Ms - SmolLM2 1.7B - beats Qwen 2.5 1.5B & Llama 3.21B, Apache 2.0 licensed, trained on 11 Trillion tokens 🔥
> 135M, 360M, 1.7B parameter model
> Trained on FineWeb-Edu, DCLM, The Stack, along w/ new mathematics and coding datasets
> Specialises in Text rewriting, Summarization & Function Calling
> Integrated with transformers & model on the hub!
You can run the 1.7B in less than 2GB VRAM on a Q4 👑
Fine-tune, run inference, test, train, repeat - intelligence is just 5 lines of code away!
https://huggingface.co/collections/HuggingFaceTB/smollm2-6723884218bcda64b34d7db9
@Machine_learn
> 135M, 360M, 1.7B parameter model
> Trained on FineWeb-Edu, DCLM, The Stack, along w/ new mathematics and coding datasets
> Specialises in Text rewriting, Summarization & Function Calling
> Integrated with transformers & model on the hub!
You can run the 1.7B in less than 2GB VRAM on a Q4 👑
Fine-tune, run inference, test, train, repeat - intelligence is just 5 lines of code away!
https://huggingface.co/collections/HuggingFaceTB/smollm2-6723884218bcda64b34d7db9
@Machine_learn
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📑A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models
📎 Study the paper
@Machine_learn
📎 Study the paper
@Machine_learn
❤1
Forwarded from Github LLMs
https://t.iss.one/deep_learning_proj
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Forwarded from Papers
الحمدالله تو اين بازه ٣ ماه تونستيم مقالات مشاركتي رو تحت وظايف زير انجام بديم:
🔹 ثبت ٤ مقاله در حوزه Multi-modal wond classification
🔹 ارائه ی دو مقاله در حوزه ی breast cancer segmentation
🔹 ارائه ی سه مقاله در حوزه ی cancer detection
که ۸۰٪ مراحل این مقالات هم تموم شده.
به زودی پس از اتمام این مقالات لیستی از مقالات مشارکتی رو خواهیم داشت .
https://t.iss.one/+SP9l58Ta_zZmYmY0
که ۸۰٪ مراحل این مقالات هم تموم شده.
به زودی پس از اتمام این مقالات لیستی از مقالات مشارکتی رو خواهیم داشت .
https://t.iss.one/+SP9l58Ta_zZmYmY0
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Papers
در اين كانال قرار مقالاتي كه كار ميكنيم رو به اشتراك بزاريم.
@Raminmousa
@Raminmousa
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Conformal prediction under ambiguous ground truth
Paper: https://arxiv.org/pdf/2307.09302v2.pdf
Codes:
https://github.com/google-deepmind/uncertain_ground_truth
https://github.com/alaalab/webcp
Dataset: Dermatology ddx dataset
@Machine_learn
Paper: https://arxiv.org/pdf/2307.09302v2.pdf
Codes:
https://github.com/google-deepmind/uncertain_ground_truth
https://github.com/alaalab/webcp
Dataset: Dermatology ddx dataset
@Machine_learn
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❤️ اکستنشن ChatGPT Search برای مرورگرهای کرومیوم منتشر شد
از طریق این لینک میتونید این افزونه رو دانلود کنید
@Machine_learn
از طریق این لینک میتونید این افزونه رو دانلود کنید
@Machine_learn
AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
🖥 Github: https://github.com/thudm/autowebglm
📕 Paper: https://arxiv.org/abs/2404.03648v1
🔥Dataset: https://paperswithcode.com/dataset/mind2web
@Machine_learn
🖥 Github: https://github.com/thudm/autowebglm
📕 Paper: https://arxiv.org/abs/2404.03648v1
🔥Dataset: https://paperswithcode.com/dataset/mind2web
@Machine_learn
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Smol TTS models are here! OuteTTS-0.1-350M - Zero shot voice cloning, built on LLaMa architecture, CC-BY license! 🔥
> Pure language modeling approach to TTS
> Zero-shot voice cloning
> LLaMa architecture w/ Audio tokens (WavTokenizer)
> BONUS: Works on-device w/ llama.cpp ⚡
Three-step approach to TTS:
> Audio tokenization using WavTokenizer (75 tok per second).
> CTC forced alignment for word-to-audio token mapping.
> Structured prompt creation w/ transcription, duration, audio tokens.
https://huggingface.co/OuteAI/OuteTTS-0.1-350M
@Machine_learn
> Pure language modeling approach to TTS
> Zero-shot voice cloning
> LLaMa architecture w/ Audio tokens (WavTokenizer)
> BONUS: Works on-device w/ llama.cpp ⚡
Three-step approach to TTS:
> Audio tokenization using WavTokenizer (75 tok per second).
> CTC forced alignment for word-to-audio token mapping.
> Structured prompt creation w/ transcription, duration, audio tokens.
https://huggingface.co/OuteAI/OuteTTS-0.1-350M
@Machine_learn
Constrained Diffusion Implicit Models!
We use diffusion models to solve noisy inverse problems like inpainting, sparse-recovery, and colorization. 10-50x faster than previous methods!
Paper: arxiv.org/pdf/2411.00359
Demo: https://t.co/m6o9GLnnZF
@Machine_learn
We use diffusion models to solve noisy inverse problems like inpainting, sparse-recovery, and colorization. 10-50x faster than previous methods!
Paper: arxiv.org/pdf/2411.00359
Demo: https://t.co/m6o9GLnnZF
@Machine_learn
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📃 Plant-based anti-cancer drug discovery using computational approaches
📎 Study the paper
@Machine_learn
📎 Study the paper
@Machine_learn
❤1
This repository contains a collection of resources in the form of eBooks related to Data Science, Machine Learning, and similar topics.
📖 book
💠 @Machine_learn
📖 book
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