🌟 Vico is an implementation of a technique that allows you to achieve greater accuracy in generating composite videos.

Vico is a no-training framework that analyzes how individual tokens from prompt input tokens affect the generated video, and adjusts the model to prevent dominance by considering all prompt words equally.

To do this, Vico builds a spatio-temporal attention graph, with which it evaluates and adjusts the representation of all input concepts in the video.

🖥 Local launch of inference without UI (with Videocrafterv2)

git clone https://github.com/Adamdad/vico.git
pip install diffusers==0.26.3
git lfs install
git clone https://huggingface.co/adamdad/videocrafterv2_diffusers
export PYTHONPATH="$PWD"
python videocrafterv2_vico.py \
--prompts XXX \
--unet_path $PATH_TO_VIDEOCRAFTERV2 \
--attribution_mode "latent_attention_flow_st_soft"

🖥 GitHub [Stars: 19 | Issues: 0 | Forks: 0 ]
🟡 Project page
🟡 Arxiv

#T2V #Framework #ML

https://t.iss.one/DataScienceT ✅

Data Science Cheat Sheets
Quick help to make a data scientist's life easier

About Dataset
A collection of cheat sheets for various data-science related languages and topics


https://t.iss.one/codeprogrammer 🔒

💡 #deeplearning #AI #ML #python

Data Science Cheat Sheets
Quick help to make a data scientist's life easier ✅

https://t.iss.one/codeprogrammer 🔒

💡 #deeplearning #AI #ML #python

Recurrent Neural Network (RNN) by hand ✍️ Excel

📂 Tags: #python #ML #RNN

https://t.iss.one/codeprogrammer ⭐️

Download Excel file 👇
https://t.iss.one/+Tdshx2j5cZ00N2Ji

Transformer by Hand ✍️ in 5 Minutes with Anna Rahn

📂 Tags: #python #ML #Transformer

https://t.iss.one/codeprogrammer ⭐️

DeepSeek-V3 Technical Report

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in #DeepSeek V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks. The model checkpoints are available at https://github.com/deepseek-ai/DeepSeek-V3.

Paper: https://arxiv.org/pdf/2412.19437v1.pdf

Code: https://github.com/deepseek-ai/deepseek-v3

#aiagents #ai #llm #ml #machinelearning #python

https://t.iss.one/DataScienceT 💚

MiniCPM-V: A GPT-4V Level MLLM on Your Phone

The recent surge of Multimodal Large Language Models (MLLMs) has fundamentally reshaped the landscape of #AI research and industry, shedding light on a promising path toward the next AI milestone. However, significant challenges remain preventing MLLMs from being practical in real-world applications. The most notable challenge comes from the huge cost of running an MLLM with a massive number of parameters and extensive computation. As a result, most MLLMs need to be deployed on high-performing cloud servers, which greatly limits their application scopes such as mobile, offline, energy-sensitive, and privacy-protective scenarios. In this work, we present MiniCPM-V, a series of efficient #MLLMs deployable on end-side devices. By integrating the latest MLLM techniques in architecture, pretraining and alignment, the latest MiniCPM-Llama3-V 2.5 has several notable features: (1) Strong performance, outperforming GPT-4V-1106, Gemini Pro and Claude 3 on OpenCompass, a comprehensive evaluation over 11 popular benchmarks, (2) strong #OCR capability and 1.8M pixel high-resolution #image perception at any aspect ratio, (3) trustworthy behavior with low hallucination rates, (4) multilingual support for 30+ languages, and (5) efficient deployment on mobile phones. More importantly, MiniCPM-V can be viewed as a representative example of a promising trend: The model sizes for achieving usable (e.g., GPT-4V) level performance are rapidly decreasing, along with the fast growth of end-side computation capacity. This jointly shows that GPT-4V level MLLMs deployed on end devices are becoming increasingly possible, unlocking a wider spectrum of real-world AI applications in the near future.

Paper: https://arxiv.org/pdf/2408.01800v1.pdf

Codes:
https://github.com/OpenBMB/MiniCPM-o
https://github.com/openbmb/minicpm-v

Datasets: Video-MME

#MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics

https://t.iss.one/DataScienceT ❤️

🐫Tülu 3 (what a name) 405B - ​​another release!

An open source model (and no, it's not a Chinese model) that outperforms the DeepSeek-V3! on multiple benchmarks

Scalable to 405B - ​​with performance on par with GPT-4o and outperforming previous models in the same class.

▪ Blog: https://allenai.org/blog/tulu-3-405B
▪You can test it here: https://playground.allenai.org/?model=tulu3-405b
▪ Technical report: https://allenai.org/blog/tulu-3-technical
▪ Hugging Face : https://huggingface.co/collections/allenai/tulu-3-models-673b8e0dc3512e30e7dc54f5

#llm #ml #ai #opensource

https://t.iss.one/DataScienceT ❤️