group rose even more, to $50,970.
"Cash offers flexibility and may increase agency to make employment decisions that align with recipients' individual circumstances, goals, and values," according to researchers.
As for what the study participants themselves felt, most couldn't believe their luck when selected to participate.
"Looking back, I regret that I didn't save more of it," one said.
"It's almost like a miracle," another said.
OpenResearch
Findings
OpenResearch is a nonprofit research lab that seeks to answer open-ended questions.
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Enterprise AI Focused startup Cohere raised a $5.5 Bn round
Highlights:
- Revenue of $35 MM ARR, up from $13 MM ARR end of 2023
- $5.5 Bn valuation implies a 150x (ish) price to sales multiple. 2x valuation from last year
Investors:
The company has raised $500 million in a Series D funding, it plans to announce on Monday.
The round was led by Canadian pension investment manager PSP Investments, alongside a syndicate of additional new backers including investors at Cisco Systems Inc., Japanโs Fujitsu, chipmaker Advanced Micro Devices Inc.โs AMD Ventures and Canadaโs export credit agency EDC.
Customers: Cohere has customers across a wide range of industries.
They include banks, tech companies and retailers.
One luxury consumer brand is using a virtual shopping tool Cohere built to help workers suggest products to customers. Toronto-Dominion Bank, a new customer, will use Cohereโs AI for tasks such as answering questions based on financial documents
Sourcing:
Cohereโs models can be used across 10 languages, including English, Spanish, Chinese, Arabic and Japanese, and its models can cite sources in answers.
Highlights:
- Revenue of $35 MM ARR, up from $13 MM ARR end of 2023
- $5.5 Bn valuation implies a 150x (ish) price to sales multiple. 2x valuation from last year
Investors:
The company has raised $500 million in a Series D funding, it plans to announce on Monday.
The round was led by Canadian pension investment manager PSP Investments, alongside a syndicate of additional new backers including investors at Cisco Systems Inc., Japanโs Fujitsu, chipmaker Advanced Micro Devices Inc.โs AMD Ventures and Canadaโs export credit agency EDC.
Customers: Cohere has customers across a wide range of industries.
They include banks, tech companies and retailers.
One luxury consumer brand is using a virtual shopping tool Cohere built to help workers suggest products to customers. Toronto-Dominion Bank, a new customer, will use Cohereโs AI for tasks such as answering questions based on financial documents
Sourcing:
Cohereโs models can be used across 10 languages, including English, Spanish, Chinese, Arabic and Japanese, and its models can cite sources in answers.
Bloomberg.com
AI Startup Cohere Valued at $5.5 Billion in New Funding Round
The Canadian AI unicorn doesn't have a viral chatbot, but itโs signed on hundreds of corporate clients.
Apple presents SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models
Comparable or better performance compared to SotA Video LLMs that are fine-tuned on video datasets while being training-free.
Comparable or better performance compared to SotA Video LLMs that are fine-tuned on video datasets while being training-free.
arXiv.org
SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large...
We propose SlowFast-LLaVA (or SF-LLaVA for short), a training-free video large language model (LLM) that can jointly capture detailed spatial semantics and long-range temporal context without...
Chinese team developed a fabrication method to produce a semiconductor material just 0.7 nanometres thick.
A team led by Liu Kaihui of Peking University, Liu Can of Renmin University, and Zhang Guangyu of the Institute of Physics at the Chinese Academy of Sciences developed a fabrication method to produce a semiconductor material just 0.7 nanometres thick.
The researchersโ findings, which were published in the peer-reviewed journal Science on July 5, address a key barrier to reducing the size of traditional silicon-based chips โ as devices shrink, silicon chips run into physical limits that affect their performance.
The scientists explored two-dimensional (2D) transition-metal dichalcogenides (TMDs) as an alternative to silicon, with a thickness of just 0.7 nanometres compared to siliconโs typical 5-10 nanometres.
TMDs also consume less power and have superior electron transport properties, making them ideal for the ultra-scaled down transistors that will be a feature of next-generation electronic and photonic chips.
However, producing TMDs has been challenging โ until now. According to the paper, the technique developed by the scientists allows them to quickly produce high-quality 2D crystals in seven formulations, making mass production feasible.
The traditional fabrication process, which involves layer-by-layer assembly of atoms on a substrate โ like building a wall with bricks โ often results in crystals with insufficient purity, Liu Kaihui told state news agency Xinhua.
โThis is due to uncontrollable atomic arrangements in crystal growth and the accumulation of impurities and defects,โ he said.
The team arranged the first layer of atoms on the substrate as if they were following the traditional process. However, subsequent atoms were added between the substrate and the first crystal layer, pushing upwards like bamboo shoots to form new layers.
A team led by Liu Kaihui of Peking University, Liu Can of Renmin University, and Zhang Guangyu of the Institute of Physics at the Chinese Academy of Sciences developed a fabrication method to produce a semiconductor material just 0.7 nanometres thick.
The researchersโ findings, which were published in the peer-reviewed journal Science on July 5, address a key barrier to reducing the size of traditional silicon-based chips โ as devices shrink, silicon chips run into physical limits that affect their performance.
The scientists explored two-dimensional (2D) transition-metal dichalcogenides (TMDs) as an alternative to silicon, with a thickness of just 0.7 nanometres compared to siliconโs typical 5-10 nanometres.
TMDs also consume less power and have superior electron transport properties, making them ideal for the ultra-scaled down transistors that will be a feature of next-generation electronic and photonic chips.
However, producing TMDs has been challenging โ until now. According to the paper, the technique developed by the scientists allows them to quickly produce high-quality 2D crystals in seven formulations, making mass production feasible.
The traditional fabrication process, which involves layer-by-layer assembly of atoms on a substrate โ like building a wall with bricks โ often results in crystals with insufficient purity, Liu Kaihui told state news agency Xinhua.
โThis is due to uncontrollable atomic arrangements in crystal growth and the accumulation of impurities and defects,โ he said.
The team arranged the first layer of atoms on the substrate as if they were following the traditional process. However, subsequent atoms were added between the substrate and the first crystal layer, pushing upwards like bamboo shoots to form new layers.
South China Morning Post
Have Chinese scientists cracked code to making ultra-thin semiconductor material?
Research by Chinese team addresses key barrier to reducing the size of traditional silicon-based chips.
Can an organism understand the code it is programmed in? Humans are getting close to this with new Generative AI models trained directly on biological data.
Anyone reading this post is programmed by the biological code - DNA, RNA, and Proteins.
With LLMs now being trained directly on biological code, we are rapidly moving towards empowering ourselves, as a species, with the toolset to decipher our own programming language better.
So, how exactly are LLMs trained directly on biological data? Let's take protein data as an example, but the same paradigm applies to DNA or RNA.
This is a sneak peek into work at Converge Bio. Here are the five steps:
1. ๐๐๐๐ฒ๐บ๐ฏ๐น๐ฒ ๐ฎ ๐บ๐ฎ๐๐๐ถ๐๐ฒ ๐ฝ๐ฟ๐ผ๐๐ฒ๐ถ๐ป ๐๐ฒ๐พ๐๐ฒ๐ป๐ฐ๐ฒ ๐ฑ๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ: With genome sequencing becoming cheaper every year, we now have billions of publicly available protein sequences for building these databases.
2. ๐ง๐ผ๐ธ๐ฒ๐ป๐ถ๐๐ฒ ๐๐ต๐ฒ ๐ฝ๐ฟ๐ผ๐๐ฒ๐ถ๐ป ๐ฑ๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ: In this step, they build a dictionary of the "words" of the protein language. These words are called tokens and they are the atomic elements that LLMs learn from. There is a huge amount of research we and others are doing on how to best divide the protein language into tokens.
3. ๐จ๐ป๐๐๐ฝ๐ฒ๐ฟ๐๐ถ๐๐ฒ๐ฑ ๐๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ถ๐๐ฒ ๐ฃ๐ฟ๐ฒ-๐ง๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด (๐๐ฃ๐ง): Train a transformer-based model by hiding part of the sequence and training the model to fill in the missing tokens. They now have a model that deeply understands the statistical distribution of information in our massive database. At this stage, the trained model is often referred to as a foundational model.
4. ๐ฆ๐๐ฝ๐ฒ๐ฟ๐๐ถ๐๐ฒ๐ฑ ๐บ๐๐น๐๐ถ-๐๐ฎ๐๐ธ ๐๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด. They now train the foundational model to understand not only the statistical distribution of information in the data but also how that information translates into real-world biological traits. This is done by training the model on any labeled trusted dataset you can get your hands on that connects a protein sequence with a biological outcome. In the protein context, here are a few examples - Protein structures, Protein annotations, and binding affinity experimental data. Research in AI shows that by using this paradigm, the model becomes "smarter" when introduced to multiple diverse tasks (similar to a child learning new skills).
5. ๐๐ถ๐ป๐ฒ ๐๐๐ป๐ถ๐ป๐ด: Given a new biological question, you can now fine-tune the model with a relatively small dataset and use it to predict complex biological interactions, explain the model's decision in a protein sequence context, and generate novel and better-performing proteins.
Anyone reading this post is programmed by the biological code - DNA, RNA, and Proteins.
With LLMs now being trained directly on biological code, we are rapidly moving towards empowering ourselves, as a species, with the toolset to decipher our own programming language better.
So, how exactly are LLMs trained directly on biological data? Let's take protein data as an example, but the same paradigm applies to DNA or RNA.
This is a sneak peek into work at Converge Bio. Here are the five steps:
1. ๐๐๐๐ฒ๐บ๐ฏ๐น๐ฒ ๐ฎ ๐บ๐ฎ๐๐๐ถ๐๐ฒ ๐ฝ๐ฟ๐ผ๐๐ฒ๐ถ๐ป ๐๐ฒ๐พ๐๐ฒ๐ป๐ฐ๐ฒ ๐ฑ๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ: With genome sequencing becoming cheaper every year, we now have billions of publicly available protein sequences for building these databases.
2. ๐ง๐ผ๐ธ๐ฒ๐ป๐ถ๐๐ฒ ๐๐ต๐ฒ ๐ฝ๐ฟ๐ผ๐๐ฒ๐ถ๐ป ๐ฑ๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ: In this step, they build a dictionary of the "words" of the protein language. These words are called tokens and they are the atomic elements that LLMs learn from. There is a huge amount of research we and others are doing on how to best divide the protein language into tokens.
3. ๐จ๐ป๐๐๐ฝ๐ฒ๐ฟ๐๐ถ๐๐ฒ๐ฑ ๐๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ถ๐๐ฒ ๐ฃ๐ฟ๐ฒ-๐ง๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด (๐๐ฃ๐ง): Train a transformer-based model by hiding part of the sequence and training the model to fill in the missing tokens. They now have a model that deeply understands the statistical distribution of information in our massive database. At this stage, the trained model is often referred to as a foundational model.
4. ๐ฆ๐๐ฝ๐ฒ๐ฟ๐๐ถ๐๐ฒ๐ฑ ๐บ๐๐น๐๐ถ-๐๐ฎ๐๐ธ ๐๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด. They now train the foundational model to understand not only the statistical distribution of information in the data but also how that information translates into real-world biological traits. This is done by training the model on any labeled trusted dataset you can get your hands on that connects a protein sequence with a biological outcome. In the protein context, here are a few examples - Protein structures, Protein annotations, and binding affinity experimental data. Research in AI shows that by using this paradigm, the model becomes "smarter" when introduced to multiple diverse tasks (similar to a child learning new skills).
5. ๐๐ถ๐ป๐ฒ ๐๐๐ป๐ถ๐ป๐ด: Given a new biological question, you can now fine-tune the model with a relatively small dataset and use it to predict complex biological interactions, explain the model's decision in a protein sequence context, and generate novel and better-performing proteins.
A neural modeling tour de force.
New paper "Towards enhanced functionality of vagus neuroprostheses through in silico optimized stimulation"
Paper.
A main contributions:
1. Researchers developed a realistic and anatomically accurate #model of the vagus nerve.
2. Developed #computational methods to make simulations efficient (from days to minutes of computation).
3. Devised a method using #physiological experiments to functionalize the models in vivo during animal experiments.
4. Optimized #neurosimulation in silico reducing side effects related to laryngeal contractions by ~70% when using VNS to reduce heart rate.
5. Shared the entire modeling framework here for public reuse and developed an online platform to showcase the models.
This multidisciplinary work, featuring histological data, computational modeling and animal experiments.
New paper "Towards enhanced functionality of vagus neuroprostheses through in silico optimized stimulation"
Paper.
A main contributions:
1. Researchers developed a realistic and anatomically accurate #model of the vagus nerve.
2. Developed #computational methods to make simulations efficient (from days to minutes of computation).
3. Devised a method using #physiological experiments to functionalize the models in vivo during animal experiments.
4. Optimized #neurosimulation in silico reducing side effects related to laryngeal contractions by ~70% when using VNS to reduce heart rate.
5. Shared the entire modeling framework here for public reuse and developed an online platform to showcase the models.
This multidisciplinary work, featuring histological data, computational modeling and animal experiments.
Zenodo
VaStim - Towards enhanced functionality of vagus neuroprostheses through in-silico optimized stimulation
The online platform showcasing the computational models can be accessed at https://neuroeng-hen.ethz.ch/online/
๐1
Machines have surpassed humans in empathy.
New evidence: AI beat humans in detecting emotions and giving support. As long as people didn't know AI created the messages, they felt more heard.
It's not that AI is so good. It's that we often fail to use our emotional intelligence.
New evidence: AI beat humans in detecting emotions and giving support. As long as people didn't know AI created the messages, they felt more heard.
It's not that AI is so good. It's that we often fail to use our emotional intelligence.
๐ฅ3โก1๐1
The future of robotics is in your hands. Literally. A new paper: R+X
A person records everyday activities while wearing a camera.A robot passively learns those skills.
No labels, no training.
A person records everyday activities while wearing a camera.A robot passively learns those skills.
No labels, no training.
Robot Learning Lab
R + X
R + X - The Robot Learning Lab at Imperial College London
๐ฅ6
๐Starlink now operating on over 1000 aircraft, said Elon Musk.
๐2
Answer AI released gpu.cpp
This is a really exciting project which provides a device-independent way to use GPU compute. No more writing separate code for CUDA, AMD, Mac, and Intel GPUs!
GitHub.
This is a really exciting project which provides a device-independent way to use GPU compute. No more writing separate code for CUDA, AMD, Mac, and Intel GPUs!
GitHub.
Answer.AI
gpu.cpp: portable GPU compute for C++ with WebGPU โ Answer.AI
Practical AI R&D
OpenAI to further block access by mainland China, Hong Kong-based developers
The move is set to deal a blow to Chinese companies developing services based on OpenAIโs LLM Llama-3.1.
A number of AI start-ups in China are building apps based on OpenAIโs large models, which also generate revenue for OpenAI, the person said, adding that if OpenAI strengthens its regulations, Chinese developers will have to turn to local alternatives.
Zhipu AI, one of Chinaโs top generative AI start-ups, said it would help affected developers transfer to its platform.
For more on startups, and open source LLMs in China, see.
The move is set to deal a blow to Chinese companies developing services based on OpenAIโs LLM Llama-3.1.
A number of AI start-ups in China are building apps based on OpenAIโs large models, which also generate revenue for OpenAI, the person said, adding that if OpenAI strengthens its regulations, Chinese developers will have to turn to local alternatives.
Zhipu AI, one of Chinaโs top generative AI start-ups, said it would help affected developers transfer to its platform.
For more on startups, and open source LLMs in China, see.
South China Morning Post
OpenAI curbs access by mainland China, Hong Kong-based developers
The move is set to deal a blow to Chinese companies developing services based on OpenAIโs large language models.
โก๏ธGoogle presented the first AI to solve International Mathematical Olympiad problems at a silver medalist level
It combines AlphaProof, a new breakthrough model for formal reasoning, and AlphaGeometry 2, an improved version of previous system.
It combines AlphaProof, a new breakthrough model for formal reasoning, and AlphaGeometry 2, an improved version of previous system.
Google DeepMind
AI achieves silver-medal standard solving International Mathematical Olympiad problems
Breakthrough models AlphaProof and AlphaGeometry 2 solve advanced reasoning problems in mathematics
๐ฅ3
โก๏ธOpenAI is entering the search market. 10,000 test users will get early access.
They โre testing SearchGPT, a temporary prototype of new AI search features.
They โre testing SearchGPT, a temporary prototype of new AI search features.
Openai
SearchGPT is a prototype of new AI search features
Weโre testing SearchGPT, a temporary prototype of new search features that give you fast and timely answers with clear and relevant sources.
Unlock the Future of Mobile AI!
Salesforce introduced MobileAIBench is a new open source framework for assessing mobile-readiness of your LLMs and LMMs.
Quickly and easily test your models on a variety of benchmarks spanning NLP, multimodality, and trust & safety. Using iOS app, test your modelโs on-device performance such as memory consumption, latency etc.
Code.
Salesforce introduced MobileAIBench is a new open source framework for assessing mobile-readiness of your LLMs and LMMs.
Quickly and easily test your models on a variety of benchmarks spanning NLP, multimodality, and trust & safety. Using iOS app, test your modelโs on-device performance such as memory consumption, latency etc.
Code.
arXiv.org
MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases
The deployment of Large Language Models (LLMs) and Large Multimodal Models (LMMs) on mobile devices has gained significant attention due to the benefits of enhanced privacy, stability, and...
MintNeuro will be making its first function-specific integrated circuits available to selected partners for evaluation in the coming months
These compact, low-power mixed-signal integrated circuits are designed specifically with implantable closed loop #Neurotech and #Bioelectronics applications in mind.
These compact, low-power mixed-signal integrated circuits are designed specifically with implantable closed loop #Neurotech and #Bioelectronics applications in mind.
mintneuro
MintNeuro launches EXPLORE โ mintneuro
New paper on end-to-end deep learning for relational databases
RDL learns directly on structured data across multiple tables, eliminates the need for feature engineering, and extends AI use cases to personalization, fraud, forecasting and others:
- Deep representation learning on across multiple tables, eliminating the need for single-table feature engineering.
- Launching RelBench, a new benchmark and testing suite to facilitate research.
RDL learns directly on structured data across multiple tables, eliminates the need for feature engineering, and extends AI use cases to personalization, fraud, forecasting and others:
- Deep representation learning on across multiple tables, eliminating the need for single-table feature engineering.
- Launching RelBench, a new benchmark and testing suite to facilitate research.
๐2
Morgan Stanley: Nvidia anticipates shipping 60,000 to 70,000 B200 server cabinets, each priced between $2 million and $3 million next year
It translates to an estimated annual revenue of at least $210 billion from these machines.
It translates to an estimated annual revenue of at least $210 billion from these machines.
Tom's Hardware
Nvidia and partners could charge up to $3 million per Blackwell server cabinet โ analysts project over $200 billion in revenueโฆ
The industry is going to need 60,000 โ 70,000 B200-based servers.
Mistral dropped Large 123BโDense, multilingual (12 languages), and 128K context.
Comes as instruct-only model checkpoint, with performance less than 405B but higher than L3.1 70B. Released under non-commercial license.
Comes as instruct-only model checkpoint, with performance less than 405B but higher than L3.1 70B. Released under non-commercial license.
huggingface.co
mistralai/Mistral-Large-Instruct-2407 ยท Hugging Face
Weโre on a journey to advance and democratize artificial intelligence through open source and open science.