Top Tweets for #FastInference
Setting up Tor for full anonymity 🕵️
Plus, speeding up my coding workflow with Fast Inference — faster responses switching models on one gateway ⚡
Try it: https://t.co/HzGt4yUZxg
👉 https://t.co/y419Zk96lJ
#ad #Tor #CyberSecurity #KaliLinux #FastInference #infosec
This outputs whole responses in the time it takes other inference providers to output one token!!! #fastinference
17,000 tokens per second!! Read that again!
LLM is hard-wired directly into silicon. no HBM, no liquid cooling, just raw specialized hardware. 10x faster and 20x cheaper than a B200.
the "waiting for the LLM to think" era is dead. Code generates at the speed of human thought.
Transition from brute-force GPU clusters to actual AI appliances.
https://t.co/Bf6DH7Q6Uf
We just released Kimi-Linear-REAP-35B-A3B-Instruct (30% pruned from 48B). Showing REAP’s robustness on Hybrid-attention MoEs, lighter footprint, more context headroom.
🤗 https://t.co/fKUb9wdr96
📄 https://t.co/ou9mi9WRdn
🔗 https://t.co/IkOnWpwyIv
#Cerebras #FastInference #Kimi
Video alert for the #ML2R #YouTube channel! @sbuschjaeger (@sfb876/@TU_Dortmund) introduces the #FastInference tool. Uniting ensemble pruning & leaf refinement for #RandomForests, #MachineLearning models are applied resource-efficiently on small devices.
📽️https://t.co/RZ82wIeXzB
2. We have my #fastinference library:
https://t.co/WnTlqRuoqH_onnx() -> Model is run through ONNX ort and DataLoaders (which are the same blank DataLoaders from 1) get exported.
Still requires that all related functions are in the namespace still 4/
First off I built #fastinference, a @fastdotai extension geared towards making inference with the fastai more approachable, faster at times, and include integrations with libraries such as ONNX. 2/ https://t.co/tHVi21k0zg
This question is geared towards my @fastdotai extension #fastinference. In the pytorch-version only, what would be the *minimal* augmentation you would expect available at runtime after training your model (from a vision perspective)
Minor little update: If you've been using #fastinference and #shap you may have noticed it takes much longer to analyze some rows. Their new release seems to have change some bits so the last version is pinned. (May look into it more once time is available)
#fastinference on pip has now been released to reflect the recent change to fastai, finally got there.
In a situation where you may not want all of #fastinference? Well now you can choose to install specific flavors to suit your need! Don't quite need GPU support? We go that covered! The base library only contains the @fastdotai inference speedup code, and building up from there
Ever wondered just what parameters you used in your @fastdotai pipline? (or what fastai set) Spent the last few days making a nice and easy way to do so! (Coming soon) And yes, I will 100% be taking advantage of this for #fastinference
Join me Sunday at 2pm CST for my walk-through of my @fastdotai extension #fastinference. I'll be talking about how to use it, how it's built, and the decisions I made along the way: https://t.co/Hu0cDOJUIp
Zach has been doing amazing work with #fastinference for @fastdotai, the addition of intrinsic attention on your text predictions is the icing on the cake!
Just released the newest version of #fastinference ! Now included: Text Model support for @fastdotai's AWD-LSTM! Besides some timing increase (predict sped up by a factor of 10X, get_preds by only a little), we also have intrinsic attention on your classification predictions!
Just released the newest version of #fastinference ! Now included: Text Model support for @fastdotai's AWD-LSTM! Besides some timing increase (predict sped up by a factor of 10X, get_preds by only a little), we also have intrinsic attention on your classification predictions!
For those wanting an in-depth guide to the major performance changes, as well as how to use the ONNX support in @fastdotai with #fastinference, here is the first part of my tutorial blogs: https://t.co/DDm9zjQ3nk
Happy to announce #fastinference! The bad news: fastshap and ClassConfusion are gone. The good news: they moved to a new home! In this module we have all of the above plus some speed-up and QOL integrations into @fastdotai's inference methods, see here: 1/
https://t.co/SLgJahtSr5
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