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Nicolay Rusnachenko

nicolay-r
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https://nicolay-r.github.io/

AI & ML interests

Information Retrieval・Medical Multimodal NLP (πŸ–Ό+πŸ“) Research Fellow @BU_Research・software developer http://arekit.io・PhD in NLP

Recent Activity

posted an update about 4 hours ago
πŸ“’ If you're aimed at quick experiment with LLM and known Chain-of-Thought (CoT) / prompt schema with no-string dependencies, then I have something relevant for you to share πŸ’Ž So far I released the updated version πŸ“¦ bulk-chain-0.25.0 πŸ“¦, which is aimed at bringing accessible API for an instant LLM application towards massive data iterators using via predefined prompt schema 🎊 πŸ“¦: https://pypi.org/project/bulk-chain/0.25.0/ 🌟: https://github.com/nicolay-r/bulk-chain πŸ“˜: https://github.com/nicolay-r/bulk-chain/issues/26 The key updates of the most recent release are: βœ… πŸͺΆ No-string (empty dependencies): you can use any framework / API for LLM. βœ… 🐍 Python API support (see first screenshot πŸ“Έ). βœ… πŸ’₯ Native try-catch wrapping to guarantee no-data-lost on using remote providers especially: OpenAI, ReplicateIO, OpenRouter, etc. βœ… πŸ”₯ Batching mode support: you may wrap for handling batches to significantly boost the performance πŸš€ (see screenshot below for bath enabling πŸ“Έ) βœ… πŸ”§ Fixed a lot of minor bugs Quick start on GoogleColab: πŸ“™: https://colab.research.google.com/github/nicolay-r/bulk-chain/blob/master/bulk_chain_tutorial.ipynb πŸ“˜ The wiki of the project is available here: https://github.com/nicolay-r/bulk-chain/wiki/Project-Documentation
reacted to as-cle-bert's post with ❀️ about 5 hours ago
Hi HuggingFacers!🀢🏼 As my last 2024 project, I've dropped a Discord Bot that knows a lot about PokemonsπŸ¦‹ GitHub πŸ‘‰ https://github.com/AstraBert/Pokemon-Bot Demo Space πŸ‘‰ https://huggingface.co/spaces/as-cle-bert/pokemon-bot The bot integrates: - Chat features (Cohere's Command-R) with RAG functionalities (hybrid search and reranking with Qdrant) and chat memory (managed through PostgreSQL) to produce information about Pokemons - Image-based search to identify Pokemons from their images (via Qdrant) - Card package random extraction and description HuggingFaceπŸ€—, as usual, plays the most important role in the application stack, with the following models: - https://huggingface.co/sentence-transformers/LaBSE - https://huggingface.co/prithivida/Splade_PP_en_v1 - https://huggingface.co/facebook/dinov2-large And datasets: - https://huggingface.co/datasets/Karbo31881/Pokemon_images - https://huggingface.co/datasets/wanghaofan/pokemon-wiki-captions - https://huggingface.co/datasets/TheFusion21/PokemonCards Have fun!πŸ•
reacted to MonsterMMORPG's post with πŸ‘ about 5 hours ago
Best open source Image to Video CogVideoX1.5-5B-I2V is pretty decent and optimized for low VRAM machines with high resolution - native resolution is 1360px and up to 10 seconds 161 frames - audios generated with new open source audio model Full YouTube tutorial for CogVideoX1.5-5B-I2V : https://youtu.be/5UCkMzP2VLE 1-Click Windows, RunPod and Massed Compute installers : https://www.patreon.com/posts/112848192 https://www.patreon.com/posts/112848192 - installs into Python 3.11 VENV Official Hugging Face repo of CogVideoX1.5-5B-I2V : https://huggingface.co/THUDM/CogVideoX1.5-5B-I2V Official github repo : https://github.com/THUDM/CogVideo Used prompts to generate videos txt file : https://gist.github.com/FurkanGozukara/471db7b987ab8d9877790358c126ac05 Demo images shared in : https://www.patreon.com/posts/112848192 I used 1360x768px images at 16 FPS and 81 frames = 5 seconds +1 frame coming from initial image Also I have enabled all the optimizations shared on Hugging Face pipe.enable_sequential_cpu_offload() pipe.vae.enable_slicing() pipe.vae.enable_tiling() quantization = int8_weight_only - you need TorchAO and DeepSpeed works great on Windows with Python 3.11 VENV Used audio model : https://github.com/hkchengrex/MMAudio 1-Click Windows, RunPod and Massed Compute Installers for MMAudio : https://www.patreon.com/posts/117990364 https://www.patreon.com/posts/117990364 - Installs into Python 3.10 VENV Used very simple prompts - it fails when there is human in input video so use text to audio in such cases I also tested some VRAM usages for CogVideoX1.5-5B-I2V Resolutions and here their VRAM requirements - may work on lower VRAM GPUs too but slower 512x288 - 41 frames : 7700 MB , 576x320 - 41 frames : 7900 MB 576x320 - 81 frames : 8850 MB , 704x384 - 81 frames : 8950 MB 768x432 - 81 frames : 10600 MB , 896x496 - 81 frames : 12050 MB 896x496 - 81 frames : 12050 MB , 960x528 - 81 frames : 12850 MB
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commented 2 papers 8 months ago
commented 5 papers 9 months ago