Made with late interaction I'd love to recreate the dataset to see a proper apache 2.0 version!
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Made with late interaction I'd love to recreate the dataset to see a proper apache 2.0 version!
I'm using https://artificialanalysis.ai/ just because it puts everything in one place! It's not the best resource but these days I'm all about saving time.

@ThomasTheMaker if you make an issue on the repo, I'll look into it!

@ThomasTheMaker it's just the raw attention and transformer architecture in golang designed for serverless so performance will definitely be less than ggml and llama.cpp since it's not accelerated by GPU's but if you're into edge AI CPU only, this is the first, only and best way to compute attention.
Quantization can definitely be supported as it's just a math model!

We built this library at takara.ai to bring attention mechanisms and transformer layers to Go β in a form that's lightweight, clean, and dependency-free.
Weβre proud to say that every part of this project reflects what we set out to do.
- Pure Go β no external dependencies, built entirely on the Go standard library
- Core support for DotProductAttention and MultiHeadAttention
- Full transformer layers with LayerNorm, feed-forward networks, and residual connections
- Designed for edge, embedded, and real-time environments where simplicity and performance matter
Thank you to everyone who has supported this so far β the stars, forks, and feedback mean a lot.

No abstracts, just bullet points.
Start your day here: https://tldr.takara.ai
This is a pretty big update for sure. The models have improved significantly which is great for everyone involved, especially the end user. Those datasets look very promising as well!
Sounds interesting, Iβll check it out!
This is a really interesting post. Iβve been looking at the DeepSeek models for sure. This shows a pretty nice improvement, would love to see some example changes!
Very cool

A little over 2 weeks ago @aldigobbler and I set out to create the largest MultiModal SVG dataset ever created, we succeeded in this and when I was in Munich, Germany I took it one step further and made an entire app with it!
We fine-tuned Mistral Small, made a Next.JS application and blew some minds, taking 3rd place out of over 100 hackers. So cool!
If you want to see the dataset, please see below.
takara-ai/fudeno-instruct-4M

Sir, basically I want to create a generative AI university helpdesk chatbot, and for this, I have created datasets myself and also fine-tuned models, but I am not getting satisfactory results. Sir, if you have time, could you please check my datasets in my profile and help me understand how I can improve my dataset and work on it so that my task gets completed? I would be very grateful to you.
I would enhance your dataset to use multi turn conversations if you can at all for llama2 you could do something like this:
<s>[INST] Is the BS Physics program a part-time or full-time course? [/INST] The BS Physics program is a full-time undergraduate program that requires regular on-campus attendance. </s><s>[INST] How many units per semester? [/INST] A typical semester load consists of 15-18 units. </s>
hope this helps! Again, please reach out to me on discord here: takarajordan_82155
gimme an invite! :D

Small Language Models Enthusiasts and GPU Poor oss enjoyers lets connect.
Just created an organization which main target is to have fun with smaller models tuneable on consumer range GPUs, feel free to join and lets have some fun, much love ;3

Amazing work

C4AI community has built Maya 8B, a new open-source multilingual VLM built on SigLIP and Aya 8B π± works on 8 languages! π£οΈ
The authors extend Llava dataset using Aya's translation capabilities with 558k examples!
ry it here kkr5155/maya_demo
Dataset maya-multimodal/pretrain
Model maya-multimodal/maya π
kudos @nahidalam and team

β¨ the models come in 1.5B https://huggingface.co/Apollo-LMMs/Apollo-1_5B-t32, 3B https://huggingface.co/Apollo-LMMs/Apollo-3B-t32 and 7B https://huggingface.co/Apollo-LMMs/Apollo-7B-t32 with A2.0 license, based on Qwen1.5 & Qwen2
β¨ the authors also release a benchmark dataset https://huggingface.co/spaces/Apollo-LMMs/ApolloBench
The paper has a lot of experiments (they trained 84 models!) about what makes the video LMs work β―οΈ
Try the demo for best setup here https://huggingface.co/spaces/Apollo-LMMs/Apollo-3B
they evaluate sampling strategies, scaling laws for models and datasets, video representation and more!
> The authors find out that whatever design decision was applied to small models also scale properly when the model and dataset are scaled π scaling dataset has diminishing returns for smaller models
> They evaluate frame sampling strategies, and find that FPS sampling is better than uniform sampling, and they find 8-32 tokens per frame optimal
> They also compare image encoders, they try a variation of models from shape optimized SigLIP to DINOv2
they find google/siglip-so400m-patch14-384 to be most powerful π₯
> they also compare freezing different parts of models, training all stages with some frozen parts give the best yield
They eventually release three models, where Apollo-3B outperforms most 7B models and Apollo 7B outperforms 30B models π₯
you guys are amazing!