FACTS is a great paper from @GoogleDeepMind on measuring the factuality of LLM outputs. You can now download their prompt templates from @huggingface to improve LLM-based fact-checking yourself!
๐ The paper introduces the FACTS Grounding benchmark for evaluating the factuality of LLM outputs.
๐ค Fact-checking is automated by an ensemble of LLM judges that verify if a response is fully grounded in a factual reference document.
๐งช The authors tested different prompt templates on held-out data to ensure their generalization.
๐ It's highly educational to read these templates to learn how frontier labs design prompts and understand their limitations.
๐พ You can now download and reuse these prompt templates via the prompt-templates library!
๐ The library simplifies sharing prompt templates on the HF hub or locally via standardized YAML files. Letโs make LLM work more transparent and reproducible by sharing more templates like this!
Multimodal ๐ผ๏ธ > ByteDance released SA2VA: a family of vision LMs that can take image, video, text and visual prompts > moondream2 is out with new capabilities like outputting structured data and gaze detection! > Dataset: Alibaba DAMO lab released multimodal textbook โ 22k hours worth of samples from instruction videos ๐คฏ > Dataset: SciCap captioning on scientific documents benchmark dataset is released along with the challenge!
LLMs ๐ฌ > Microsoft released Phi-4, sota open-source 14B language model ๐ฅ > Dolphin is back with Dolphin 3.0 Llama 3.1 8B ๐ฌ๐ฌ > Prime-RL released Eurus-2-7B-PRIME a new language model trained using PRIME alignment > SmallThinker-3B is a new small reasoning LM based on Owen2.5-3B-Instruct ๐ญ > Dataset: QWQ-LONGCOT-500K is the dataset used to train SmallThinker, generated using QwQ-32B-preview ๐ > Dataset: @cfahlgren1 released React Code Instructions: a dataset of code instruction-code pairs ๐ > Dataset: Qwen team is on the roll, they just released CodeElo, a dataset of code preferences ๐ฉ๐ปโ๐ป
Embeddings ๐ > @MoritzLaurer released zero-shot version of ModernBERT large ๐ > KaLM is a new family of performant multilingual embedding models with MIT license built using Qwen2-0.5B
Image/Video Generation โฏ๏ธ > NVIDIA released Cosmos, a new family of diffusion/autoregressive World Foundation Models generating worlds from images, videos and texts ๐ฅ > Adobe released TransPixar: a new text-to-video model that can generate assets with transparent backgrounds (a first!) > Dataset: fal released cosmos-openvid-1m Cosmos-tokenized OpenVid-1M with samples from OpenVid-1M
Others > Prior Labs released TabPFNv2, the best tabular transformer is out for classification and regression > Metagene-1 is a new RNA language model that can be used for pathogen detection, zero-shot embedding and genome understanding
This week a few more languages have got 1,000 annotations for the educational quality of data from HuggingFaceFW/fineweb-2.
Why should you care?
The quality of pre-training data can have a big impact on the performance of downstream language models trained on that data (HuggingFaceFW/blogpost-fineweb-v1).
Being able to filter by educational quality is on way of improving the quality of the data you use for training an LLM. Very importantly this approach can also reduce the amount of data needed for pertaining.
Why not use an LLM?
LLMs can be used to annotate educational quality for a subset of data. This data can then be used to train a smaller encoder only model to label the full dataset. However, this may not work well for languages outside of english. This is where fineweb-c (community) comes in.
The community is annotating the educational quality of fineweb2 data. Currently 114 languages have some annotations. These annotations will enable a number of things:
- Evaluate whether an LLM can label the educational quality for texts in that language well - Directly be used for training quality classifiers - Help discover other rules and huerisitcs for refining fineweb2 further for different languages.
Community fine-tuned models are more carbon efficient than the models they are derived from! ๐ฅณ๐ฟ
@alozowski@clefourrier@SaylorTwift@albertvillanova evaluated COโ emissions associated with model inference for over 3000 models on the Open LLM Leaderboard. Interesting trends and new insights emerged...๐
The TRL v0.13 release is ๐ฅ! My highlight are the new process reward trainer to train models similar to o1 and tool call support:
๐ง Process reward trainer: Enables training of Process-supervised Reward Models (PRMs), which reward the quality of intermediate steps, promoting structured reasoning. Perfect for tasks like stepwise reasoning.
๐ Model merging: A new callback leverages mergekit to merge models during training, improving performance by blending reference and policy models - optionally pushing merged models to the Hugging Face Hub.
๐ ๏ธ Tool call support: TRL preprocessing now supports tool integration, laying the groundwork for agent fine-tuning with examples like dynamic temperature fetching in prompts.
โ๏ธ Mixture of judges: The new AllTrueJudge combines decisions from multiple binary judges for more nuanced evaluation.
> The models are capable of tasks involving vision-language understanding and visual referrals (referring segmentation) both for images and videos โฏ๏ธ
> The models come in 1B, 4B and 8B and are based on InternVL2.5 for base architecture and Qwen2, Qwen2.5 and InternLM2 for language model part (depending on the checkpoint)
> The model is very interesting, it has different encoders for different modalities each (visual prompt, text prompt, image and video) then it concatenates these to feed into LLM ๐ฌ
the output segmentation tokens are passed to SAM2, to sort of match text (captions or semantic classes) to masks โคต๏ธ
> Their annotation pipeline is also interesting, they seems to use two open large vision LMs to refine the annotations, and have different levels of descriptions to provide consistency.
๐ Supercharge your LLM apps with Langfuse on Hugging Face Spaces!
Langfuse brings end-to-end observability and tooling to accelerate your dev workflow from experiments through production
Now available as a Docker Space directly on the HF Hub! ๐ค
๐ Trace everything: monitor LLM calls, retrieval, and agent actions with popular frameworks 1โฃ One-click deployment: on Spaces with persistent storage and integrated OAuth ๐ Simple Prompt Management: Version, edit, and update without redeployment โ Intuitive Evals: Collect user feedback, run model/prompt evaluations, and improve quality ๐ Dataset Creation: Build datasets directly from production data to enhance future performance
Kudos to the Langfuse team for this collab and the awesome, open-first product theyโre building! ๐ @marcklingen@Clemo@MJannik
Since I published it on GitHub a few days ago, Hugging Face's new agentic library ๐๐บ๐ผ๐น๐ฎ๐ด๐ฒ๐ป๐๐ has gathered nearly 4k stars ๐คฏ
โก๏ธ But we are just getting started on agents: so we are hiring an ML Engineer to join me and double down on this effort!
The plan is to build GUI agents: agents that can act on your computer with mouse & keyboard, like Claude Computer Use.
OpenAI is losing money on the $200/month subscription ๐คฏ. It's crazy how expensive it is to run these largest LLMs:
- ChatGPT Pro costs $200/month ($2,400/year) and is still unprofitable for OpenAI due to higher-than-expected usage. - OpenAI reportedly expected losses of about $5 billion on revenue of $3.7 billion last year, with ChatGPT alone once costing an estimated $700,000 per day to operate. ๐ธ๐ฅ - They build strong models and do great research. Whether this business model will work in the long run is one of the biggest questions in the AI economy today.
Cosmos is a family of pre-trained models purpose-built for generating physics-aware videos and world states to advance physical AI development. The release includes Tokenizers nvidia/cosmos-tokenizer-672b93023add81b66a8ff8e6
๐ Releasing a new zeroshot-classifier based on ModernBERT! Some key takeaways:
- โก Speed & efficiency: It's multiple times faster and uses significantly less memory than DeBERTav3. You can use larger batch sizes and enabling bf16 (instead of fp16) gave me a ~2x speed boost as well - ๐ Performance tradeoff: It performs slightly worse than DeBERTav3 on average across my zeroshot classification task collection - ๐ง Use cases: I recommend using it for scenarios requiring speed and a larger context window (8k). - ๐ก Whatโs next? Iโm preparing a newer version trained on better + longer synthetic data to fully leverage the 8k context window and improve upon the training mix of my older zeroshot-v2.0 models. I also hope that there will be a multilingual variant in the future.
I was initially pretty sceptical about Meta's Coconut paper [1] because the largest perf gains were reported on toy linguistic problems. However, these results on machine translation are pretty impressive!
* Iteratively sample CoTs from the model, using a mix of different search strategies. This gives you something like Stream of Search via prompting. * Verify correctness of each CoT using GPT-4o (needed because exact match doesn't work well in medicine where there are lots of aliases) * Use GPT-4o to reformat the concatenated CoTs into a single stream that includes smooth transitions like "hmm, wait" etc that one sees in o1 * Use the resulting data for SFT & RL * Use sparse rewards from GPT-4o to guide RL training. They find RL gives an average ~3 point boost across medical benchmarks and SFT on this data already gives a strong improvement.
Applying this strategy to other domains could be quite promising, provided the training data can be formulated with verifiable problems!
Quite excited by the ModernBERT release! 0.15/0.4B small, 2T modern pre-training data and tokenizer with code, 8k context window, great efficient model for embeddings & classification!
This will probably be the basis for many future SOTA encoders! And I can finally stop using DeBERTav3 from 2021 :D
