A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality
Paper
โข
2507.07202
โข
Published
โข
22
AI & ML interests
Webhooks are now publicly available on Hugging Face!
taesiriย
authored
a
paper
about 1 month ago
Post
3859
YAML engineering becomes more and more important than ever from infra provisioning to model training (recipes).
Here, I built a simple editor first for @dstackai , and I will share the live endpoint this week. Let me know what you think about this approach.
Based on this approach, if people think this is useful, I am going to do the same thing for the LLM training recipes for popular frameworks such as Hugging Face open-r1, Axolotl, and so on. Let me hear.
Here, I built a simple editor first for @dstackai , and I will share the live endpoint this week. Let me know what you think about this approach.
Based on this approach, if people think this is useful, I am going to do the same thing for the LLM training recipes for popular frameworks such as Hugging Face open-r1, Axolotl, and so on. Let me hear.
Post
4680
Open Source Avengers, Assemble! Ask an expert AI agent team to solve complex problems together ๐ฅ
Consilium brings together multiple agents that debate and use live research (web, arXiv, SEC) to reach a consensus. You set the strategy, they find the answer.
Credit to @azettl for this awesome demo: Agents-MCP-Hackathon/consilium_mcp
Consilium brings together multiple agents that debate and use live research (web, arXiv, SEC) to reach a consensus. You set the strategy, they find the answer.
Credit to @azettl for this awesome demo: Agents-MCP-Hackathon/consilium_mcp
- 2 replies
lidiyaย
authored
a
paper
2 months ago
davanstrienย
posted
an
update
2 months ago
Post
3389
Inspired by Hugging Face's official MCP server, I've developed a complementary tool that exposes my semantic search API to enhance discovery across the HF platform.
Key capabilities:
- AI-powered semantic search for models and datasets
- Parameter count analysis via safetensors metadata
- Trending content discovery
- Find similar models/datasets functionality
- 11 tools total for enhanced ecosystem navigation
The semantic search goes beyond simple keyword matching, understanding context and relationships between different models and datasets.
Example query: "Find around 10 reasoning Hugging Face datasets published in 2025 focusing on topics other than maths and science. Show a link and a short summary for each dataset." (results in video!)
https://github.com/davanstrien/hub-semantic-search-mcp
Key capabilities:
- AI-powered semantic search for models and datasets
- Parameter count analysis via safetensors metadata
- Trending content discovery
- Find similar models/datasets functionality
- 11 tools total for enhanced ecosystem navigation
The semantic search goes beyond simple keyword matching, understanding context and relationships between different models and datasets.
Example query: "Find around 10 reasoning Hugging Face datasets published in 2025 focusing on topics other than maths and science. Show a link and a short summary for each dataset." (results in video!)
https://github.com/davanstrien/hub-semantic-search-mcp
- 1 reply
Post
6399
BOOOOM: Today I'm dropping TINY AGENTS
the 50 lines of code Agent in Javascript ๐ฅ
I spent the last few weeks working on this, so I hope you will like it.
I've been diving into MCP (Model Context Protocol) to understand what the hype was all about.
It is fairly simple, but still quite powerful: MCP is a standard API to expose sets of Tools that can be hooked to LLMs.
But while doing that, came my second realization:
Once you have a MCP Client, an Agent is literally just a while loop on top of it. ๐คฏ
โก๏ธ read it exclusively on the official HF blog: https://huggingface.co/blog/tiny-agents
the 50 lines of code Agent in Javascript ๐ฅ
I spent the last few weeks working on this, so I hope you will like it.
I've been diving into MCP (Model Context Protocol) to understand what the hype was all about.
It is fairly simple, but still quite powerful: MCP is a standard API to expose sets of Tools that can be hooked to LLMs.
But while doing that, came my second realization:
Once you have a MCP Client, an Agent is literally just a while loop on top of it. ๐คฏ
โก๏ธ read it exclusively on the official HF blog: https://huggingface.co/blog/tiny-agents
- 1 reply
Post
4986
davanstrienย
posted
an
update
4 months ago
Post
2304
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!)
davanstrienย
posted
an
update
4 months ago
Post
1725
I've created a v1 dataset (
davanstrien/reasoning-required) and model (
davanstrien/ModernBERT-based-Reasoning-Required) to help curate "wild text" data for generating reasoning examples beyond the usual code/math/science domains.
- I developed a "Reasoning Required" dataset with a 0-4 scoring system for reasoning complexity
- I used educational content from HuggingFaceFW/fineweb-edu, adding annotations for domains, reasoning types, and example questions
My approach enables a more efficient workflow: filter text with small models first, then use LLMs only on high-value content.
This significantly reduces computation costs while expanding reasoning dataset domain coverage.
- I developed a "Reasoning Required" dataset with a 0-4 scoring system for reasoning complexity
- I used educational content from HuggingFaceFW/fineweb-edu, adding annotations for domains, reasoning types, and example questions
My approach enables a more efficient workflow: filter text with small models first, then use LLMs only on high-value content.
This significantly reduces computation costs while expanding reasoning dataset domain coverage.
nouamanetaziย
authored
a
paper
4 months ago
osansevieroย
authored
a
paper
5 months ago
Post
3922
simple guide on the recipe for GRPO on Open-R1 which is built on top of TRL
I think FastAPI wrapper of vLLM with WeightSyncWorker is pretty cool feature. Also, we have many predefined reward functions out of the box!
I think FastAPI wrapper of vLLM with WeightSyncWorker is pretty cool feature. Also, we have many predefined reward functions out of the box!
- 5 replies
emreย
authored
a
paper
5 months ago
Post
3595
having trouble with auto train
hello there this is the first time i am testing auto train with a 1.8k SFT dataset. Howevery i am not quite sure the training is going smooth. Logs seem quite confusing, token did not match can not auth, generates confusing train splits, do you know how i can check my running job properly?
what is being used for training as data?
any ideas?
hello there this is the first time i am testing auto train with a 1.8k SFT dataset. Howevery i am not quite sure the training is going smooth. Logs seem quite confusing, token did not match can not auth, generates confusing train splits, do you know how i can check my running job properly?
what is being used for training as data?
any ideas?
- 1 reply
Post
2659
Mistral AI Small 3.1 24B is not only commercial free but also the best model in a single GPU deployment.
I packed up all the information you need to know in a single picture. Hope this helps! :)
I packed up all the information you need to know in a single picture. Hope this helps! :)
- 1 reply
Post
1592
Gemma 3 Release in a nutshell
(seems like function calling is not supported whereas the announcement said so)
(seems like function calling is not supported whereas the announcement said so)
Post
4096
Important notice ๐จ
For Inference Providers who have built support for our Billing API (currently: Fal, Novita, HF-Inference โ with more coming soon), we've started enabling Pay as you go (=PAYG)
What this means is that you can use those Inference Providers beyond the free included credits, and they're charged to your HF account.
You can see it on this view: any provider that does not have a "Billing disabled" badge, is PAYG-compatible.
For Inference Providers who have built support for our Billing API (currently: Fal, Novita, HF-Inference โ with more coming soon), we've started enabling Pay as you go (=PAYG)
What this means is that you can use those Inference Providers beyond the free included credits, and they're charged to your HF account.
You can see it on this view: any provider that does not have a "Billing disabled" badge, is PAYG-compatible.
- 9 replies
taesiriย
authored
a
paper
5 months ago
davanstrienย
posted
an
update
6 months ago
Post
2970
๐ Introducing "Hugging Face Dataset Spotlight" ๐
I'm excited to share the first episode of our AI-generated podcast series focusing on nice datasets from the Hugging Face Hub!
This first episode explores mathematical reasoning datasets:
- SynthLabsAI/Big-Math-RL-Verified: Over 250,000 rigorously verified problems spanning multiple difficulty levels and mathematical domains
- open-r1/OpenR1-Math-220k: 220,000 math problems with multiple reasoning traces, verified for accuracy using Math Verify and Llama-3.3-70B models.
- facebook/natural_reasoning: 1.1 million general reasoning questions carefully deduplicated and decontaminated from existing benchmarks, showing superior scaling effects when training models like Llama3.1-8B-Instruct.
Plus a bonus segment on bespokelabs/bespoke-manim!
https://www.youtube.com/watch?v=-TgmRq45tW4
I'm excited to share the first episode of our AI-generated podcast series focusing on nice datasets from the Hugging Face Hub!
This first episode explores mathematical reasoning datasets:
- SynthLabsAI/Big-Math-RL-Verified: Over 250,000 rigorously verified problems spanning multiple difficulty levels and mathematical domains
- open-r1/OpenR1-Math-220k: 220,000 math problems with multiple reasoning traces, verified for accuracy using Math Verify and Llama-3.3-70B models.
- facebook/natural_reasoning: 1.1 million general reasoning questions carefully deduplicated and decontaminated from existing benchmarks, showing superior scaling effects when training models like Llama3.1-8B-Instruct.
Plus a bonus segment on bespokelabs/bespoke-manim!
https://www.youtube.com/watch?v=-TgmRq45tW4
davanstrienย
posted
an
update
6 months ago
Post
3715
Quick POC: Turn a Hugging Face dataset card into a short podcast introducing the dataset using all open models.
I think I'm the only weirdo who would enjoy listening to something like this though ๐
Here is an example for eth-nlped/stepverify
I think I'm the only weirdo who would enjoy listening to something like this though ๐
Here is an example for eth-nlped/stepverify
- 2 replies