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CidoyiΒ  published a Space about 2 months ago
caiehack/tabicl
CidoyiΒ  updated a Space about 2 months ago
caiehack/tabicl
CidoyiΒ  published a dataset about 2 months ago
caiehack/uq_data
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CidoyiΒ 
published a Space about 2 months ago
CidoyiΒ 
updated a Space about 2 months ago
CidoyiΒ 
published a dataset about 2 months ago
CidoyiΒ 
updated a dataset about 2 months ago
reach-vbΒ 
posted an update 5 months ago
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5482
Excited to onboard FeatherlessAI on Hugging Face as an Inference Provider - they bring a fleet of 6,700+ LLMs on-demand on the Hugging Face Hub 🀯

Starting today, you'd be able to access all those LLMs (OpenAI compatible) on HF model pages and via OpenAI client libraries too! πŸ’₯

Go, play with it today: https://huggingface.co/blog/inference-providers-featherless

P.S. They're also bringing on more GPUs to support all your concurrent requests!
  • 1 reply
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reach-vbΒ 
posted an update 5 months ago
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4557
hey hey @mradermacher - VB from Hugging Face here, we'd love to onboard you over to our optimised xet backend! πŸ’₯

as you know we're in the process of upgrading our storage backend to xet (which helps us scale and offer blazingly fast upload/ download speeds too): https://huggingface.co/blog/xet-on-the-hub and now that we are certain that the backend can scale with even big models like Llama 4/ Qwen 3 - we;re moving to the next phase of inviting impactful orgs and users on the hub over as you are a big part of the open source ML community - we would love to onboard you next and create some excitement about it in the community too!

in terms of actual steps - it should be as simple as one of the org admins to join hf.co/join/xet - we'll take care of the rest.

p.s. you'd need to have a the latest hf_xet version of huggingface_hub lib but everything else should be the same: https://huggingface.co/docs/hub/storage-backends#using-xet-storage

p.p.s. this is fully backwards compatible so everything will work as it should! πŸ€—
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reach-vbΒ 
authored a paper 7 months ago
ngxsonΒ 
posted an update 8 months ago
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5275
A comprehensive matrix for which format should you use.

Read more on my blog post: https://huggingface.co/blog/ngxson/common-ai-model-formats

| Hardware        | GGUF      | PyTorch                | Safetensors              | ONNX  |
|-----------------|-----------|------------------------|--------------------------|-------|
| CPU             | βœ… (best) | 🟑                      | 🟑                       | βœ…    |
| GPU             | βœ…        | βœ…                      | βœ…                       | βœ…    |
| Mobile          | βœ…        | 🟑 (via executorch)     | ❌                       | βœ…    |
| Apple silicon   | βœ…        | 🟑                      | βœ… (via MLX framework)   | βœ…    |
  • 1 reply
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ngxsonΒ 
authored a paper 9 months ago
reach-vbΒ 
authored a paper 9 months ago
ngxsonΒ 
posted an update 9 months ago
ngxsonΒ 
posted an update 10 months ago
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3864
Check out my collection of pre-made GGUF LoRA adapters!

This allow you to use both normal + abliterated version of popular models like llama, qwen, etc, without having to double to amount of VRAM usage.

ngxson/gguf_lora_collection
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ngxsonΒ 
posted an update 10 months ago
reach-vbΒ 
posted an update 11 months ago
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7241
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: @jiuhai

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

What a time to be alive! πŸ”₯
reach-vbΒ 
posted an update 11 months ago
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5076
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 (https://huggingface.co/collections/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€—
ali-chararaΒ 
updated a model 11 months ago
lukalafayeΒ 
updated a Space 11 months ago
OrdinaryDev83Β 
updated a model 11 months ago
OrdinaryDev83Β 
updated a dataset 11 months ago
CidoyiΒ 
updated a Space 12 months ago