John6666 (John Smith)

Reacted to nicolay-r's post with 👀 about 5 hours ago

Post

277

📢 If you were earlier interested in quick translator application for bunch of texts with spans of fixed parts that tolerant for translation, then this post might be relevant! Delighted to share a bulk_translate -- a framework for automatic texts translation with the pre-anotated fixed spans.

📦 https://pypi.org/project/bulk-translate/
🌟 https://github.com/nicolay-r/bulk-translate

🔑 Spans allows you to control your objects in texts, so that objects would be tollerant to translator. By default it provides implementation for GoogleTranslate.

bulk_translate features:
✅ Native Implementation of two translation modes:
- fast-mode: exploits extra chars for grouping text parts into single batch
- accurate: pefroms individual translation of each text part.
✅ No strings: you're free to adopt any LM / LLM backend.
Support googletrans by default.

The initial release of the project supports fixed spans as text parts wrapped in square brackets [] with non inner space characters.

You can play with your data in CSV here on GoogleColab:
📒 https://colab.research.google.com/github/nicolay-r/bulk-translate/blob/master/bulk_translate_demo.ipynb

👏 This project is based on AREkit 0.25.1 pipelines for deployment lm-based workflows:
https://github.com/nicolay-r/AREkit

Reacted to prithivMLmods's post with 🔥 about 19 hours ago

Post

679

Weekend Dribble 📦🍺

Adapters for Product Ad Backdrops, Smooth Polaroids, Minimalist Sketch cards, Super Blends!!

🤏Demo on: prithivMLmods/FLUX-LoRA-DLC

Stranger Zones :
👉🏼{ Super Blend } : strangerzonehf/Flux-Super-Blend-LoRA

👉🏼{ Product Concept Ad } : prithivMLmods/Flux-Product-Ad-Backdrop
👉🏼{ Frosted Mock-ups } : prithivMLmods/Flux.1-Dev-Frosted-Container-LoRA
👉🏼{ Polaroid Plus } : prithivMLmods/Flux-Polaroid-Plus
👉🏼{Sketch Cards} : prithivMLmods/Flux.1-Dev-Sketch-Card-LoRA

👉Stranger Zone: https://huggingface.co/strangerzonehf

👉Flux LoRA Collections: prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be

.
.
.
@prithivMLmods 🤗

Reacted to jeffboudier's post with 🤗 about 19 hours ago

Post

394

New - add your bluesky account to your HF profile:
https://huggingface.co/settings/profile

Is the grass greener, the sky bluer? Will try and figure it out at https://bsky.app/profile/jeffboudier.bsky.social

By the way, HF people starter pack https://bsky.app/starter-pack/huggingface.bsky.social/3laz5x7naiz22

Reacted to andrewrreed's post with 👀 about 19 hours ago

Post

315

Trace LLM calls with Arize AI's Phoenix observability dashboards on Hugging Face Spaces! 🚀

✨ I just added a new recipe to the Open-Source AI Cookbook that shows you how to:
1️⃣ Deploy Phoenix on HF Spaces with persistent storage in a few clicks
2️⃣ Configure LLM tracing with the 𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗔𝗣𝗜
3️⃣ Observe multi-agent application runs with the CrewAI integration

𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝗰𝗿𝘂𝗰𝗶𝗮𝗹 for building robust LLM apps.

Phoenix makes it easy to visualize trace data, evaluate performance, and track down issues. Give it a try!

🔗 Cookbook recipe: https://huggingface.co/learn/cookbook/en/phoenix_observability_on_hf_spaces
🔗 Phoenix docs: https://docs.arize.com/phoenix

Reacted to rwightman's post with 👍 about 19 hours ago

Post

409

I'm currently on a push to expand the scope of image based datasets on the Hub. There's certainly a lot already, but for anyone who's looked closely, there's not a whole lot of standardization. I am to fix that, datasets under the https://huggingface.co/timm and https://huggingface.co/pixparse orgs will serve as canonical examples for various task / modality combinations and be useable without fuss in libraries like timm, OpenCLIP, and hopefully more.

I just uploaded the first multi-label dataset that I'll support with timm scripts soon: timm/plant-pathology-2021

Next up object detection & segmentation! I've got an annotation spec sorted out, a lot of datasets ready to rip, and yeah that means timm support for object detection, eventually segmentation, is finally under development :O

Reacted to ednsinf's post with 👀 about 19 hours ago

Post

216

All wisper or transcriptions projects spaces (including Zero GPUs plans) are very slow or have many quotas bugs or processing errors. I tested all... anything can transcribe a single 3 min short audio file!!! How it´s possible???

3 replies

·

replied to ednsinf's post about 19 hours ago

Basically, the HF Spaces are for demonstration purposes. There is no problem using them for other than demonstrations, though.
The free CPU Spaces are slow because they don't have GPUs, and the Zero GPU Spaces have a quota, so in principle you have to finish processing within 120 seconds.😓
If someone were to release a space on a pay-as-you-go GPU plan, there would probably be no restrictions, but that would be quite rare.
For serious use, you should consider local environments or a paid service including other companies.

Reacted to MonsterMMORPG's post with 🚀👀 about 19 hours ago

Post

337

NVIDIA Labs developed SANA model weights and Gradio demo app published —Check out this amazing new Text to Image model by NVIDIA

Official repo : https://github.com/NVlabs/Sana

1-Click Windows, RunPod, Massed Compute installers and free Kaggle notebook : https://www.patreon.com/posts/116474081

You can follow instructions on the repository to install and use locally. I tested on my Windows RTX 3060 and 3090 GPUs.

I have tested some speeds and VRAM usage too

Uses 9.5 GB VRAM but someone reported works good on 8 GB GPUs too

Default settings per image speeds as below

Free Kaggle Account Notebook on T4 GPU : 15 second
RTX 3060 (12 GB) : 9.5 second
RTX 3090 : 4 second
RTX 4090 : 2 second
More info : https://nvlabs.github.io/Sana/

Works great on RunPod and Massed Compute as well (cloud)

Sana : Efficient High-Resolution Image Synthesis
with Linear Diffusion Transformer

About Sana — Taken from official repo

We introduce Sana, a text-to-image framework that can efficiently generate images up to 4096 × 4096 resolution. Sana can synthesize high-resolution, high-quality images with strong text-image alignment at a remarkably fast speed, deployable on laptop GPU. Core designs include: Deep compression autoencoder: unlike traditional AEs, which compress images only 8×, we trained an AE that can compress images 32×, effectively reducing the number of latent tokens. Linear DiT: we replace all vanilla attention in DiT with linear attention, which is more efficient at high resolutions without sacrificing quality. Decoder-only text encoder: we replaced T5 with modern decoder-only small LLM as the text encoder and designed complex human instruction with in-context learning to enhance the image-text alignment. Efficient training and sampling: we propose Flow-DPM-Solver to reduce sampling steps, with efficient caption labeling and selection to accelerate convergence.

Reacted to fdaudens's post with 🤗❤️ about 19 hours ago

Post

688

🦋 Hug the butterfly! You can now add your Bluesky handle to your Hugging Face profile! ✨

Reacted to hexgrad's post with 👍 about 19 hours ago

Post

699

hexgrad/Kokoro-TTS just got an upgrade that substantially improves TTS naturalness for short bursts while maintaining parity for longer utterances! 🔥

Read more and listen to before/after audio samples at https://hf.co/blog/hexgrad/kokoro-short-burst-upgrade

(Probably would have made that Article a Post instead, if audio could be embedded into Posts.)

1 reply

·

replied to Keltezaa's post 1 day ago

Also refer it.
https://huggingface.co/docs/hub/models-gated

Reacted to Keltezaa's post with 👀 1 day ago

Post

288

401 Client Error: Unauthorized for url: black-forest-labs/FLUX.1-dev

The above exception was the direct cause of the following exception:

huggingface_hub.errors.GatedRepoError: 401 Client Error. (Request ID: Root=1-6740a1d6-26b6f3b44563a26a49aea19d;fa54ac02-6068-44e1-b499-f793dd20335c)

Cannot access gated repo for url black-forest-labs/FLUX.1-dev.
Access to model black-forest-labs/FLUX.1-dev is restricted. You must have access to it and be authenticated to access it. Please log in.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/home/user/app/app.py", line 32, in <module>
good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)

2 replies

·

Reacted to lin-tan's post with 🔥 1 day ago

Post

651

Can language models replace developers? #RepoCod says “Not Yet”, because GPT-4o and other LLMs have <30% accuracy/pass@1 on real-world code generation tasks.
- Leaderboard https://lt-asset.github.io/REPOCOD/
- Dataset: lt-asset/REPOCOD
@jiang719 @shanchao @Yiran-Hu1007
Compared to #SWEBench, RepoCod tasks are
- General code generation tasks, while SWE-Bench tasks resolve pull requests from GitHub issues.
- With 2.6X more tests per task (313.5 compared to SWE-Bench’s 120.8).

Compared to #HumanEval, #MBPP, #CoderEval, and #ClassEval, RepoCod has 980 instances from 11 Python projects, with
- Whole function generation
- Repository-level context
- Validation with test cases, and
- Real-world complex tasks: longest average canonical solution length (331.6 tokens) and the highest average cyclomatic complexity (9.00)

Introducing hashtag #RepoCod-Lite 🐟 for faster evaluations: 200 of the toughest tasks from RepoCod with:
- 67 repository-level, 67 file-level, and 66 self-contains tasks
- Detailed problem descriptions (967 tokens) and long canonical solutions (918 tokens)
- GPT-4o and other LLMs have < 10% accuracy/pass@1 on RepoCod-Lite tasks.
- Dataset: lt-asset/REPOCOD_Lite

#LLM4code #LLM #CodeGeneration #Security

1 reply

·

Reacted to m-ric's post with 🚀 1 day ago

Post

715

𝗡𝗲𝘄 𝗹𝗲𝗮𝗱𝗲𝗿𝗯𝗼𝗮𝗿𝗱 𝗿𝗮𝗻𝗸𝘀 𝗟𝗟𝗠𝘀 𝗳𝗼𝗿 𝗟𝗟𝗠-𝗮𝘀-𝗮-𝗷𝘂𝗱𝗴𝗲: 𝗟𝗹𝗮𝗺𝗮-𝟯.𝟭-𝟳𝟬𝗕 𝘁𝗼𝗽𝘀 𝘁𝗵𝗲 𝗿𝗮𝗻𝗸𝗶𝗻𝗴𝘀! 🧑‍⚖️

Evaluating systems is critical during prototyping and in production, and LLM-as-a-judge has become a standard technique to do it.

First, what is "LLM-as-a-judge"?
👉 It's a very useful technique for evaluating LLM outputs. If anything you're evaluating cannot be properly evaluated with deterministic criteria, like the "politeness" of an LLM output, or how faithful it is to an original source, you can use LLM-judge instead : prompt another LLM with "Here's an LLM output, please rate this on criterion {criterion} on a scale of 1 to 5", then parse the number from its output, and voilà, you get your score.

🧐 But who judges the judge?
How can you make sure your LLM-judge is reliable? You can have a specific dataset annotated with scores provided by human judges, and compare how LLM-judge scores correlate with human judge scores.

📊 Before even running that benchmark, to get you started, there's a new option to get you started: a leaderboard that measures how well different model perform as judges!

And the outcome is surprising, models come in quite different orders from what we're used to in general rankings: probably some have much better bias mitigation than others!

Take a deeper look here 👉 https://huggingface.co/blog/arena-atla

Reacted to BrigitteTousi's post with 🚀 1 day ago

Post

744

I'm biased but I think HF Posts is the #1 social platform for the AI community! 🤗 That being said, most of us are already on X and now also joining Bluesky.

Looking for us on Bsky? We started a team list here: https://bsky.app/starter-pack/did:plc:yyfrnpcutxghwc6eac4xplwp/3lbem54cnxp26

Reacted to victor's post with 🚀🔥👀 1 day ago

Post

1509

Qwen2.5-72B is now the default HuggingChat model.
This model is so good that you must try it! I often get better results on rephrasing with it than Sonnet or GPT-4!!

John Smith PRO

AI & ML interests

Recent Activity

Organizations

John6666's activity