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Tiny Agents: a MCP-powered agent in 50 lines of code
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Brigitte Tousignant
BrigitteTousi
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Tiny Agents: a MCP-powered agent in 50 lines of code
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Jaward's
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5 days ago
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2207
New reasoning algo just dropped: Adaptive Parallel Reasoning
“we propose Adaptive Parallel Reasoning (APR), a novel reasoning framework that enables language models to orchestrate both serialized and parallel computations end-to-end. APR generalizes existing reasoning methods by enabling adaptive multi-threaded inference using spawn() and join() operations.”
Paper: https://arxiv.org/pdf/2504.15466
Code: https://github.com/Parallel-Reasoning/APR
“we propose Adaptive Parallel Reasoning (APR), a novel reasoning framework that enables language models to orchestrate both serialized and parallel computations end-to-end. APR generalizes existing reasoning methods by enabling adaptive multi-threaded inference using spawn() and join() operations.”
Paper: https://arxiv.org/pdf/2504.15466
Code: https://github.com/Parallel-Reasoning/APR
reacted to
hannayukhymenko's
post with 🔥
5 days ago
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3308
🚀 We are delighted to announce MamayLM, a new state-of-the-art efficient Ukrainian LLM!
📈 MamayLM surpasses similar-sized models in both English and Ukrainian, while matching or overtaking up to 10x larger models.
📊 MamayLM is a 9B model that can run on a single GPU, enabling cost-efficient AI autonomy and adoption across sectors in Ukraine such as education, legal, healthcare, public services and others (e.g., by specializing it to particular use cases). MalayLM is also attractive for organizations wishing to preserve data privacy as it s efficiency allows it to run on a local machine.
🧠 MamayLM is trained on high-quality Ukrainian data and understands Ukrainian language, culture, and history. It is built on top of Google’s Gemma 2 9B model, but uses a number of new advances stemming from INSAIT’s experience in creating BgGPT, a Bulgarian LLM we released last year, now adopted nationwide and profiled several times by Google as a worldwide success case.
🤝 MamayLM is developed in a collaboration between researchers at INSAIT and ETH Zürich and is trained entirely via donations to INSAIT for AI compute resources.
📥 MamayLM is now freely available to download on INSAIT’s HuggingFace in both full and quantized versions. We also publicly release all Ukrainian benchmarks we evaluated on.
📝 Further, we release blog posts in both English and Ukrainian, sharing our approach to creating MamayLM, hoping to drive further improvements by the community.
🌎 The release of LLMs for various languages is part of INSAIT’s mission in ensuring countries can achieve AI autonomy in a cost-efficient, controlled, safe and predictable manner.
MamayLM model and benchmarks:
INSAIT-Institute
Blog (EN): https://huggingface.co/blog/INSAIT-Institute/mamaylm
Blog (UKR): https://huggingface.co/blog/INSAIT-Institute/mamaylm-ukr
📈 MamayLM surpasses similar-sized models in both English and Ukrainian, while matching or overtaking up to 10x larger models.
📊 MamayLM is a 9B model that can run on a single GPU, enabling cost-efficient AI autonomy and adoption across sectors in Ukraine such as education, legal, healthcare, public services and others (e.g., by specializing it to particular use cases). MalayLM is also attractive for organizations wishing to preserve data privacy as it s efficiency allows it to run on a local machine.
🧠 MamayLM is trained on high-quality Ukrainian data and understands Ukrainian language, culture, and history. It is built on top of Google’s Gemma 2 9B model, but uses a number of new advances stemming from INSAIT’s experience in creating BgGPT, a Bulgarian LLM we released last year, now adopted nationwide and profiled several times by Google as a worldwide success case.
🤝 MamayLM is developed in a collaboration between researchers at INSAIT and ETH Zürich and is trained entirely via donations to INSAIT for AI compute resources.
📥 MamayLM is now freely available to download on INSAIT’s HuggingFace in both full and quantized versions. We also publicly release all Ukrainian benchmarks we evaluated on.
📝 Further, we release blog posts in both English and Ukrainian, sharing our approach to creating MamayLM, hoping to drive further improvements by the community.
🌎 The release of LLMs for various languages is part of INSAIT’s mission in ensuring countries can achieve AI autonomy in a cost-efficient, controlled, safe and predictable manner.
MamayLM model and benchmarks:
INSAIT-Institute
Blog (EN): https://huggingface.co/blog/INSAIT-Institute/mamaylm
Blog (UKR): https://huggingface.co/blog/INSAIT-Institute/mamaylm-ukr
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reacted to
davanstrien's
post with 🔥❤️
5 days ago
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1804
Came across a very nice submission from
@marcodsn
for the reasoning datasets competition (https://huggingface.co/blog/bespokelabs/reasoning-datasets-competition).
The dataset distils reasoning chains from arXiv research papers in biology and economics. Some nice features of the dataset:
- Extracts both the logical structure AND researcher intuition from academic papers
- Adopts the persona of researchers "before experiments" to capture exploratory thinking
- Provides multi-short and single-long reasoning formats with token budgets - Shows 7.2% improvement on MMLU-Pro Economics when fine-tuning a 3B model
It's created using the Curator framework with plans to scale across more scientific domains and incorporate multi-modal reasoning with charts and mathematics.
I personally am very excited about datasets like this, which involve creativity in their creation and don't just rely on $$$ to produce a big dataset with little novelty.
Dataset can be found here: marcodsn/academic-chains (give it a like!)
The dataset distils reasoning chains from arXiv research papers in biology and economics. Some nice features of the dataset:
- Extracts both the logical structure AND researcher intuition from academic papers
- Adopts the persona of researchers "before experiments" to capture exploratory thinking
- Provides multi-short and single-long reasoning formats with token budgets - Shows 7.2% improvement on MMLU-Pro Economics when fine-tuning a 3B model
It's created using the Curator framework with plans to scale across more scientific domains and incorporate multi-modal reasoning with charts and mathematics.
I personally am very excited about datasets like this, which involve creativity in their creation and don't just rely on $$$ to produce a big dataset with little novelty.
Dataset can be found here: marcodsn/academic-chains (give it a like!)
reacted to
merve's
post with 🔥
5 days ago
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2745
New foundation model on image and video captioning just dropped by NVIDIA AI 🔥
Describe Anything Model (DAM) is a 3B vision language model to generate detailed captions with localized references 😮
The team released the models, the dataset, a new benchmark and a demo 🤩 nvidia/describe-anything-680825bb8f5e41ff0785834c
Most of the vision LMs focus on image as a whole, lacking localized references in captions, and not taking in visual prompts (points, boxes, drawings around objects)
DAM addresses this on two levels: new vision backbone that takes in focal crops and the image itself, and a large scale dataset 👀
They generate a dataset by extending existing segmentation and referring expression generation datasets like REFCOCO, by passing in the images and classes to VLMs and generating captions.
Lastly, they also release a new benchmark again with self-supervision, they use an LLM to evaluate the detailed captions focusing on localization 👏
Describe Anything Model (DAM) is a 3B vision language model to generate detailed captions with localized references 😮
The team released the models, the dataset, a new benchmark and a demo 🤩 nvidia/describe-anything-680825bb8f5e41ff0785834c
Most of the vision LMs focus on image as a whole, lacking localized references in captions, and not taking in visual prompts (points, boxes, drawings around objects)
DAM addresses this on two levels: new vision backbone that takes in focal crops and the image itself, and a large scale dataset 👀
They generate a dataset by extending existing segmentation and referring expression generation datasets like REFCOCO, by passing in the images and classes to VLMs and generating captions.
Lastly, they also release a new benchmark again with self-supervision, they use an LLM to evaluate the detailed captions focusing on localization 👏
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davidberenstein1957's
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5 days ago
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2082
🔥 Announcing FLUX-Juiced: The Fastest Image Generation Endpoint (2.6x faster)!
Optimisations are widely applied and can reduce inference time, but their impact on quality often remains unclear, so we decided to challenge the status quo and create our own optimised version of FLUX.1[dev] called FLUX-juiced.
Blog: https://huggingface.co/blog/PrunaAI/flux-fastest-image-generation-endpoint
Optimisations are widely applied and can reduce inference time, but their impact on quality often remains unclear, so we decided to challenge the status quo and create our own optimised version of FLUX.1[dev] called FLUX-juiced.
Blog: https://huggingface.co/blog/PrunaAI/flux-fastest-image-generation-endpoint
reacted to
AdinaY's
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5 days ago
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2762
MAGI-1 🪄 the autoregressive diffusion video model, released by Sand AI
sand-ai/MAGI-1
✨ 24B with Apache 2.0
✨ Strong temporal consistency
✨ Benchmark-topping performance
sand-ai/MAGI-1
✨ 24B with Apache 2.0
✨ Strong temporal consistency
✨ Benchmark-topping performance
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reacted to
clem's
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5 days ago
Post
3763
Energy is a massive constraint for AI but do you even know what energy your chatGPT convos are using?
We're trying to change this by releasing ChatUI-energy, the first interface where you see in real-time what energy your AI conversations consume. Great work from @jdelavande powered by spaces & TGI, available for a dozen of open-source models like Llama, Mistral, Qwen, Gemma and more.
jdelavande/chat-ui-energy
Should all chat interfaces have this? Just like ingredients have to be shown on products you buy, we need more transparency in AI for users!
We're trying to change this by releasing ChatUI-energy, the first interface where you see in real-time what energy your AI conversations consume. Great work from @jdelavande powered by spaces & TGI, available for a dozen of open-source models like Llama, Mistral, Qwen, Gemma and more.
jdelavande/chat-ui-energy
Should all chat interfaces have this? Just like ingredients have to be shown on products you buy, we need more transparency in AI for users!
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meg's
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5 days ago
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2235
New launch: See the energy use of chatbot conversations, in real time. =)
jdelavande/chat-ui-energy
Great work from @JulienDelavande !
jdelavande/chat-ui-energy
Great work from @JulienDelavande !
reacted to
clem's
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6 days ago
Post
2823
Just crossed half a million public apps on Hugging Face. A new public app is created every minute these days 🤯🤯🤯
What's your favorite? http://hf.co/spaces
What's your favorite? http://hf.co/spaces
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6 days ago
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giadap's
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11 days ago
Post
1525
🤗 Just published: "Consent by Design" - exploring how we're building better consent mechanisms across the HF ecosystem!
Our research shows open AI development enables:
- Community-driven ethical standards
- Transparent accountability
- Context-specific implementations
- Privacy as core infrastructure
Check out our Space Privacy Analyzer tool that automatically generates privacy summaries of applications!
Effective consent isn't about perfect policies; it's about architectures that empower users while enabling innovation. 🚀
Read more: https://huggingface.co/blog/giadap/consent-by-design
Our research shows open AI development enables:
- Community-driven ethical standards
- Transparent accountability
- Context-specific implementations
- Privacy as core infrastructure
Check out our Space Privacy Analyzer tool that automatically generates privacy summaries of applications!
Effective consent isn't about perfect policies; it's about architectures that empower users while enabling innovation. 🚀
Read more: https://huggingface.co/blog/giadap/consent-by-design
reacted to
burtenshaw's
post with 🚀
11 days ago
Post
1770
Hacked my presentation building with inference providers, Cohere command a, and sheer simplicity. Use this script if you’re burning too much time on presentations:
🔗 https://github.com/burtenshaw/course_generator/blob/main/scripts/create_presentation.py
This is what it does:
- uses command a to generates slides and speaker notes based on some material.
- it renders the material in remark open format and imports all images, tables, etc
- you can then review the slides as markdown and iterate
- export to either pdf or pptx using backslide
🚀 Next steps are: add text to speech for the audio and generate a video. This should make Hugging Face educational content scale to a billion AI Learners.
🔗 https://github.com/burtenshaw/course_generator/blob/main/scripts/create_presentation.py
This is what it does:
- uses command a to generates slides and speaker notes based on some material.
- it renders the material in remark open format and imports all images, tables, etc
- you can then review the slides as markdown and iterate
- export to either pdf or pptx using backslide
🚀 Next steps are: add text to speech for the audio and generate a video. This should make Hugging Face educational content scale to a billion AI Learners.
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17 Reasons Why Gradio Isn't Just Another UI Library
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