Just recently, the guys from Shanghai University rolled out MG-LLaVA - MLLM, which expands the capabilities of processing visual information through the use of additional components: special components that are responsible for working with low and high resolution.
MG-LLaVA integrates an additional high-resolution visual encoder to capture fine details, which are then combined with underlying visual features using the Conv-Gate network.
Trained exclusively on publicly available multimodal data, MG-LLaVA achieves excellent results.
https://t.me/DataScienceT
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pip install "unstructured[all-docs]"Unstructured provides components for preprocessing images and text documents; supports many formats: PDF, HTML, Word docs, etc.
Run the library in a container:
docker run -dt --name unstructured downloads.unstructured.io/unstructured-io/unstructured:latest
docker exec -it unstructured bash
https://t.me/DataScienceT
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Forwarded from Data Science Premium (Books & Courses)
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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://t.me/DataScienceT
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ุชูุจูู ุงููู
ูุฐุฌ ุงูู
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ู ู
ุฎุชุจุฑ ุดูุบูุงู ููุฐูุงุก ุงูุงุตุทูุงุนู๐จ
ู ุฑุญุจูุง ุจู ุฏุงุฎูููุง 2.5
โค๏ธโ๐ฅ ู ูุฏูู 7B ุนุงูู ุงูุฌูุฏุฉ
๐ ู ุง ูุตู ุฅูู ู ูููู ูุงูุฐุฉ ุณูุงููุฉ
โ๏ธ ุฅู ูุงููุงุช ุงุณุชุฎุฏุงู ุงูุฃุฏุงุฉ
ูุชุทูุน ููุนุจ ู ุนูุง
https://huggingface.co/collections/internlm/internlm25-66853f32717072d17581bc13
https://t.me/DataScienceTโ
ู ุฑุญุจูุง ุจู ุฏุงุฎูููุง 2.5
โค๏ธโ๐ฅ ู ูุฏูู 7B ุนุงูู ุงูุฌูุฏุฉ
๐ ู ุง ูุตู ุฅูู ู ูููู ูุงูุฐุฉ ุณูุงููุฉ
โ๏ธ ุฅู ูุงููุงุช ุงุณุชุฎุฏุงู ุงูุฃุฏุงุฉ
ูุชุทูุน ููุนุจ ู ุนูุง
https://huggingface.co/collections/internlm/internlm25-66853f32717072d17581bc13
https://t.me/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
โช๏ธ HF: https://huggingface.co/espnet/xeus
โช๏ธ Dataset: https://huggingface.co/datasets/espnet/mms_ulab_v2
https://t.me/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
๐ค Hugging Face
https://t.me/DataScienceT
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InternLM-XComposer-2.5 copes with the tasks of text description 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://t.me/DataScienceT
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