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John6666
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reacted to DawnC's post with ❤️ about 5 hours ago
New in PawMatchAI🐾 : Turn Your Dog Photos into Art! I’m excited to introduce a brand-new creative feature — Dog Style Transfer is now live on PawMatchAI! Just upload your dog’s photo and transform it into 5 artistic styles: 🌸 Japanese Anime 📚 Classic Cartoon 🖼️ Oil Painting 🎨 Watercolor 🌆 Cyberpunk All powered by Stable Diffusion and enhanced with smart prompt tuning to preserve your dog’s unique traits and breed identity , so the artwork stays true to your furry friend. Whether you're creating a custom portrait or just having fun, this feature brings your pet photos to life in completely new ways. And here’s a little secret: although it’s designed with dogs in mind, it actually works on any photo — cats, plush toys, even humans. Feel free to experiment! Results may not always be perfectly accurate, sometimes your photo might come back looking a little different, or even beyond your imagination. But that’s part of the fun! It’s all about creative surprises and letting the AI do its thing. Try it now: https://huggingface.co/spaces/DawnC/PawMatchAI If this new feature made you smile, a ❤️ for this space would mean a lot. #AIArt #StyleTransfer #StableDiffusion #ComputerVision #MachineLearning #DeepLearning
reacted to severo's post with 👀 about 5 hours ago
Need to convert CSV to Parquet? Use https://www.chatdb.ai/tools/csv-to-parquet-converter. It does the job instantly. @cfahlgren1 provides many other tools on his website. Approved and bookmarked!
reacted to daavoo's post with 👍 about 5 hours ago
After working on agent evaluation🔍🤖 the last weeks, we started to accumulate code to make trying different agent frameworks easier. From that code, we have built and just released a small library called `any-agent`. Give it a try and a ⭐: https://github.com/mozilla-ai/any-agent ```py from any_agent import AgentConfig, AgentFramework, AnyAgent agent = AnyAgent.create( framework=AgentFramework("smolagents"), # or openai, langchain, llama_index main_agent=AgentConfig( model_id="gpt-4o-mini" ) ) agent.run("Which Agent Framework is the best??") ```
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John6666's activity

reacted to DawnC's post with ❤️ about 5 hours ago
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383
New in PawMatchAI🐾 : Turn Your Dog Photos into Art!

I’m excited to introduce a brand-new creative feature — Dog Style Transfer is now live on PawMatchAI!

Just upload your dog’s photo and transform it into 5 artistic styles:
🌸 Japanese Anime
📚 Classic Cartoon
🖼️ Oil Painting
🎨 Watercolor
🌆 Cyberpunk

All powered by Stable Diffusion and enhanced with smart prompt tuning to preserve your dog’s unique traits and breed identity , so the artwork stays true to your furry friend.

Whether you're creating a custom portrait or just having fun, this feature brings your pet photos to life in completely new ways.

And here’s a little secret: although it’s designed with dogs in mind, it actually works on any photo — cats, plush toys, even humans. Feel free to experiment!

Results may not always be perfectly accurate, sometimes your photo might come back looking a little different, or even beyond your imagination. But that’s part of the fun! It’s all about creative surprises and letting the AI do its thing.

Try it now: DawnC/PawMatchAI

If this new feature made you smile, a ❤️ for this space would mean a lot.

#AIArt #StyleTransfer #StableDiffusion #ComputerVision #MachineLearning #DeepLearning
reacted to severo's post with 👀 about 5 hours ago
reacted to daavoo's post with 👍 about 5 hours ago
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156
After working on agent evaluation🔍🤖 the last weeks, we started to accumulate code to make trying different agent frameworks easier. From that code, we have built and just released a small library called any-agent.


Give it a try and a ⭐: https://github.com/mozilla-ai/any-agent

from any_agent import AgentConfig, AgentFramework, AnyAgent

agent = AnyAgent.create(
    framework=AgentFramework("smolagents"),  # or openai, langchain, llama_index
    main_agent=AgentConfig(
        model_id="gpt-4o-mini"
    )
)
agent.run("Which Agent Framework is the best??")
reacted to fcakyon's post with 🔥 about 5 hours ago
reacted to DawnC's post with 🔥 about 6 hours ago
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383
New in PawMatchAI🐾 : Turn Your Dog Photos into Art!

I’m excited to introduce a brand-new creative feature — Dog Style Transfer is now live on PawMatchAI!

Just upload your dog’s photo and transform it into 5 artistic styles:
🌸 Japanese Anime
📚 Classic Cartoon
🖼️ Oil Painting
🎨 Watercolor
🌆 Cyberpunk

All powered by Stable Diffusion and enhanced with smart prompt tuning to preserve your dog’s unique traits and breed identity , so the artwork stays true to your furry friend.

Whether you're creating a custom portrait or just having fun, this feature brings your pet photos to life in completely new ways.

And here’s a little secret: although it’s designed with dogs in mind, it actually works on any photo — cats, plush toys, even humans. Feel free to experiment!

Results may not always be perfectly accurate, sometimes your photo might come back looking a little different, or even beyond your imagination. But that’s part of the fun! It’s all about creative surprises and letting the AI do its thing.

Try it now: DawnC/PawMatchAI

If this new feature made you smile, a ❤️ for this space would mean a lot.

#AIArt #StyleTransfer #StableDiffusion #ComputerVision #MachineLearning #DeepLearning
reacted to giux78's post with 👀 about 10 hours ago
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733
LLAMA4 release highlight the importance of political and social bias. According to their own evaluation described in the release blog post:
- Refusals on contentious prompts dropped from 7% (hashtag#LLAMA 3.3) to under 2%
- Unequal response refusals are now under 1%
- Political lean bias is said to be halved compared to hashtag#LLaMA 3.3 and comparable to Grok

However, we @efederici @mferraretto @FinancialSupport and I released some weeks ago an independent open source benchmark called Propaganda to measure political bias in LLMs: https://github.com/mii-llm/propaganda

In the chart below, we evaluated multiple leading models on the basis of ratings across a range of prompts designed to expose ideological leanings.

Despite Meta’s stated neutrality goals, LLAMA4 ranks at the very top in terms of total ratings aligned with a clear ideological bias. The models were tested on their ability to respond even-handedly to politically sensitive prompts. LLaMA 4 scored even higher than models known for strong alignment policies like GPT-4o.

LLMs may be refusing less, but they still show bias through content framing. This suggests that refusal rates alone are not a sufficient measure of ideological bias. Relying solely on internal evaluations from AI labs also raises concerns about transparency and objectivity.
reacted to AdinaY's post with 👍 about 10 hours ago
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730
IndexTTS 📢 a TTS built on XTTS + Tortoise, released by BiliBili - a Chinese video sharing platform/community.
Model: IndexTeam/Index-TTS
Demo: IndexTeam/IndexTTS

✨Chinese pronunciation correction via pinyin
✨Pause control via punctuation
✨Improved speaker conditioning & audio quality (BigVGAN2)
✨Trained on 10k+ hours
reacted to AdinaY's post with 👍 about 10 hours ago
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392
MAYE🎈a from-scratch RL framework for Vision Language Models, released by GAIR - an active research group from the Chinese community.

✨Minimal & transparent pipeline with standard tools
✨Standardized eval to track training & reflection
✨Open Code & Dataset

Code:
https://github.com/GAIR-NLP/MAYE?tab=readme-ov-file
Dataset:
ManTle/MAYE
Paper:
Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation Scheme (2504.02587)
reacted to danielhanchen's post with 🤗🔥 about 10 hours ago
reacted to onekq's post with 🚀 about 10 hours ago
reacted to csabakecskemeti's post with 😎 about 10 hours ago
reacted to luigi12345's post with 👍 about 10 hours ago
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1560
🚀 Meta’s Llama 4 Models Now on Hugging Face!

Meta has released Llama 4 Scout and Llama 4 Maverick, now available on Hugging Face:
• Llama 4 Scout: 17B active parameters, 16-expert Mixture of Experts (MoE) architecture, 10M token context window, fits on a single H100 GPU. 
• Llama 4 Maverick: 17B active parameters, 128-expert MoE architecture, 1M token context window, optimized for DGX H100 systems. 

🔥 Key Features:
• Native Multimodality: Seamlessly processes text and images. 
• Extended Context Window: Up to 10 million tokens for handling extensive inputs.
• Multilingual Support: Trained on 200 languages, with fine-tuning support for 12, including Arabic, Spanish, and German. 

🛠️ Access and Integration:
• Model Checkpoints: Available under the meta-llama organization on the Hugging Face Hub.
• Transformers Compatibility: Fully supported in transformers v4.51.0 for easy loading and fine-tuning.
• Efficient Deployment: Supports tensor-parallelism and automatic device mapping.

These models offer developers enhanced capabilities for building sophisticated, multimodal AI applications. 
reacted to fdaudens's post with ❤️ about 10 hours ago
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1718
I read the 456-page AI Index report so you don't have to (kidding). The wild part? While AI gets ridiculously more accessible, the power gap is actually widening:

1️⃣ The democratization of AI capabilities is accelerating rapidly:
- The gap between open and closed models is basically closed: difference in benchmarks like MMLU and HumanEval shrunk to just 1.7% in 2024
- The cost to run GPT-3.5-level performance dropped 280x in 2 years
- Model size is shrinking while maintaining performance - Phi-3-mini hitting 60%+ MMLU at fraction of parameters of early models like PaLM

2️⃣ But we're seeing concerning divides deepening:
- Geographic: US private investment ($109B) dwarfs everyone else - 12x China's $9.3B
- Research concentration: US and China dominate highly-cited papers (50 and 34 respectively in 2023), while next closest is only 7
- Gender: Major gaps in AI skill penetration rates - US shows 2.39 vs 1.71 male/female ratio

The tech is getting more accessible but the benefits aren't being distributed evenly. Worth thinking about as these tools become more central to the economy.

Give it a read - fascinating portrait of where AI is heading! https://hai-production.s3.amazonaws.com/files/hai_ai_index_report_2025.pdf
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reacted to sequelbox's post with 👍 about 10 hours ago
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886
TITANIUM 2 Deepseek-R1 dataset is here! Open-source synthetic architecture and DevOps dataset: sequelbox/Titanium2-DeepSeek-R1

Esper 3 will be coming out soon for multiple base models, trained on Titanium, Raiden, and more :)

with my love,
allegra
reacted to jsulz's post with 🔥 about 10 hours ago
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1064
The Llama 4 release - meta-llama/llama-4-67f0c30d9fe03840bc9d0164 - was a big one for the xet-team with every model backed by the storage infrastructure of the future for the Hub.

It's been a wild few days, and especially 🤯 to see every tensor file with a Xet logo next to it instead of LFS.

The attached graph shows requests per second to our content-addressed store (CAS) right as the release went live.

yellow = GETs; dashed line = launch time.

You can definitely tell when the community started downloading 👀

h/t to @rajatarya for the graph, the entire Xet crew to bring us to this point, and special shoutout to Rajat, @port8080 , @brianronan , @seanses , and @znation who made sure the bytes kept flying all weekend ⚡️
reacted to BrigitteTousi's post with 🤗 about 10 hours ago
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1579
AI agents are transforming how we interact with technology, but how sustainable are they? 🌍

Design choices — like model size and structure — can massively impact energy use and cost. ⚡💰 The key takeaway: smaller, task-specific models can be far more efficient than large, general-purpose ones.

🔑 Open-source models offer greater transparency, allowing us to track energy consumption and make more informed decisions on deployment. 🌱 Open-source = more efficient, eco-friendly, and accountable AI.

Read our latest, led by @sasha with assists from myself + @yjernite 🤗
https://huggingface.co/blog/sasha/ai-agent-sustainability
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reacted to jjokah's post with 🔥 1 day ago
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1651
# Video Tokenization — for efficient AI video processing

Meet 𝐕𝐢𝐝𝐓𝐨𝐤, a new open-source video tokenization technique developed by Microsoft Research to address the computational challenges of processing large volumes of video data. The core problem VidTok tackles is the inefficiency caused by redundant information in raw video pixels.

VidTok converts complex video footage into compact, structured units called tokens, making it easier and more efficient for AI systems to analyze, understand, and generate video content.

Research Paper: https://arxiv.org/abs/2412.13061
VidTok Code: https://github.com/microsoft/VidTok
reacted to javelinsoam's post with 🔥 1 day ago
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1669
Requests Fail with 404 on HuggingFace Inference Due to X-Forwarded-Host Header

We’re encountering a 404 Not Found error from the HuggingFace Inference endpoint when the request includes the X-Forwarded-Host header.

The issue appears to stem from the presence of this header, even if we use any private/public domain:

X-Forwarded-Host: google.com


Without Header – Works

When this header is removed, the request succeeds.
Identical payloads and endpoints return valid responses when the header is omitted.

With Header – Fails

If included (even with a valid public domain), the request fails with:

{
  "error": "Not Found: google.com"
}



You can use curl command to replicate this issue
curl "https://{your-inference-endpoint}/v1/chat/completions" \
-X POST \
-H "Authorization: Bearer <HF_TOKEN>" \
-H "Content-Type: application/json" \
-H "X-Forwarded-Host: any-domain.com" \
-d '{
  "model": "unsloth/DeepSeek-R1-GGUF",
  "messages": [{"role": "user", "content": "What is deep learning?"}],
  "max_tokens": 150
}'


Please let us know if there’s a workaround or config option available to suppress this behavior.
reacted to as-cle-bert's post with 🔥 1 day ago
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2190
Llama-4 is out and I couldn't resist but to cook something with it... So I came up with 𝐋𝐥𝐚𝐦𝐚𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐫 (https://llamaresearcher.com), your deep-research AI companion!🔎

The workflow behind 𝗟𝗹𝗮𝗺𝗮𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿 is simple:
💬 You submit a query
🛡️ Your query is evaluated by Llama 3 guard model, which deems it safe or unsafe
🧠 If your query is safe, it is routed to the Researcher Agent
⚙️ The Researcher Agent expands the query into three sub-queries, with which to search the web
🌐 The web is searched for each of the sub-queries
📊 The retrieved information is evaluated for relevancy against your original query
✍️ The Researcher Agent produces an essay based on the information it gathered, paying attention to referencing its sources

The agent itself is also built with easy-to-use and intuitive blocks:
🦙 LlamaIndex provides the agentic architecture and the integrations with the language models
⚡Groq makes Llama-4 available with its lightning-fast inference
🔎 Linkup allows the agent to deep-search the web and provides sourced answers
💪 FastAPI does the heavy loading with wrapping everything within an elegant API interface
⏱️ Redis is used for API rate limiting
🎨 Gradio creates a simple but powerful user interface

Special mention also to Lovable, which helped me build the first draft of the landing page for LlamaResearcher!💖

If you're curious and you want to try LlamaResearcher, you can - completely for free and without subscription - for 30 days from now ➡️ https://llamaresearcher.com
And if you're like me, and you like getting your hands in code and build stuff on your own machine, I have good news: this is all open-source, fully reproducible locally and Docker-ready🐋
Just go to the GitHub repo: https://github.com/AstraBert/llama-4-researcher and don't forget to star it, if you find it useful!⭐

As always, have fun and feel free to leave your feedback✨