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XenovaΒ 
posted an update 7 days ago
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1908
Introducing Moonshine Web: real-time speech recognition running 100% locally in your browser!
πŸš€ Faster and more accurate than Whisper
πŸ”’ Privacy-focused (no data leaves your device)
⚑️ WebGPU accelerated (w/ WASM fallback)
πŸ”₯ Powered by ONNX Runtime Web and Transformers.js

Demo: webml-community/moonshine-web
Source code: https://github.com/huggingface/transformers.js-examples/tree/main/moonshine-web
XenovaΒ 
posted an update 17 days ago
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2525
Introducing TTS WebGPU: The first ever text-to-speech web app built with WebGPU acceleration! πŸ”₯ High-quality and natural speech generation that runs 100% locally in your browser, powered by OuteTTS and Transformers.js. πŸ€— Try it out yourself!

Demo: webml-community/text-to-speech-webgpu
Source code: https://github.com/huggingface/transformers.js-examples/tree/main/text-to-speech-webgpu
Model: onnx-community/OuteTTS-0.2-500M (ONNX), OuteAI/OuteTTS-0.2-500M (PyTorch)
reach-vbΒ 
posted an update 18 days ago
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3204
VLMs are going through quite an open revolution AND on-device friendly sizes:

1. Google DeepMind w/ PaliGemma2 - 3B, 10B & 28B: google/paligemma-2-release-67500e1e1dbfdd4dee27ba48

2. OpenGVLabs w/ InternVL 2.5 - 1B, 2B, 4B, 8B, 26B, 38B & 78B: https://huggingface.co/collections/OpenGVLab/internvl-25-673e1019b66e2218f68d7c1c

3. Qwen w/ Qwen 2 VL - 2B, 7B & 72B: Qwen/qwen2-vl-66cee7455501d7126940800d

4. Microsoft w/ FlorenceVL - 3B & 8B: https://huggingface.co/jiuhai

5. Moondream2 w/ 0.5B: https://huggingface.co/vikhyatk/

What a time to be alive! πŸ”₯
ariG23498Β 
posted an update 20 days ago
XenovaΒ 
posted an update 27 days ago
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3918
We just released Transformers.js v3.1 and you're not going to believe what's now possible in the browser w/ WebGPU! 🀯 Let's take a look:
πŸ”€ Janus from Deepseek for unified multimodal understanding and generation (Text-to-Image and Image-Text-to-Text)
πŸ‘οΈ Qwen2-VL from Qwen for dynamic-resolution image understanding
πŸ”’ JinaCLIP from Jina AI for general-purpose multilingual multimodal embeddings
πŸŒ‹ LLaVA-OneVision from ByteDance for Image-Text-to-Text generation
πŸ€Έβ€β™€οΈ ViTPose for pose estimation
πŸ“„ MGP-STR for optical character recognition (OCR)
πŸ“ˆ PatchTST & PatchTSMixer for time series forecasting

That's right, everything running 100% locally in your browser (no data sent to a server)! πŸ”₯ Huge for privacy!

Check out the release notes for more information. πŸ‘‡
https://github.com/huggingface/transformers.js/releases/tag/3.1.0

Demo link (+ source code): webml-community/Janus-1.3B-WebGPU
reach-vbΒ 
posted an update about 1 month ago
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3162
Massive week for Open AI/ ML:

Mistral Pixtral & Instruct Large - ~123B, 128K context, multilingual, json + function calling & open weights
mistralai/Pixtral-Large-Instruct-2411
mistralai/Mistral-Large-Instruct-2411

Allen AI TΓΌlu 70B & 8B - competive with claude 3.5 haiku, beats all major open models like llama 3.1 70B, qwen 2.5 and nemotron
allenai/tulu-3-models-673b8e0dc3512e30e7dc54f5
allenai/tulu-3-datasets-673b8df14442393f7213f372

Llava o1 - vlm capable of spontaneous, systematic reasoning, similar to GPT-o1, 11B model outperforms gemini-1.5-pro, gpt-4o-mini, and llama-3.2-90B-vision
Xkev/Llama-3.2V-11B-cot

Black Forest Labs Flux.1 tools - four new state of the art model checkpoints & 2 adapters for fill, depth, canny & redux, open weights
reach-vb/black-forest-labs-flux1-6743847bde9997dd26609817

Jina AI Jina CLIP v2 - general purpose multilingual and multimodal (text & image) embedding model, 900M params, 512 x 512 resolution, matroyoshka representations (1024 to 64)
jinaai/jina-clip-v2

Apple AIM v2 & CoreML MobileCLIP - large scale vision encoders outperform CLIP and SigLIP. CoreML optimised MobileCLIP models
apple/aimv2-6720fe1558d94c7805f7688c
apple/coreml-mobileclip

A lot more got released like, OpenScholar ( OpenScholar/openscholar-v1-67376a89f6a80f448da411a6), smoltalk ( HuggingFaceTB/smoltalk), Hymba ( nvidia/hymba-673c35516c12c4b98b5e845f), Open ASR Leaderboard ( hf-audio/open_asr_leaderboard) and much more..

Can't wait for the next week! πŸ€—
loubnabnlΒ 
posted an update about 1 month ago
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1637
Making SmolLM2 reproducible: open-sourcing our training & evaluation toolkit πŸ› οΈ https://github.com/huggingface/smollm/

- Pre-training code with nanotron
- Evaluation suite with lighteval
- Synthetic data generation using distilabel (powers our new SFT dataset HuggingFaceTB/smoltalk)
- Post-training scripts with TRL & the alignment handbook
- On-device tools with llama.cpp for summarization, rewriting & agents

Apache 2.0 licensed. V2 pre-training data mix coming soon!

Which other tools should we add next?
SaylorTwiftΒ 
posted an update about 1 month ago
XenovaΒ 
posted an update about 1 month ago
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5517
Have you tried out πŸ€— Transformers.js v3? Here are the new features:
⚑ WebGPU support (up to 100x faster than WASM)
πŸ”’ New quantization formats (dtypes)
πŸ› 120 supported architectures in total
πŸ“‚ 25 new example projects and templates
πŸ€– Over 1200 pre-converted models
🌐 Node.js (ESM + CJS), Deno, and Bun compatibility
🏑 A new home on GitHub and NPM

Get started with npm i @huggingface/transformers.

Learn more in our blog post: https://huggingface.co/blog/transformersjs-v3
  • 3 replies
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ArthurZΒ 
posted an update about 1 month ago
reach-vbΒ 
posted an update about 1 month ago
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4329
What a brilliant week for Open Source AI!

Qwen 2.5 Coder by Alibaba - 0.5B / 1.5B / 3B / 7B / 14B/ 32B (Base + Instruct) Code generation LLMs, with 32B tackling giants like Gemnini 1.5 Pro, Claude Sonnet
Qwen/qwen25-coder-66eaa22e6f99801bf65b0c2f

LLM2CLIP from Microsoft - Leverage LLMs to train ultra-powerful CLIP models! Boosts performance over the previous SOTA by ~17%
microsoft/llm2clip-672323a266173cfa40b32d4c

Athene v2 Chat & Agent by NexusFlow - SoTA general LLM fine-tuned from Qwen 2.5 72B excels at Chat + Function Calling/ JSON/ Agents
Nexusflow/athene-v2-6735b85e505981a794fb02cc

Orca Agent Instruct by Microsoft - 1 million instruct pairs covering text editing, creative writing, coding, reading comprehension, etc - permissively licensed
microsoft/orca-agentinstruct-1M-v1

Ultravox by FixieAI - 70B/ 8B model approaching GPT4o level, pick any LLM, train an adapter with Whisper as Audio Encoder
reach-vb/ultravox-audio-language-model-release-67373b602af0a52b2a88ae71

JanusFlow 1.3 by DeepSeek - Next iteration of their Unified MultiModal LLM Janus with RectifiedFlow
deepseek-ai/JanusFlow-1.3B

Common Corpus by Pleais - 2,003,039,184,047 multilingual, commercially permissive and high quality tokens!
PleIAs/common_corpus

I'm sure I missed a lot, can't wait for the next week!

Put down in comments what I missed! πŸ€—
ariG23498Β 
posted an update about 1 month ago
reach-vbΒ 
posted an update about 2 months ago
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1589
Smol TTS models are here! OuteTTS-0.1-350M - Zero shot voice cloning, built on LLaMa architecture, CC-BY license! πŸ”₯

> 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

The model is extremely impressive for 350M parameters! Kudos to the
OuteAI team on such a brilliant feat - I'd love to see this be applied on larger data and smarter backbones like SmolLM πŸ€—

Check out the models here: OuteAI/outetts-6728aa71a53a076e4ba4817c
reach-vbΒ 
posted an update about 2 months ago
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2973
Smol models ftw! AMD released AMD OLMo 1B - beats OpenELM, tiny llama on MT Bench, Alpaca Eval - Apache 2.0 licensed πŸ”₯

> Trained with 1.3 trillion (dolma 1.7) tokens on 16 nodes, each with 4 MI250 GPUs

> Three checkpoints:

- AMD OLMo 1B: Pre-trained model
- AMD OLMo 1B SFT: Supervised fine-tuned on Tulu V2, OpenHermes-2.5, WebInstructSub, and Code-Feedback datasets
- AMD OLMo 1B SFT DPO: Aligned with human preferences using Direct Preference Optimization (DPO) on UltraFeedback dataset

Key Insights:
> Pre-trained with less than half the tokens of OLMo-1B
> Post-training steps include two-phase SFT and DPO alignment
> Data for SFT:
- Phase 1: Tulu V2
- Phase 2: OpenHermes-2.5, WebInstructSub, and Code-Feedback

> Model checkpoints on the Hub & Integrated with Transformers ⚑️

Congratulations & kudos to AMD on a brilliant smol model release! πŸ€—

amd/amd-olmo-6723e7d04a49116d8ec95070
ariG23498Β 
posted an update 2 months ago