prithivMLmods (Prithiv Sakthi)

reacted to davanstrien's post with 🧠 about 2 hours ago

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Hacked together a way to log trl GRPO training completions to a 🤗 dataset repo. This allows you to:

- Track rewards from multiple reward functions
- Treat the completion and rewards from training as a "proper" dataset and do EDA
- Share results for open science

The implementation is super hacky, but I'm curious if people would find this useful.

To push completions to the Hub, you just need two extra parameters:

log_completions=True
log_completions_hub_repo='your-username/repo-name'

Example dataset: davanstrien/test-logs
Colab: https://colab.research.google.com/drive/1wzBFPVthRYYTp-mEYlznLg_e_0Za1M3g

reacted to merve's post with 🧠🧠 about 12 hours ago

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Google just released PaliGemma 2 Mix: new versatile instruction vision language models 🔥

> Three new models: 3B, 10B, 28B with res 224, 448 💙
> Can do vision language tasks with open-ended prompts, understand documents, and segment or detect anything 🤯

Read more https://huggingface.co/blog/paligemma2mix
Try the demo google/paligemma2-10b-mix
All models are here google/paligemma-2-mix-67ac6a251aaf3ee73679dcc4

reacted to burtenshaw's post with 🚀 1 day ago

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AGENTS + FINETUNING! This week Hugging Face learn has a whole pathway on finetuning for agentic applications. You can follow these two courses to get knowledge on levelling up your agent game beyond prompts:

1️⃣ New Supervised Fine-tuning unit in the NLP Course https://huggingface.co/learn/nlp-course/en/chapter11/1
2️⃣New Finetuning for agents bonus module in the Agents Course https://huggingface.co/learn/agents-course/bonus-unit1/introduction

Fine-tuning will squeeze everything out of your model for how you’re using it, more than any prompt.

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reacted to AdinaY's post with ❤️ 1 day ago

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🚀 StepFun阶跃星辰 is making BIG open moves!

Last year, their GOT-OCR 2.0 took the community by storm 🔥but many didn’t know they were also building some amazing models. Now, they’ve just dropped something huge on the hub!

📺 Step-Video-T2V: a 30B bilingual open video model that generates 204 frames (8-10s) at 540P resolution with high information density & consistency.
stepfun-ai/stepvideo-t2v

🔊 Step-Audio-TTS-3B : a TTS trained with the LLM-Chat paradigm on a large synthetic dataset, capable of generating RAP & Humming
stepfun-ai/step-audio-67b33accf45735bb21131b0b

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posted an update 2 days ago

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Dino: The Minimalist Multipurpose Chat System 🌠
Agent-Dino : prithivMLmods/Agent-Dino
Github: https://github.com/PRITHIVSAKTHIUR/Agent-Dino

By default, it performs the following tasks:
{Text-to-Text Generation}, {Image-Text-Text Generation}
@image: Generates an image using Stable Diffusion xL.
@3d: Generates a 3D mesh.
@web: Web search agents.
@rAgent: Initiates a reasoning chain using Llama mode for coding explanations.
@tts1-♀, @tts2-♂: Voice generation (Female and Male voices).
@yolo : Object Detection

reacted to ZennyKenny's post with 🤗 2 days ago

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Really excited to start contributing to the SWE Arena project: https://swe-arena.com/

Led by IBM PhD fellow @terryyz , our goal is to advance research in code generation and app development by frontier LLMs.

reacted to sayakpaul's post with 🔥 2 days ago

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Inference-time scaling meets Flux.1-Dev (and others) 🔥

Presenting a simple re-implementation of "Inference-time scaling diffusion models beyond denoising steps" by Ma et al.

I did the simplest random search strategy, but results can potentially be improved with better-guided search methods.

Supports Gemini 2 Flash & Qwen2.5 as verifiers for "LLMGrading" 🤗

The steps are simple:

For each round:

1> Starting by sampling 2 starting noises with different seeds.
2> Score the generations w.r.t a metric.
3> Obtain the best generation from the current round.

If you have more compute budget, go to the next search round. Scale the noise pool (2 ** search_round) and repeat 1 - 3.

This constitutes the random search method as done in the paper by Google DeepMind.

Code, more results, and a bunch of other stuff are in the repository. Check it out here: https://github.com/sayakpaul/tt-scale-flux/ 🤗

posted an update 4 days ago

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The last week of Impression Craft Arts and sketches from strangerzonehf🎨🧑🏻‍🎨

- Collection : strangerzonehf/Flux-Ultimate-LoRA-Collection

Adapters:
+ Ld-Art : strangerzonehf/Ld-Art
+ Animeopix-Flux : strangerzonehf/Animeopix-Flux
+ Flux-Super-Paint-LoRA : strangerzonehf/Flux-Super-Paint-LoRA
+ CinematicShot-Pics-Flux : strangerzonehf/cinematicShot-Pics-Flux
+ Oil-Wall-Art-Flux : strangerzonehf/Oil-Wall-Art-Flux
+ Pixelo-Flux : strangerzonehf/Pixelo-Flux
+ Abstract-Shattered : strangerzonehf/Abstract-Shattered
+ Neon-Impressionism-Flux : strangerzonehf/Neon-Impressionism-Flux
+ NewG-Art : strangerzonehf/NewG-Art

🪧Demo : prithivMLmods/FLUX-LoRA-DLC
🤗Page : https://huggingface.co/strangerzonehf

reacted to louisbrulenaudet's post with 🤗 4 days ago

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I am pleased to introduce my first project built upon Hugging Face’s smolagents framework, integrated with Alpaca for financial market analysis automation 🦙🤗

The project implements technical indicators such as the Relative Strength Index (RSI) and Bollinger Bands to provide momentum and volatility analysis. Market data is retrieved through the Alpaca API, enabling access to historical price information across various timeframes.

AI-powered insights are generated using Hugging Face’s inference API, facilitating the analysis of market trends through natural language processing with DuckDuckGo search integration for real-time sentiment analysis based on financial news 🦆

Link to the GitHub project: https://github.com/louisbrulenaudet/agentic-market-tool

reacted to davanstrien's post with 🔥 4 days ago

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Dataset descriptions for trending Hugging Face datasets? Powered by a Smol model davanstrien/Smol-Hub-tldr

reacted to nicolay-r's post with 🤝 5 days ago

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📢 For those who consider a quick and inplace annotation of entities in JSON / CSV tabular data, I got a good news. So far releasing the latest version of the bulk-ner which does these things for you:
🌟 https://github.com/nicolay-r/bulk-ner/releases/tag/0.25.2

bulk-ner is a no-string wrapper over NER service using popular frameworks like DeepPavlov, Spacy, Flair.

What's new? The latest 0.25.2 version has the following key features:
🔧 Fixed: 🐛 the output ignores other input content in input #31
🔥 Schemas support: you can annotate various coulmns by combining them as you wish and map onto the other output colums (see 📸 below) #28

Below is the screenshot on how you can quick start of using it with Spacy models.

🌌 List of other providers @ nlp-thirdgate:
https://github.com/nicolay-r/nlp-thirdgate/tree/master/ner

reacted to davanstrien's post with ❤️ 5 days ago

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How do you make 1M+ Hugging Face models & datasets more discoverable?

davanstrien/Smol-Hub-tldr!

I fine-tuned HuggingFaceTB/SmolLM2-360M to generate one-line summaries from a model or dataset README.

Its own self-description?
"A model for generating concise summaries of model & dataset cards from the Hugging Face Hub"

The goal? Make it easier to find the right models and datasets for your specific needs. It's already powering a semantic search for datasets Space.

It's still a WIP but thanks to @loubnabnl , @anton-l , @eliebak et al, for cooking such a nice base model for fine-tuning small, efficient models for specific domains and tasks. 🙏

reacted to merve's post with 🔥 6 days ago

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Your weekly recap of open AI is here, and it's packed with models! merve/feb-14-releases-67af876b404cc27c6d837767

👀 Multimodal
> OpenGVLab released InternVideo 2.5 Chat models, new video LMs with long context
> AIDC released Ovis2 model family along with Ovis dataset, new vision LMs in different sizes (1B, 2B, 4B, 8B, 16B, 34B), with video and OCR support
> ColQwenStella-2b is a multilingual visual retrieval model that is sota in it's size
> Hoags-2B-Exp is a new multilingual vision LM with contextual reasoning, long context video understanding

💬 LLMs
A lot of math models!
> Open-R1 team released OpenR1-Math-220k large scale math reasoning dataset, along with Qwen2.5-220K-Math fine-tuned on the dataset, OpenR1-Qwen-7B
> Nomic AI released new Nomic Embed multilingual retrieval model, a MoE with 500 params with 305M active params, outperforming other models
> DeepScaleR-1.5B-Preview is a new DeepSeek-R1-Distill fine-tune using distributed RL on math
> LIMO is a new fine-tune of Qwen2.5-32B-Instruct on Math

🗣️ Audio
> Zonos-v0.1 is a new family of speech recognition models, which contains the model itself and embeddings

🖼️ Vision and Image Generation
> We have ported DepthPro of Apple to transformers for your convenience!
> illustrious-xl-v1.0 is a new illustration generation model

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reacted to davidberenstein1957's post with 🔥 8 days ago

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🚀 Find banger tools for your smolagents!

I created the Tools gallery, which makes tools specifically developed by/for smolagents searchable and visible. This will help with:
- inspiration
- best practices
- finding cool tools

Space: davidberenstein1957/smolagents-and-tools

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reacted to burtenshaw's post with 🔥 9 days ago

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The Hugging Face agents course is finally out!

👉 https://huggingface.co/agents-course

This first unit of the course sets you up with all the fundamentals to become a pro in agents.

- What's an AI Agent?
- What are LLMs?
- Messages and Special Tokens
- Understanding AI Agents through the Thought-Action-Observation Cycle
- Thought, Internal Reasoning and the Re-Act Approach
- Actions, Enabling the Agent to Engage with Its Environment
- Observe, Integrating Feedback to Reflect and Adapt

reacted to nicolay-r's post with 🚀 12 days ago

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📢 If you wish to empower LLM with IR and named entity recognition module, then I got relevant findings.
Just tested Flair below is how you can start for adapting for processing your CSV / JSONL data via bulk-ner
👩‍💻 code: https://github.com/nicolay-r/nlp-thirdgate/blob/master/tutorials/ner_flair_0151.sh
🤖 models: https://huggingface.co/flair

Provider: https://raw.githubusercontent.com/nicolay-r/nlp-thirdgate/refs/heads/master/ner/flair_0151.py
Framework: https://github.com/nicolay-r/bulk-ner

🚀 Performance: the default ner model (Thinkpad X1 Nano)
Batch-size 1 6it/sec
Batch-size 10+ 12it/sec

🌌 other wrappers for bulk-ner nlp-thirdgate: https://github.com/nicolay-r/nlp-thirdgate

posted an update 12 days ago

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QwQ Edge Gets a Small Update..! 💬
try now: prithivMLmods/QwQ-Edge

🚀Now, you can use the following commands for different tasks:

🖼️ @image 'prompt...' → Generates an image
🔉@tts1 'prompt...' → Generates speech in a female voice
🔉 @tts2 'prompt...' → Generates speech in a male voice
🅰️@text 'prompt...' → Enables textual conversation (If not specified, text-to-text generation is the default mode)

💬Multimodality Support : prithivMLmods/Qwen2-VL-OCR-2B-Instruct
💬For text generation, the FastThink-0.5B model ensures quick and efficient responses, prithivMLmods/FastThink-0.5B-Tiny
💬Image Generation: sdxl lightning model, SG161222/RealVisXL_V4.0_Lightning

Github: https://github.com/PRITHIVSAKTHIUR/QwQ-Edge

graph TD
    A[User Interface] --> B[Chat Logic]
    B --> C{Command Type}
    C -->|Text| D[FastThink-0.5B]
    C -->|Image| E[Qwen2-VL-OCR-2B]
    C -->|@image| F[Stable Diffusion XL]
    C -->|@tts| G[Edge TTS]
    D --> H[Response]
    E --> H
    F --> H
    G --> H

reacted to burtenshaw's post with 🤗 13 days ago

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SmolLM2 paper is out! 😊

😍 Why do I love it? Because it facilitates teaching and learning!

Over the past few months I've engaged with (no joke) thousands of students based on SmolLM.

- People have inferred, fine-tuned, aligned, and evaluated this smol model.
- People used they're own machines and they've used free tools like colab, kaggle, and spaces.
- People tackled use cases in their job, for fun, in their own language, and with their friends.

upvote the paper SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model (2502.02737)

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reacted to nicolay-r's post with 🧠 14 days ago

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🚨 Key takeaway of a quick mastering Sentiment Analysis nowadays. Trough the questionare 📝 of the past RuOpinoinNE-2024 competition we got insights and participants model preference chocies. Our main conclusion:

✨ The submissions of the top performed models exploit Few-shot learning for LLM.

Takeaway note comparing with the prior RuSentNE-2023 competition:
🧠 Reasoning in steps requires more actions for tweaking. Most recent solutions empowered with Chain-of-Thouhgt are tend to think too much. Earlier we might see improvements for the Flan-T5 (2.8B) in fine-tuned mode but not among the zero-shot approaches.
nicolay-r/flan-t5-tsa-thor-xl

Related materials:
https://github.com/dialogue-evaluation/RuOpinionNE-2024
RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian News Texts (2305.17679)
Large Language Models in Targeted Sentiment Analysis (2404.12342)

Prithiv Sakthi

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