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This is HUGE, Hugging Face just shipped Inference Providers on the Hub Partnering with Together AI, @FAL, Replicate and SambaNova! Starting today you can access thousands of Models like DeepSeek R1, Llama, Flux, Whisper, and more Directly from Hugging Face!

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AK

500,245 subscribers

177,126 Aufrufe • vor 1 Jahr •via X (Twitter)

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AKvor 1 Jahr

blog:

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APILayervor 1 Jahr

🔓 Unlock new possibilities for your business with APIs! 🚀 Discover our extensive API Marketplace, featuring top-notch, cutting-edge solutions tailored just for you. Elevate your company and drive innovation! Get started with apilayer today:

Profilbild von TheHeroShep
TheHeroShepvor 1 Jahr

@togethercompute @FAL @replicate @SambaNovaAI We love when an epic team comes together… Now who’s Hawkeye? 🤣

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Sourabhvor 1 Jahr

@togethercompute @FAL @replicate @SambaNovaAI ai game just changed fr

Profilbild von Cédric Limousin
Cédric Limousinvor 1 Jahr

@togethercompute @FAL @replicate @SambaNovaAI So like a competitor to Fal and Replicate ?

Profilbild von Rahul Asthana
Rahul Asthanavor 1 Jahr

@togethercompute @FAL @replicate @SambaNovaAI so cool

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Amoussouvivor 1 Jahr

@togethercompute @FAL @replicate @SambaNovaAI Is it free or do you need a pro account ?

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Victor Mvor 1 Jahr

This is wild 🤯 We finally have an open-source music generation model that rocks 🚀

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Unsloth AIvor 1 Jahr

Introducing 1.58bit DeepSeek-R1 GGUFs! 🐋 DeepSeek-R1 can now run in 1.58-bit, while being fully functional. We shrank the 671B parameter model from 720GB to just 131GB - a 80% size reduction. Naively quantizing all layers breaks the model entirely, causing endless loops & gibberish outputs. Our dynamic quants solve this. The 1.58-bit quant fits in 160GB VRAM (2x H100 80GB) for fast inference at ~140 tokens/sec. By studying DeepSeek-R1’s architecture, we selectively quantized certain layers to higher bits (like 4-bit), and leave most MoE layers to 1.5-bit. Benchmarks + Blog: Dynamic GGUFs (131GB–212GB) on Hugging Face:

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Qwenvor 1 Jahr

🎉 恭喜发财🧧🐍 As we welcome the Chinese New Year, we're thrilled to announce the launch of Qwen2.5-VL , our latest flagship vision-language model! 🚀 💗 Qwen Chat: 📖 Blog: 🤗 Hugging Face: 🤖 ModelScope: 🌟 Key Highlights: * Visual Understanding : From flowers to complex charts, Qwen2.5-VL sees it all! * Agentic Capabilities : It’s a visual agent that can reason and interact with tools like computers & phones. * Long Video Comprehension : Captures events in videos over 1 hour long! ⏳🎥 * Precise Localization : Generates bounding boxes & JSON outputs for accurate object detection. * Structured Data Outputs : Perfect for finance & commerce, handling invoices, forms & more! 💼📊 Try Qwen2.5-VL now at Qwen Chat or explore models on Hugging Face & ModelScope . 🌐

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SkalskiPvor 1 Jahr

Alibaba dropped QWEN2.5VL yesterday; I spend all night working on fine-tuning tutorial this notebook covers - dataset preparation - tokenization - training with LoRA/QLoRA (for max performance on low-power devices) - fine-tuned model evaluation link:

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AK

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Atomic Chat is now on Hugging Face 🤗 We're officially a Local App on the world's biggest AI hub. Run 200,000+ open-weight models from Hugging Face directly on your device - private, local, and open source!

atomic.chat

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Welcome to the party Hugging Face 🤗 Access the Hugging Face Hub directly from Gemini CLI with this new Gemini CLI extension. 🔍 - Search models, datasets and papers 📈 - Find trending models or datasets 🤗 - Learn how to fine-tune models and more! Big thanks to Shaun Smith and Vaibhav (VB) Srivastav for making this happen!

Jack Wotherspoon

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You can now run Nebius AI Studio models directly in Visual Studio Code, just select Hugging Face as your Copilot provider and enter your HF key. ✅ Use models like Qwen, DeepSeek, Kimi, GLM ⚡ Powered by Nebius inference infrastructure Open models, instant APIs, zero setup.

Nebius Token Factory

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Awesome to see the collab between Kaggle and Hugging Face. Now you can use any model from Hugging Face directly in Kaggle. Previously, we had to download and upload the models as datasets. This was much needed 🚀

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Added web search to Papers with Code's upcoming chat interface This runs on Hugging Face Inference Providers via Baseten (new DeepSeek-V4 Flash), using Exa's MCP server, running inside a Modal container Drop your HF username, and I'll add you, eager for feedback :)
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Added web search to Papers with Code's upcoming chat interface This runs on Hugging Face Inference Providers via Baseten (new DeepSeek-V4 Flash), using Exa's MCP server, running inside a Modal container Drop your HF username, and I'll add you, eager for feedback :)

Niels Rogge

20,030 Aufrufe • vor 16 Tagen

DeepSeek-R1. Now running *locally* on your Umbrel. Yes, you can now self-host open source AI models like R1, Llama, and more on your home cloud — thanks to ollama and Open WebUI. Download Open WebUI and Ollama from the Umbrel App Store to get started.
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DeepSeek-R1. Now running *locally* on your Umbrel. Yes, you can now self-host open source AI models like R1, Llama, and more on your home cloud — thanks to ollama and Open WebUI. Download Open WebUI and Ollama from the Umbrel App Store to get started.

Umbrel ☂️

36,562 Aufrufe • vor 1 Jahr

DeepSeek R1 671B is ridiculously good, but running at 158 tokens per second is something out of this world. A model *this good* running *this fast* is 3 years ahead of this time. The secret: DeepSeek in this short video isn't running on GPUs. It's running on custom-made chips! This is running on SambaNova and their SN40L chips, which are designed and optimized for running AI workflows. SambaNova is sponsoring my work. We designed GPUs to run games and later realized we could use them for AI. This is very different from creating a chip specifically for AI from the ground up. Just think about this for a second: One single SNL40 chip can simultaneously hold 100+ models (trillions of parameters) in memory! An agentic workflow that uses multiple models simultaneously won't need to swap models from memory even once! By the way, the Llama family of models *screams* on the SambaNova Cloud. Here are some numbers from my latest tests: • Llama 3.3 70B: 367.32 t/s • Llama 3.2 1B: 2381.82 t/s • Llama 3.2 3B: 1335.71 t/s • Llama 3.1 8B: 929.58 t/s • Llama 3.1 70B: 369.16 t/s • Llama 3.1 405B: 103.37 t/s You can access all of these models for free!
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DeepSeek R1 671B is ridiculously good, but running at 158 tokens per second is something out of this world. A model *this good* running *this fast* is 3 years ahead of this time. The secret: DeepSeek in this short video isn't running on GPUs. It's running on custom-made chips! This is running on SambaNova and their SN40L chips, which are designed and optimized for running AI workflows. SambaNova is sponsoring my work. We designed GPUs to run games and later realized we could use them for AI. This is very different from creating a chip specifically for AI from the ground up. Just think about this for a second: One single SNL40 chip can simultaneously hold 100+ models (trillions of parameters) in memory! An agentic workflow that uses multiple models simultaneously won't need to swap models from memory even once! By the way, the Llama family of models *screams* on the SambaNova Cloud. Here are some numbers from my latest tests: • Llama 3.3 70B: 367.32 t/s • Llama 3.2 1B: 2381.82 t/s • Llama 3.2 3B: 1335.71 t/s • Llama 3.1 8B: 929.58 t/s • Llama 3.1 70B: 369.16 t/s • Llama 3.1 405B: 103.37 t/s You can access all of these models for free!

Santiago

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BOOM! Starting today you can use open source frontier LLMs in Visual Studio Code with HF Inference Providers! 🔥 Use your inference credits on SoTA llms like GLM 4.5, Qwen3 Coder, DeepSeek 3.1 and more All of it packaged in one simple extension - try it out today 🤗

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0:30

A record 450 Al developers joined Llamapalooza NYC at NASDAQ in the heart of Times Square. Llamapalooza is a one-of-a-kind conference around fine-tuning, inference and using llama in production. We learned from the model creators AI at Meta, infra providers @CerebrasSystems, and llama developers Hugging Face. Big thanks to Val Town, Haize Labs , Slingshot AI, and LiveKit for incredible demos and the hundreds of llama developers who joined us. See you at the next one!

Cerebras

13,143 Aufrufe • vor 1 Jahr

Open source/weight models are often used in regulated industries like Health Care or Financial Services, where they handle personally identifiable data, and can't send it to proprietary LLM providers. We recently chatted to Vaibhav (VB) Srivastav about the partnership VS Visual Studio Code and Hugging Face inference providers have to let you use open weight models directly in your IDE! Something that surprised me was just how fast inference providers like Cerebras make it to generate code! In this episode we made a journaling CLI tool using Qwen! I personally love open source and hope more of these models will become small enough to run locally!
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Open source/weight models are often used in regulated industries like Health Care or Financial Services, where they handle personally identifiable data, and can't send it to proprietary LLM providers. We recently chatted to Vaibhav (VB) Srivastav about the partnership VS Visual Studio Code and Hugging Face inference providers have to let you use open weight models directly in your IDE! Something that surprised me was just how fast inference providers like Cerebras make it to generate code! In this episode we made a journaling CLI tool using Qwen! I personally love open source and hope more of these models will become small enough to run locally!

Marlene Mhangami

18,215 Aufrufe • vor 9 Monaten

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.
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Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

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