Smells Like ML @smellslikeml
Develop and Deploy AI Apps with the @remyxai Agent Keeper of Build-In-Public-Zoo https://t.co/omlFsnemgV #AI #deeplearning #machinelearning remyx.ai San Francisco, CA Joined October 2018-
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LLaMA3 and Phi3 have made the splash this week in LLM Arena. But how strong is their visual understanding ability? ⚡We release LLaMA3-Vision and Phi3-Vision models that beat their larger size LLM competitors. Github: github.com/mbzuai-oryx/LL… HF: huggingface.co/collections/MB…
We're always hearing about closed versus open AI. How about some attention to the topic of centralized versus decentralized AI. When a centralized service is down, it doesn't matter if it was open or closed, everybody suffers.
🚨 Code Release 🚨 MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation github.com/KunpengSong/Mo…
Do you know a developer who can afford to make decisions about which AI to deploy based on hearsay from TechCrunch journalists? Where is the scientific method here? 🔍 When e-commerce and content streaming giants want to help users make sense of a massive catalogue, they use…
Do you know a developer who can afford to make decisions about which AI to deploy based on hearsay from TechCrunch journalists? Where is the scientific method here? 🔍 When e-commerce and content streaming giants want to help users make sense of a massive catalogue, they use…
Just as the rising tide lifts all boats, better bases beget better fine-tunes with releases like llama3. The architecture is the platform, what will you build? Soon the same data curation and generation tools available to researchers will be packaged for use for any…
The choice for the next LLM to fine-tune is easy! What will you build? FREE Fine tune for your llama3-8B @remyxai Stay tuned for data generation and curation tools🦙
Is it though? Meta recommends LoRA/QLoRA to adapt llama3 and LIMA actually says that 1000 examples are sufficient to specialize your well-trained base model. Many have found LLMs can pick up new knowledge through fine-tuning, but it's not about a bigger dataset and full-fine…
Is it though? Meta recommends LoRA/QLoRA to adapt llama3 and LIMA actually says that 1000 examples are sufficient to specialize your well-trained base model. Many have found LLMs can pick up new knowledge through fine-tuning, but it's not about a bigger dataset and full-fine…
Image what you can create after synthesizing data like this with Llama 3.🤯 Stay tuned for data generation tooling coming soon to Remyx AI🙌
Image what you can create after synthesizing data like this with Llama 3.🤯 Stay tuned for data generation tooling coming soon to Remyx AI🙌
Llama3 released! llama.meta.com/llama3/ github.com/meta-llama/lla…
While reviewing an A/B testing platform, I found that a couple frontier model providers are customers. What happened to "evals is all you need?" But when the topic of LLM evaluation comes up, we only hear about the results of the LM Eval Harness or chatbot arena. 🤔 More than…
Who's beating GPT-4 today? 🥊 There is less certainty than ever before about the best LLM to use in your application. We know the benchmarks are bunk and lately the arena rankings are in flux too. There are application-specific benchmarks and you can even customize your…
When performance counts, you'll want your server to be GPU-aware for greater throughput using features like dynamic batching. But grappling with configuration and environment setup is a time suck. Simplified deployments doesn't have to mean you need to give up on performance.…
Hard to beat LoRA with good data and it's cheaper than ever to synthesize and curate the right samples for your application.
Hard to beat LoRA with good data and it's cheaper than ever to synthesize and curate the right samples for your application.
Getting top performance from your LLM requires fine-tuning on the right data. In LESS, researchers show how to retrieve the best training samples. Compared to a few months ago, we know so much more about cheaply synthesizing and curating high-quality data for LLM fine-tuning.…
Trying out CodecLM to synthesize data for fine-tuning LLMs. First, your seed text is encoded into instruction metadata. For example, if you start with the seed: "Develop a comprehensive marketing strategy for a B2B software company looking to increase its brand recognition and…
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776 Followers 1K Following Machine Learning Research Scientist @ToyotaResearch | PhD in AI and DL @GeorgiaTech | Researching @neural_fields | 3D Vision | Embodied AI
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8K Followers 5K Following 非专业脱口秀，全栈老码农，数字游民，爱自己，爱家人，爱苍老师。 曾发布在华军软件园的一部分软件开源了：https://t.co/CK8EeszRRl 专注AI，网络安全，系统架构、软件研发等前沿赚钱技术领域！ 购买课程或合法赚钱项目合作请加WX：HappyQQ868（添加时注明来自推上，非诚勿扰，谢谢)
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@sama So true sama. You should come onto my podcast and say lots of things in 30 seconds that would take others 5 minutes
SAM + Optical Flow = FlowSAM FlowSAM can discover and segment moving objects in a video and outperforms all previous approaches by a considerable margin in both single and multi-object benchmarks 🔥 robots.ox.ac.uk/~vgg/research/…
Run Apple's new OpenELM models in MLX LM thanks to @Prince_Canuma pip install -U mlx-lm 270M model in 16-bit runs quite fast on an 8GB M2 Mini (512 tokens at 115 toks/sec). Also pretty good quality for the size:
if you are using LoRA: divide the A matrix learning rate by 8 and multiply the B matrix learning rate by 8. you can thank me later
✨ Excited ✨ Wanna get a whirlwind tour of memorization in diffusion models, how to find it, how to mitigate it, drop by the talk! Will discuss all my 3 papers (including style memorization) on this topic. Papers: 1. arxiv.org/abs/2212.03860 (CVPR'23) 2.…
Our community is looking forward to welcoming @gowthami_s on Monday, May 6th as she presents her work on "Understanding and Mitigating Memorization in Diffusion Models" Learn more: cohere.com/events/c4ai-Go…
llama3-42b: Pruned llama3-70b huggingface.co/chargoddard/ll…
Best to think of meta's entire training regiment as the "pre-training phase". LIMA says that a fine-tune-of-a-fine-tune can specialize (needing as little as 1000 quality samples) but doesn't gain new capabilities outside of what was introduced in pretraining
@Teknium1 Of course, you can introduce knowledge through fine-tuning. That's how so many have gamed the benchmarks. The superficial alignment hypothesis says that most of the model's capabilities are introduced in pretraining. That's why many fine-tunes don't rank on the chatbot arena.…
The base model is the platform and specializing on the best quality data for your use case is key for your LLM application.
Is it though? Meta recommends LoRA/QLoRA to adapt llama3 and LIMA actually says that 1000 examples are sufficient to specialize your well-trained base model. Many have found LLMs can pick up new knowledge through fine-tuning, but it's not about a bigger dataset and full-fine…
The highest leverage way to improve the performance of your AI model is by designing your dataset. Part of the reason behind the impressive performance of the new Llama 3 model is their pipeline for curating public data as well as the synthetic data generated using Llama 2.
🥁 Llama3 is out 🥁 8B and 70B models available today. 8k context length. Trained with 15 trillion tokens on a custom-built 24k GPU cluster. Great performance on various benchmarks, with Llam3-8B doing better than Llama2-70B in some cases. More versions are coming over the next…
Would you build on top of APIs constantly changing for improved engagement from a broad, generic audience?
While reviewing an A/B testing platform, I found that a couple frontier model providers are customers. And yet when the topic of LLM evaluation comes up, we only hear about the results of the LM Eval Harness or chatbot arena. 🤔 More than any time before, the capabilities of…
Soon enough, a few sample demonstrations or context provided through a seed phrase will be enough to specialize your LLM. It's much cheaper to train with high-quality data as the cost per token continues to drop while our methods of synthesis and curation advance.
Hard to beat LoRA with good data and it's cheaper than ever to synthesize and curate the right samples for your application.
Definite shout-out to github.com/amitportnoy for catching my bug in the delta update for the Infini-Transformer implementation. Much appreciated!
llama recipes repo is a gem and lots of people have learnt valuable things from it. here's a multilingual extension recipe for the same. we should have a mistral recipe repo too.
Extending Llama to a new language github.com/meta-llama/lla…
Figure it out that tinyllava is a good choice in v100 settings.
Maybe stupid, but does 8xV100-16G support the training of llava-1.6 or other small muliti-modal language model?
Getting CV running on a flipper zero is based
Many are surprised to find that they can do #computervision on a @flipper_zero But where there's an ESP32, there's a way and so we're hacking our new flipperzero-mayhem hat: github.com/eried/flipperz… Learn how to automate your attacks: hackster.io/myx/visual-vul…
@remyxai and @smellslikeml were featured over on the IG - we want to see more tools that have been created with Remyx! #airevolution #ai #artificialintelligence #podcast instagram.com/p/C5nqnkVv6hu/…
@chris_j_paxton I feel like everyone who tries to create a “ROS alternative” doesn’t realize how many design decisions (and effort) went into ROS to truly make it “modular” across most robot domains. Maybe this just speaks to the frustration of the steep learning curve ROS has.
The barriers to writing code keep going down. Complete applications are within reach, more than ever it is not about the code but about the problems being solved and how the solution is distributed. Spoke to someone who never wrote code in their life before LLMs, shipping…
@bitcloud “Poor Faulkner. Does he really think big emotions come from big words? He thinks I don’t know the ten-dollar words. I know them all right. But there are older and simpler and better words, and those are the ones I use.” ― Ernest Hemingway
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