QVQ 72B Preview - a Hugging Face Space by Qwen Discover amazing ML apps made by the community

You can play directly with the model via this HF space:

huggingface.co/spaces/Qwen/...

Qwen/QVQ-72B-Preview · Hugging Face We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Model weights here: huggingface.co/Qwen/QVQ-72B...

Qwen released QvQ 72B OpenAI o1 like reasoning model on Hugging Face with Vision capabilities - beating GPT4o, Claude Sonnet 3.5 🔥

meta-llama/Llama-3.3-70B-Instruct - HuggingChat Use meta-llama/Llama-3.3-70B-Instruct with HuggingChat

Chat with it live for free here:

huggingface.co/chat/models/...

3.1 70B vs 3.3 70B:

Code Generation
> HumanEval: 80.5% → 88.4% (+7.9%)
> MBPP EvalPlus: 86.0% → 87.6% (+1.6%)

Steerability
> IFEval: 87.5% → 92.1% (+4.6%)

Reasoning & Math
> GPQA Diamond (CoT): 48.0% → 50.5% (+2.5%)
> MATH (CoT): 68.0% → 77.0% (+9%)

meta-llama/Llama-3.3-70B-Instruct · Hugging Face We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Llama 3.3 70B vs 405B:

> GPQA Diamond (CoT): 50.5% vs 49.0%
> Math (CoT): 77.0% vs 73.8%
> Steerability (IFEval): 92.1% vs 88.6%

huggingface.co/meta-llama/L...

BOOOOM! Meta released Llama 3.3 70B - 128K context, multilingual, enhanced tool calling, outperforms Llama 3.1 70B and comparable to Llama 405B 🔥

Comparable performance to 405B with 6x LESSER parameters ⚡

GitHub - huggingface/parler-tts: Inference and training library for high-quality TTS models. Inference and training library for high-quality TTS models. - huggingface/parler-tts

Ofc.. here's the codebase:

github.com/huggingface/...

Indic Parler-TTS - a Hugging Face Space by ai4bharat A demo of Indic Parler-TTS

And.. here's a space to try out the model too:

huggingface.co/spaces/ai4bh...

ai4bharat/indic-parler-tts · Hugging Face We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Check out the model checkpoints here:

huggingface.co/ai4bharat/in...

Introducing Indic-Parler TTS - Trained on 10K hours of data, 938M params, supports 20 Indic languages, emotional synthesis, apache 2.0 licensed! 🔥

w/ fully customisable speech and voice personas!

Try it out directly below or use the model weights as you want!

🇮🇳/acc

Hugging Face – The AI community building the future. We’re on a journey to advance and democratize artificial intelligence through open source and open science.

try it out today on hf.co/datasets - just click on `SQL Console` followed by `AI Query` 💯

you can just do things - ask AI to create your SQL queries and execute them right in your browser! 🔥

let your creativity guide you - powered by qwen 2.5 coder 32b ⚡

available on all 254,746 public datasets on the hub!

go check it out today! 🤗

This demo of structured data extraction running on an LLM that executes entirely in the browser (Chrome only for the moment since it uses WebGPU) is amazing

My notes here: simonwillison.net/2024/Nov/29/...

GitHub - Vaibhavs10/github-issue-generator-webgpu Contribute to Vaibhavs10/github-issue-generator-webgpu development by creating an account on GitHub.

Here's the GitHub repo in case you fancy it:

github.com/Vaibhavs10/g...

Github Issue Generator - a Hugging Face Space by reach-vb Discover amazing ML apps made by the community

To showcase how much you can do with just a 1.7B LLM, you pass free text, define a schema of parsing the text into a GitHub issue (title, description, categories, tags, etc) - Let MLC & XGrammar do the rest!

That's it, the code is super readable, try it out today! 🤗

huggingface.co/spaces/reach...

Fuck it! Structured Generation w/ SmolLM2 running in browser & WebGPU 🔥

Powered by MLC Web-LLM & XGrammar ⚡

Define a JSON schema, Input free text, get structured data right in your browser - profit!!

FYI, here's the entire code to create a dataset of every single bsky message in real time:

```
from atproto import *
def f(m): print(m.header, parse_subscribe_repos_message())
FirehoseSubscribeReposClient().start(f)
```

Transformers: Origins An unofficial origin story of the transformer neural network architecture.

I have converted a portion of my NLP Online Masters course to blog form. This is the progression I present that takes one from recurrent neural network to seq2seq with attention to transformer. mark-riedl.medium.com/transformers...

I'm disheartened by how toxic and violent some responses were here.

There was a mistake, a quick follow up to mitigate and an apology. I worked with Daniel for years and is one of the persons most preoccupied with ethical implications of AI. Some replies are Reddit-toxic level. We need empathy.

Hymba - a nvidia Collection A series of Hybrid Small Language Models.

> uses 90% sliding window and 10% global attention for efficiency

> 2-stage pre-training and 3-phase post-training, including a trapezoid learning rate schedule

try it out on hugging face today! 🤗

huggingface.co/collections/...

yo! nvidia finally released the weights for Hymba-1.5B - outperforms Qwen, and SmolLM2 w/ 6-12x less training

trained ONLY on 1.5T tokens

> massive reductions in KV cache size and improved throughput
> combines Mamba and Attention in a hybrid parallel architecture with a 5:1 ratio and meta-tokens

Let's go! We are releasing SmolVLM, a smol 2B VLM built for on-device inference that outperforms all models at similar GPU RAM usage and tokens throughputs.

SmolVLM can be fine-tuned on a Google collab and be run on a laptop! Or process millions of documents with a consumer GPU!

OuteAI/OuteTTS-0.2-500M-GGUF · Hugging Face We’re on a journey to advance and democratize artificial intelligence through open source and open science.

You can run inference via llama.cpp too:

huggingface.co/OuteAI/OuteT...

OuteAI/OuteTTS-0.2-500M · Hugging Face We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Model weights on the hub, you can even run this on a Raspberry Pi! Go run, inference now! 🐐

huggingface.co/OuteAI/OuteT...

Smol TTS keeps getting better! Introducing OuteTTS v0.2 - 500M parameters, multilingual with voice cloning! 🔥

> Multilingual - English, Chinese, Korean & Japanese
> Cross platform inference w/ llama.cpp
> Trained on 5 Billion audio tokens
> Qwen 2.5 0.5B LLM backbone
> Trained via HF GPU grants

💯

🐐

It depends on what you define long context; I'm fairly confident up until 64K and moderately till 128K, beyond that - I've personally never tested.

Most of my observations are based on chat use-cases.

Yeah! @loubnabnl.hf.co & @eliebak.bsky.social are 🐐