Evan

evdcush

3 188 810

AI & ML interests

None yet

Recent Activity

posted an update about 23 hours ago

Sample output from https://huggingface.co/ACE-Step/acestep-v15-xl-sft I had issues with the upstream ACE-Step1.5 pipeline, and seeing there were other reports of issues with the python pipeline, but not the Comfy implementation, I used the Comfy `audio_ace_step1_5_xl_sft` workflow (https://github.com/Comfy-Org/workflow_templates/blob/8417f4f2a8380556070721d0ff1da8285d6e5438/templates/audio_ace_step1_5_xl_sft.json). Inference params: ``` "TextEncodeAceStepAudio1.5": { "bpm": 96, "duration": 260, "timesignature": "4", "language": "ja", "keyscale": "F# minor", "cfg_scale": 2, "temperature": 0.85, "top_p": 1, "top_k": 0, "min_p": 0 } "KSampler": { "steps": 50, "cfg": 1, "denoise": 1 } ``` Both "lyrics" and "tags" were defined, but the the vocals in the output were not properly generated (perhaps due to the low sampler cfg).

liked a model 7 days ago

nvidia/music-flamingo-2601-hf

liked a model 8 days ago

nvidia/NVIDIA-NemotronLabs-VoiceChat-11B

View all activity

Organizations

posted an update about 23 hours ago

Post

Sample output from ACE-Step/acestep-v15-xl-sft

I had issues with the upstream ACE-Step1.5 pipeline, and seeing there were other reports of issues with the python pipeline, but not the Comfy implementation, I used the Comfy audio_ace_step1_5_xl_sft workflow (https://github.com/Comfy-Org/workflow_templates/blob/8417f4f2a8380556070721d0ff1da8285d6e5438/templates/audio_ace_step1_5_xl_sft.json).

Inference params:

"TextEncodeAceStepAudio1.5": {
    "bpm": 96,
    "duration": 260,
    "timesignature": "4",
    "language": "ja",
    "keyscale": "F# minor",
    "cfg_scale": 2,
    "temperature": 0.85,
    "top_p": 1,
    "top_k": 0,
    "min_p": 0
}



"KSampler": {
    "steps": 50,
    "cfg": 1,
    "denoise": 1
}

Both "lyrics" and "tags" were defined, but the the vocals in the output were not properly generated (perhaps due to the low sampler cfg).

reacted to mlabonne's post with 🤗 4 months ago

Post

4908

Big update to llm-datasets, my curated list of datasets and tools for post-training LLMs.

> Added many new datasets
> New "thinking" column
> Refreshed recommended tools.

Thanks to everyone who told me they used it for their research at ICLR, you motivated this update!

2 replies

reacted to codelion's post with 🔥 9 months ago

Post

2026

Introducing OpenEvolve Prompt Optimizer - a Space that automatically evolves and optimizes your prompts using OpenEvolve!

This tool uses OpenEvolve to iteratively improve prompts by testing them on real datasets and evolving better versions. No more manual prompt engineering guesswork - let OpenEvolve find the optimal prompts for you.

How it works:
- Enter your initial prompt using {input} as a placeholder for dataset inputs
- Input any HuggingFace dataset name you want to use for optimization
- Specify the dataset split and field names for your use case
- Click Optimize Prompt and the system will validate everything first
- Compare your initial prompt vs the evolved best prompt side-by-side

Try it here: algorithmicsuperintelligence/prompt-optimizer

OpenEvolve GitHub: https://github.com/algorithmicsuperintelligence/openevolve

reacted to sergiopaniego's post with 🚀 10 months ago

Post

3243

Meet OpenEnv 👋, an open ecosystem of environments for intelligent agents. Build, share, and test agents safely and consistently.

Ideal for training with TRL (we include examples🤓), deployment, and community collaboration via the HF Hub

Blog: https://huggingface.co/blog/openenv
Hub for Environments:

openenv
OpenEnv repo: https://github.com/meta-pytorch/OpenEnv
Try it out using TRL: https://huggingface.co/docs/trl/main/en/openenv

1 reply

reacted to m-ric's post with 🔥 about 1 year ago

Post

4558

I've made an open version of Google's NotebookLM, and it shows the superiority of the open source tech task! 💪

The app's workflow is simple. Given a source PDF or URL, it extracts the content from it, then tasks Meta's Llama 3.3-70B with writing the podcast script, with a good prompt crafted by @gabrielchua ("two hosts, with lively discussion, fun notes, insightful question etc.")
Then it hands off the text-to-speech conversion to Kokoro-82M, and there you go, you have two hosts discussion any article.

The generation is nearly instant, because:
> Llama 3.3 70B is running at 1,000 tokens/seconds with Cerebras inference
> The audio is generated in streaming mode by the tiny (yet powerful) Kokoro, generating voices faster than real-time.

And the audio generation runs for free on Zero GPUs, hosted by HF on H200s.

Overall, open source solutions rival the quality of closed-source solutions at close to no cost!

Try it here 👉👉 m-ric/open-notebooklm

3 replies

reacted to burtenshaw's post with 🔥👍❤️ over 1 year ago

Post

2872

For anyone looking to boost their LLM fine-tuning and alignment skills this decemeber. We're running this free and open course called smol course. It’s not big like Li Yin and @mlabonne , it’s just smol.

👷 It focuses on practical use cases, so if you’re working on something, bring it along.

👯‍♀️ It’s peer reviewed and open so you can discuss and get feedback.

🤘 If you’re already a smol pro, feel free to drop a star or issue.

> > Part 1 starts now, and it’s on instruction tuning!

https://github.com/huggingface/smol-course

reacted to rwightman's post with 🚀❤️ over 2 years ago

Post

1891

timm 1.0 is finally out. The big feature that I wanted to complete before doing this? Having the unified feature map extraciton interface (features_only=True) supporting almost all models (97%) 🎉 See docs at https://huggingface.co/docs/timm/en/feature_extraction

Also in this release, the new set of SBB (searching for better baselins) ViT models, covering new architectures and hparam exploration between tiny and base. See timm/searching-for-better-vit-baselines-663eb74f64f847d2f35a9c19

I also snuck in image-tower loading for PaliGemma (via jax weights on Hub) google/paligemma-release-6643a9ffbf57de2ae0448dda

reacted to radames's post with 🚀🤯🧠❤️🔥 over 2 years ago

Post

2229

AI-town now runs on Hugging Face Spaces with our API for LLMs and embeddings, including the open-source Convex backend, all in one container. Easy to duplicate and config on your own

Demo: radames/ai-town
Instructions: https://github.com/radames/ai-town-huggingface

9 replies

reacted to chiphuyen's post with ❤️ over 2 years ago

Post

Huggingface is carrying the AI open source ecosystem https://huyenchip.com/2024/03/14/ai-oss.html

4 replies

reacted to akhaliq's post with 👍 over 2 years ago

Post

Stealing Part of a Production Language Model

Stealing Part of a Production Language Model (2403.06634)

We introduce the first model-stealing attack that extracts precise, nontrivial information from black-box production language models like OpenAI's ChatGPT or Google's PaLM-2. Specifically, our attack recovers the embedding projection layer (up to symmetries) of a transformer model, given typical API access. For under \20 USD, our attack extracts the entire projection matrix of OpenAI's Ada and Babbage language models. We thereby confirm, for the first time, that these black-box models have a hidden dimension of 1024 and 2048, respectively. We also recover the exact hidden dimension size of the gpt-3.5-turbo model, and estimate it would cost under 2,000 in queries to recover the entire projection matrix. We conclude with potential defenses and mitigations, and discuss the implications of possible future work that could extend our attack.

2 replies

reacted to yjernite's post with 🤗❤️ over 2 years ago

Post

👷🏽‍♀️📚🔨 Announcing the Foundation Model Development Cheatsheet!

My first 🤗Post🤗 ever to announce the release of a fantastic collaborative resource to support model developers across the full development stack: The FM Development Cheatsheet available here: https://fmcheatsheet.org/

The cheatsheet is a growing database of the many crucial resources coming from open research and development efforts to support the responsible development of models. This new resource highlights essential yet often underutilized tools in order to make it as easy as possible for developers to adopt best practices, covering among other aspects:
🧑🏼‍🤝‍🧑🏼 data selection, curation, and governance;
📖 accurate and limitations-aware documentation;
⚡ energy efficiency throughout the training phase;
📊 thorough capability assessments and risk evaluations;
🌏 environmentally and socially conscious deployment strategies.

We strongly encourage developers working on creating and improving models to make full use of the tools listed here, and to help keep the resource up to date by adding the resources that you yourself have developed or found useful in your own practice 🤗

Congrats to all the participants in this effort for the release! Read more about it from:
@Shayne - https://twitter.com/ShayneRedford/status/1763215814860186005
@hails and @stellaathena - https://blog.eleuther.ai/fm-dev-cheatsheet/
@alon-albalak - http://nlp.cs.ucsb.edu/blog/a-new-guide-for-the-responsible-development-of-foundation-models.html

And also to @gabrielilharco @sayashk @kklyman @kylel @mbrauh @fauxneticien @avi-skowron @Bertievidgen Laura Weidinger, Arvind Narayanan, @VictorSanh @Davlan @percyliang Rishi Bommasani, @breakend @sasha 🔥

1 reply

Evan

AI & ML interests

Recent Activity

Organizations

evdcush's activity