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Eng. Hussein Sheikho
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Is it possible to generate LLM response on two RTX 3060s faster than on A100 (which is 16+ times more expensive)?
Yes, it is possible with EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency) and the accuracy of the responses is preserved.
EAGLE allows you to extrapolate the context feature vectors of the second top layer of the LLM, which greatly improves the generation efficiency.
EAGLE is 2x faster than Lookahead (13B), and 1.6x faster than Medusa (13B).
And yes, EAGLE can be combined with other acceleration techniques like vLLM, DeepSpeed, Mamba, FlashAttention, quantization and hardware optimization.
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Forwarded from Data Science Premium (Books & Courses)
Building Transformer Models with Attention (2023)
The number one book for learning transformers from beginners to professionalism. The book provides an interesting explanation of transformers. The issue is available to the first twenty people only.
Price: 4$
PayPal / Credit Card: https://www.patreon.com/DataScienceBooks/shop/building-transformer-models-with-2023-253971
Crypto Payment: http://t.me/send?start=IVq3O4aPlSWF
The number one book for learning transformers from beginners to professionalism. The book provides an interesting explanation of transformers. The issue is available to the first twenty people only.
Price: 4$
PayPal / Credit Card: https://www.patreon.com/DataScienceBooks/shop/building-transformer-models-with-2023-253971
Crypto Payment: http://t.me/send?start=IVq3O4aPlSWF
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pip install medmnistchttps://news.1rj.ru/str/DataScienceT
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تنبيه النموذج المفتوح الجديد من مختبر شنغهاي للذكاء الاصطناعي🚨
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https://huggingface.co/collections/internlm/internlm25-66853f32717072d17581bc13
https://news.1rj.ru/str/DataScienceT✅
مرحبًا بك داخليًا 2.5
❤️🔥 موديل 7B عالي الجودة
🚀 ما يصل إلى مليون نافذة سياقية
⚙️ إمكانيات استخدام الأداة
نتطلع للعب معها
https://huggingface.co/collections/internlm/internlm25-66853f32717072d17581bc13
https://news.1rj.ru/str/DataScienceT
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A multi-lingual speech recognition and translation model from Carnegie Mellon University that is trained in over 4000 languages!
> MIT license
> 577 million parameters.
> Superior to MMS 1B and w2v-BERT v2 2.0
> E-Branchformer architecture
> Dataset 8900 hours of audio recordings in over 4023 languages
git lfs install
git clone https://huggingface.co/espnet/XEUS
▪️ HF: https://huggingface.co/espnet/xeus
▪️ Dataset: https://huggingface.co/datasets/espnet/mms_ulab_v2
https://news.1rj.ru/str/DataScienceT
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MoMA requires no training and can quickly generate image images with high detail accuracy and identity preservation.
The speed of MoMA is achieved by optimizing the attention mechanism, which transfers features from the original image to the diffusion model.
The model is a universal adapter and can be applied to various models without modification.
Today, MoMA outperforms similar existing methods in synthetic tests and allows you to create images with a high level of compliance with the prompt while preserving the style of the reference image as much as possible.
22 GB or more GPU memory:
args.load_8bit, args.load_4bit = False, False
18 GB or more GPU memory:
args.load_8bit, args.load_4bit = True, False
14 GB or more GPU memory:
args.load_8bit, args.load_4bit = False, True
🤗 Hugging Face
https://news.1rj.ru/str/DataScienceT
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InternLM-XComposer-2.5 copes with the tasks of text denoscription of images with complex composition, achieving the capabilities of GPT-4V. Trained with alternating 24 KB image-text contexts, it can easily expand to 96 KB contexts via RoPE extrapolation.
Compared to the previous version 2.0, InternLM-XComposer-2.5 has three major improvements:
- understanding of ultra-high resolution;
- detailed understanding of the video;
- process several images in the context of 1 dialogue.
Using extra Lora, XComposer-2.5 is capable of performing complex tasks:
- creation of web pages;
- creation of high-quality text articles with images.
XComposer-2.5 was evaluated on 28 benchmarks, outperforming existing state-of-the-art open source models in 16 benchmarks . It also closely competes with GPT-4V and Gemini Pro on 16 key tasks.
https://news.1rj.ru/str/DataScienceT
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Forwarded from Machine Learning
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Updated : Version 2024
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In anticipation of the upcoming ICML 2024 (Vienna, July 21-27, 2024), Microsoft has published the results of a study from the MInference project. This method allows you to speed up the processing of long sequences due to sparse calculations and the use of unique templates in matrices.
The MInference technique does not require changes in pre-training settings.
Microsoft researchers' synthetic tests of the method on the LLaMA-3-1M, GLM4-1M, Yi-200K, Phi-3-128K, and Qwen2-128K models show up to a 10x reduction in latency and prefill errors on the A100 while maintaining accuracy.
https://news.1rj.ru/str/DataScienceT
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Arcee Agent 7B is superior to GPT-3.5-Turbo, and many other models in writing and interpreting code.
Arcee Agent 7B is especially suitable for those wishing to implement complex AI solutions without the computational expense of large language models.
And yes, there are also quantized GGUF versions of Arcee Agent 7B.
https://news.1rj.ru/str/DataScienceT
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Kolors is a large diffusion model published recently by the Kuaishou Kolors team.
Kolors has been trained on billions of text-to-image pairs and shows excellent results in generating complex photorealistic images.
As evaluated by 50 independent experts, the Kolors model generates more realistic and beautiful images than Midjourney-v6, Stable Diffusion 3, DALL-E 3 and other models
🟡 Kolors page
🟡 Try
🖥 GitHub
https://news.1rj.ru/str/DataScienceT
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