ai4b-hf (ai4b-hf)

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 Tülu 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! 🤗

reach-vb

posted an update about 1 month ago

Post

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! 🤗

reach-vb

posted an update about 2 months ago

Post

1589

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

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 🤗

Check out the models here: OuteAI/outetts-6728aa71a53a076e4ba4817c

reach-vb

authored a paper about 2 months ago

Lina-Speech: Gated Linear Attention is a Fast and Parameter-Efficient Learner for text-to-speech synthesis

Paper • 2410.23320 • Published Oct 30 • 7

reach-vb

posted an update about 2 months ago

Post

2973

Smol models ftw! AMD released AMD OLMo 1B - beats OpenELM, tiny llama on MT Bench, Alpaca Eval - Apache 2.0 licensed 🔥

> 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! 🤗

amd/amd-olmo-6723e7d04a49116d8ec95070

SherryT997

updated a dataset about 2 months ago

ai4b-hf/GLOBE-annotated

Viewer • Updated Oct 31 • 582k • 290 • 3

miteshk

authored a paper 2 months ago

Cross-Lingual Auto Evaluation for Assessing Multilingual LLMs

Paper • 2410.13394 • Published Oct 17 • 1

SherryT997

updated a dataset 2 months ago

ai4b-hf/GLOBE-for-parler

Viewer • Updated Oct 20 • 582k • 173

reach-vb

posted an update 2 months ago

Post

2449

What a great day for Open Science! @AIatMeta released models, datasets, and code for many of its research artefacts! 🔥

1. Meta Segment Anything Model 2.1: An updated checkpoint with improved results on visually similar objects, small objects and occlusion handling. A new developer suite will be added to make it easier for developers to build with SAM 2.

Model checkpoints: reach-vb/sam-21-6702d40defe7611a8bafa881

2. Layer Skip: Inference code and fine-tuned checkpoints demonstrating a new method for enhancing LLM performance.

Model checkpoints: facebook/layerskip-666b25c50c8ae90e1965727a

3. SALSA: New code enables researchers to benchmark AI-based attacks to validate security for post-quantum cryptography.

Repo: https://github.com/facebookresearch/LWE-benchmarking

4. Meta Lingua: A lightweight and self-contained codebase designed to train language models at scale.

Repo: https://github.com/facebookresearch/lingua

5. Meta Open Materials: New open source models and the largest dataset to accelerate AI-driven discovery of new inorganic materials.

Model checkpoints: fairchem/OMAT24

6. MEXMA: A new research paper and code for our novel pre-trained cross-lingual sentence encoder covering 80 languages.

Model checkpoint: facebook/MEXMA

7. Self-Taught Evaluator: a new method for generating synthetic preference data to train reward models without relying on human annotations.

Model checkpoint: facebook/Self-taught-evaluator-llama3.1-70B

8. Meta Spirit LM: An open-source language model for seamless speech and text integration.

Repo: https://github.com/facebookresearch/spiritlm

3 replies

·

reach-vb

posted an update 2 months ago

Post

5445

Multimodal Ichigo Llama 3.1 - Real Time Voice AI 🔥

> WhisperSpeech X Llama 3.1 8B
> Trained on 50K hours of speech (7 languages)
> Continually trained on 45hrs 10x A1000s
> MLS -> WhisperVQ tokens -> Llama 3.1
> Instruction tuned on 1.89M samples
> 70% speech, 20% transcription, 10% text
> Apache 2.0 licensed ⚡

Architecture:
> WhisperSpeech/ VQ for Semantic Tokens
> Llama 3.1 8B Instruct for Text backbone
> Early fusion (Chameleon)

I'm super bullish on HomeBrew/ Jan and early fusion, audio and text, multimodal models!

(P.S. Play with the demo on Hugging Face: jan-hq/Ichigo-llama3.1-s-instruct)

reach-vb

posted an update 3 months ago

Post

3089

NEW: Open Source Text/ Image to video model is out - MIT licensed - Rivals Gen-3, Pika & Kling 🔥

> Pyramid Flow: Training-efficient Autoregressive Video Generation method
> Utilizes Flow Matching
> Trains on open-source datasets
> Generates high-quality 10-second videos
> Video resolution: 768p
> Frame rate: 24 FPS
> Supports image-to-video generation

> Model checkpoints available on the hub 🤗: rain1011/pyramid-flow-sd3

reach-vb

posted an update 3 months ago

Post

2093

On-device AI framework ecosystem is blooming these days:

1. llama.cpp - All things Whisper, LLMs & VLMs - run across Metal, CUDA and other backends (AMD/ NPU etc)
https://github.com/ggerganov/llama.cpp

2. MLC - Deploy LLMs across platforms especially WebGPU (fastest WebGPU LLM implementation out there)
https://github.com/mlc-ai/web-llm

3. MLX - Arguably the fastest general purpose framework (Mac only) - Supports all major Image Generation (Flux, SDXL, etc), Transcription (Whisper), LLMs
https://github.com/ml-explore/mlx-examples

4. Candle - Cross-platform general purpose framework written in Rust - wide coverage across model categories
https://github.com/huggingface/candle

Honorable mentions:

1. Transformers.js - Javascript (WebGPU) implementation built on top of ONNXruntimeweb
https://github.com/xenova/transformers.js

2. Mistral rs - Rust implementation for LLMs & VLMs, built on top of Candle
https://github.com/EricLBuehler/mistral.rs

3. Ratchet - Cross platform, rust based WebGPU framework built for battle-tested deployments
https://github.com/huggingface/ratchet

4. Zml - Cross platform, Zig based ML framework
https://github.com/zml/zml

Looking forward to how the ecosystem would look 1 year from now - Quite bullish on the top 4 atm - but open source ecosystem changes quite a bit! 🤗

Also, which frameworks did I miss?

1 reply

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AshwinSankar

authored 3 papers 3 months ago

Enhancing Out-of-Vocabulary Performance of Indian TTS Systems for Practical Applications through Low-Effort Data Strategies

Paper • 2407.13435 • Published Jul 18

IndicVoices-R: Unlocking a Massive Multilingual Multi-speaker Speech Corpus for Scaling Indian TTS

Paper • 2409.05356 • Published Sep 9

Rasa: Building Expressive Speech Synthesis Systems for Indian Languages in Low-resource Settings

Paper • 2407.14056 • Published Jul 19

srija616

authored a paper 3 months ago

Enhancing Out-of-Vocabulary Performance of Indian TTS Systems for Practical Applications through Low-Effort Data Strategies

Paper • 2407.13435 • Published Jul 18

ai4b-hf

AI & ML interests

Recent Activity

ai4b-hf's activity

BhasaAnuvaad: A Speech Translation Dataset for 14 Indian Languages

ai4bharat/indic-parler-tts

ai4bharat/indic-parler-tts-pretrained

Lina-Speech: Gated Linear Attention is a Fast and Parameter-Efficient Learner for text-to-speech synthesis

ai4b-hf/GLOBE-annotated

Cross-Lingual Auto Evaluation for Assessing Multilingual LLMs

ai4b-hf/GLOBE-for-parler

Enhancing Out-of-Vocabulary Performance of Indian TTS Systems for Practical Applications through Low-Effort Data Strategies

IndicVoices-R: Unlocking a Massive Multilingual Multi-speaker Speech Corpus for Scaling Indian TTS

Rasa: Building Expressive Speech Synthesis Systems for Indian Languages in Low-resource Settings

Enhancing Out-of-Vocabulary Performance of Indian TTS Systems for Practical Applications through Low-Effort Data Strategies

AI & ML interests

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