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Tom-Neverwinter

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New activity in Apollo-LMMs/README 8 days ago

model pulled

#1 opened 8 days ago by
Tom-Neverwinter
reacted to tomaarsen's post with โค๏ธ๐Ÿš€๐Ÿ”ฅ 2 months ago
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๐Ÿ“ฃ Sentence Transformers v3.2.0 is out, marking the biggest release for inference in 2 years! 2 new backends for embedding models: ONNX (+ optimization & quantization) and OpenVINO, allowing for speedups up to 2x-3x AND Static Embeddings for 500x speedups at 10-20% accuracy cost.

1๏ธโƒฃ ONNX Backend: This backend uses the ONNX Runtime to accelerate model inference on both CPU and GPU, reaching up to 1.4x-3x speedup depending on the precision. We also introduce 2 helper methods for optimizing and quantizing models for (much) faster inference.
2๏ธโƒฃ OpenVINO Backend: This backend uses Intel their OpenVINO instead, outperforming ONNX in some situations on CPU.

Usage is as simple as SentenceTransformer("all-MiniLM-L6-v2", backend="onnx"). Does your model not have an ONNX or OpenVINO file yet? No worries - it'll be autoexported for you. Thank me later ๐Ÿ˜‰

๐Ÿ”’ Another major new feature is Static Embeddings: think word embeddings like GLoVe and word2vec, but modernized. Static Embeddings are bags of token embeddings that are summed together to create text embeddings, allowing for lightning-fast embeddings that don't require any neural networks. They're initialized in one of 2 ways:

1๏ธโƒฃ via Model2Vec, a new technique for distilling any Sentence Transformer models into static embeddings. Either via a pre-distilled model with from_model2vec or with from_distillation where you do the distillation yourself. It'll only take 5 seconds on GPU & 2 minutes on CPU, no dataset needed.
2๏ธโƒฃ Random initialization. This requires finetuning, but finetuning is extremely quick (e.g. I trained with 3 million pairs in 7 minutes). My final model was 6.6% worse than bge-base-en-v1.5, but 500x faster on CPU.

Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.2.0
Documentation on Speeding up Inference: https://sbert.net/docs/sentence_transformer/usage/efficiency.html
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reacted to merve's post with ๐Ÿ”ฅ 3 months ago
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Meta AI vision has been cooking @facebook
They shipped multiple models and demos for their papers at @ECCV ๐Ÿค—

Here's a compilation of my top picks:
- Sapiens is family of foundation models for human-centric depth estimation, segmentation and more, all models have open weights and demos ๐Ÿ‘

All models have their demos and even torchscript checkpoints!
A collection of models and demos: facebook/sapiens-66d22047daa6402d565cb2fc
- VFusion3D is state-of-the-art consistent 3D generation model from images

Model: facebook/vfusion3d
Demo: facebook/VFusion3D

- CoTracker is the state-of-the-art point (pixel) tracking model

Demo: facebook/cotracker
Model: facebook/cotracker
reacted to louisbrulenaudet's post with ๐Ÿ‘ 3 months ago
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The Romulus model series has been released on Hugging Face, continually pre-trained on 34,864,949 tokens of French laws and intended to serve as a foundation for fine-tuning on labeled data ๐Ÿค—

The training code, dataset and model weights are open and available free on HF and the training was based on H100 provided by Microsoft for Startups using Unsloth AI by @danielhanchen and @shimmyshimmer ๐Ÿฆฅ

Link to the base model: louisbrulenaudet/Romulus-cpt-Llama-3.1-8B-v0.1

Link to the instruct model: louisbrulenaudet/Romulus-cpt-Llama-3.1-8B-v0.1-Instruct

Link to the dataset: louisbrulenaudet/Romulus-cpt-fr

Please note that these models have not been aligned for the production of usable texts as they stand, and will certainly need to be refined for the desired tasks in order to produce satisfactory results.
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New activity in multimodalart/flux-lora-the-explorer 4 months ago

how to make a lora

3
#2 opened 4 months ago by
guardiancc
reacted to vikhyatk's post with ๐Ÿ”ฅ 5 months ago
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๐Ÿš€ Exciting news! We've just launched "Thundermoon" - the latest version of Moondream, our open-source vision language model! ๐ŸŒ™

Key improvements in this release:
1. Massive leap in OCR capabilities
2. Enhanced document understanding
3. Significant boosts across key metrics:
* DocVQA: 61.9 (โ†‘103%)
* TextVQA: 60.2 (โ†‘5.2%)
* GQA: 64.9 (โ†‘2.9%)

What does this mean? Moondream can now tackle complex document analysis tasks with unprecedented accuracy for a model of its size. From deciphering handwritten notes to interpreting data tables, the applications are vast.

Check out the image for a glimpse of Moondream in action, effortlessly extracting insights from a 1944 sugar industry document!

Why it matters:
* Democratizing AI: As an open-source project, we're making advanced vision AI accessible to all developers.
* Efficiency: Proving that smaller models can deliver big results.
* Real-world impact: From historical document analysis to modern business intelligence, the potential use cases are exciting.

Curious to try it out? Try out the live demo here! https://moondream.ai/playground
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replied to Xenova's post 5 months ago
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still have yet to get this to work locally even following the instructions stated on reddit.

New activity in Xenova/whisper-speaker-diarization 5 months ago

how do we run this?

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#2 opened 5 months ago by
Tom-Neverwinter
reacted to lamhieu's post with ๐Ÿ˜” 5 months ago
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๐ŸŽ‰ The Ghost 8B Beta model outperforms prominent models such as Llama 3 8B Instruct, GPT 3.5 Turbo in the lc_winrate score. In addition, it also outperforms Claude 3 Opus, Claude 3 Sonnet, GPT-4, and Mistral Large when comparing the winrate score of AlpacaEval 2.0.

Ghost 8B Beta is a large language model developed with goals that include excellent multilingual support, superior knowledge capabilities, and cost-effectiveness. The model comes in two context length versions, 8k and 128k, along with multilingual function tools support by default.
The languages supported are ๐Ÿ‡บ๐Ÿ‡ธ English, ๐Ÿ‡ซ๐Ÿ‡ท French, ๐Ÿ‡ฎ๐Ÿ‡น Italian, ๐Ÿ‡ช๐Ÿ‡ธ Spanish, ๐Ÿ‡ต๐Ÿ‡น Portuguese, ๐Ÿ‡ฉ๐Ÿ‡ช German, ๐Ÿ‡ป๐Ÿ‡ณ Vietnamese, ๐Ÿ‡ฐ๐Ÿ‡ท Korean and ๐Ÿ‡จ๐Ÿ‡ณ Chinese.

Explore the Potential:
To learn more about this groundbreaking language model, visit the official website or explore the online demo platforms:
- Ghost 8B Beta (ฮฒ, 8k) on Spaces: lamhieu/ghost-8b-beta-8k.
- Ghost 8B Beta (ฮฒ, 128k) on Spaces: lamhieu/ghost-8b-beta-128k
- Official website: https://ghost-x.org/docs/models/ghost-8b-beta
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replied to Tar9897's post 6 months ago
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why dont we take the top scoring papers in schools and pass them into a llm? starting wtih kindgergarden and then all the way up in succession? in one year you would have a variety of masters level knowledge and all the foundational material