c-and-a (C&A)

m-ric

posted an update 1 day ago

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2518

Since I published it on GitHub a few days ago,
Hugging Face's new agentic library 𝘀𝗺𝗼𝗹𝗮𝗴𝗲𝗻𝘁𝘀 has gathered nearly 4k stars 🤯

➡️ 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.

We will make it work better, and fully open. ✨

Sounds like something you'd like to do? Apply here 👉 https://apply.workable.com/huggingface/j/AF1D4E3FEB/

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MoritzLaurer

posted an update 1 day ago

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OpenAI is losing money on the $200/month subscription 🤯. 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. 💸🔥
- 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.

Source with the numbers 👇
https://techcrunch.com/2025/01/05/openai-is-losing-money-on-its-pricey-chatgpt-pro-plan-ceo-sam-altman-says/

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MoritzLaurer

posted an update 2 days ago

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2066

🚀 Releasing a new zeroshot-classifier based on ModernBERT! Some key takeaways:

- ⚡ 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
- 📉 Performance tradeoff: It performs slightly worse than DeBERTav3 on average across my zeroshot classification task collection
- 🧠 Use cases: I recommend using it for scenarios requiring speed and a larger context window (8k).
- 💡 What’s next? I’m 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.

Great work by https://huggingface.co/answerdotai !

If you’re looking for a high-speed zeroshot classifier, give it a try!

📄 Resources below: 👇
Base model: MoritzLaurer/ModernBERT-base-zeroshot-v2.0
Large model: MoritzLaurer/ModernBERT-large-zeroshot-v2.0
Updated zeroshot collection: MoritzLaurer/zeroshot-classifiers-6548b4ff407bb19ff5c3ad6f
ModernBERT collection with paper: answerdotai/modernbert-67627ad707a4acbf33c41deb

MoritzLaurer

posted an update 19 days ago

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2592

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

Congrats @answerdotai , @LightOnIO and collaborators like @tomaarsen !

Paper and models here 👇https://huggingface.co/collections/answerdotai/modernbert-67627ad707a4acbf33c41deb

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m-ric

posted an update 20 days ago

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2126

After 6 years, BERT, the workhorse of encoder models, finally gets a replacement: 𝗪𝗲𝗹𝗰𝗼𝗺𝗲 𝗠𝗼𝗱𝗲𝗿𝗻𝗕𝗘𝗥𝗧! 🤗

We talk a lot about ✨Generative AI✨, meaning "Decoder version of the Transformers architecture", but this is only one of the ways to build LLMs: encoder models, that turn a sentence in a vector, are maybe even more widely used in industry than generative models.

The workhorse for this category has been BERT since its release in 2018 (that's prehistory for LLMs).

It's not a fancy 100B parameters supermodel (just a few hundred millions), but it's an excellent workhorse, kind of a Honda Civic for LLMs.

Many applications use BERT-family models - the top models in this category cumulate millions of downloads on the Hub.

➡️ Now a collaboration between Answer.AI and LightOn just introduced BERT's replacement: ModernBERT.

𝗧𝗟;𝗗𝗥:
🏛️ Architecture changes:
⇒ First, standard modernizations:
- Rotary positional embeddings (RoPE)
- Replace GeLU with GeGLU,
- Use Flash Attention 2
✨ The team also introduced innovative techniques like alternating attention instead of full attention, and sequence packing to get rid of padding overhead.

🥇 As a result, the model tops the game of encoder models:
It beats previous standard DeBERTaV3 for 1/5th the memory footprint, and runs 4x faster!

Read the blog post 👉 https://huggingface.co/blog/modernbert

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m-ric

posted an update 21 days ago

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𝐇𝐮𝐠𝐠𝐢𝐧𝐠 𝐅𝐚𝐜𝐞 𝐫𝐞𝐥𝐞𝐚𝐬𝐞𝐬 𝐏𝐢𝐜𝐨𝐭𝐫𝐨𝐧, 𝐚 𝐦𝐢𝐜𝐫𝐨𝐬𝐜𝐨𝐩𝐢𝐜 𝐥𝐢𝐛 𝐭𝐡𝐚𝐭 𝐬𝐨𝐥𝐯𝐞𝐬 𝐋𝐋𝐌 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝟒𝐃 𝐩𝐚𝐫𝐚𝐥𝐥𝐞𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 🥳

🕰️ Llama-3.1-405B took 39 million GPU-hours to train, i.e. about 4.5 thousand years.

👴🏻 If they had needed all this time, we would have GPU stories from the time of Pharaoh 𓂀: "Alas, Lord of Two Lands, the shipment of counting-stones arriving from Cathay was lost to pirates, this shall delay the building of your computing temple by many moons "

🛠️ But instead, they just parallelized the training on 24k H100s, which made it take just a few months.
This required parallelizing across 4 dimensions: data, tensor, context, pipeline.
And it is infamously hard to do, making for bloated code repos that hold together only by magic.

🤏 𝗕𝘂𝘁 𝗻𝗼𝘄 𝘄𝗲 𝗱𝗼𝗻'𝘁 𝗻𝗲𝗲𝗱 𝗵𝘂𝗴𝗲 𝗿𝗲𝗽𝗼𝘀 𝗮𝗻𝘆𝗺𝗼𝗿𝗲! Instead of building mega-training codes, Hugging Face colleagues cooked in the other direction, towards tiny 4D parallelism libs. A team has built Nanotron, already widely used in industry.
And now a team releases Picotron, a radical approach to code 4D Parallelism in just a few hundred lines of code, a real engineering prowess, making it much easier to understand what's actually happening!

⚡ 𝗜𝘁'𝘀 𝘁𝗶𝗻𝘆, 𝘆𝗲𝘁 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹:
Counting in MFU (Model FLOPs Utilization, how much the model actually uses all the compute potential), this lib reaches ~50% on SmolLM-1.7B model with 8 H100 GPUs, which is really close to what huge libs would reach. (Caution: the team is leading further benchmarks to verify this)

Go take a look 👉 https://github.com/huggingface/picotron/tree/main/picotron

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MoritzLaurer

posted an update 22 days ago

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"Open-source AI: year in review 2024": amazing Space with lots of data-driven insights into AI in 2024! Check it out 👇
huggingface/open-source-ai-year-in-review-2024

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m-ric

posted an update 26 days ago

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2189

𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗽𝗮𝗿𝗮𝗱𝗶𝗴𝗺 𝘀𝗵𝗶𝗳𝘁 𝗶𝗻 𝗟𝗟𝗠𝘀: 𝗻𝗲𝘄 𝗽𝗮𝗽𝗲𝗿 𝗯𝘆 𝗠𝗲𝘁𝗮 𝗰𝗹𝗮𝗶𝗺𝘀 𝘁𝗵𝗮𝘁 𝘄𝗲 𝗰𝗮𝗻 𝗴𝗲𝘁 𝗿𝗶𝗱 𝗼𝗳 𝘁𝗼𝗸𝗲𝗻𝗶𝘇𝗲𝗿𝘀! 🥳

Current LLMs process text by first splitting it into tokens. They use a module named "tokenizer", that -spl-it-s- th-e- te-xt- in-to- arbitrary tokens depending on a fixed dictionnary.
On the Hub you can find this dictionary in a model's files under tokenizer.json.

➡️ This process is called BPE tokenization. It is suboptimal, everyone says it. It breaks text into predefined chunks that often fail to capture the nuance of language. But it has been a necessary evil in language models since their inception.

💥 In Byte Latent Transformer (BLT), Meta researchers propose an elegant solution by eliminating tokenization entirely, working directly with raw bytes while maintaining efficiency through dynamic "patches."

This had been tried before with different byte-level tokenizations, but it's the first time that an architecture of this type scales as well as BPE tokenization. And it could mean a real paradigm shift! 👏👏

🏗️ 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲:
Instead of a lightweight tokenizer, BLT has a lightweight encoder that process raw bytes into patches. Then the patches are processed by the main heavy-duty transformers as we do normally (but for patches of bytes instead of tokens), before converting back to bytes.

🧩 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗣𝗮𝘁𝗰𝗵𝗶𝗻𝗴:
Instead of fixed tokens, BLT groups bytes based on their predictability (measured by entropy) - using more compute for complex sequences and efficiently handling simple ones. This allows efficient processing while maintaining byte-level understanding.

I hope this breakthrough is confirmed and we can get rid of all the tokenizer stuff, it will make model handling easier!

Read their paper here 👉 https://dl.fbaipublicfiles.com/blt/BLT__Patches_Scale_Better_Than_Tokens.pdf

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MoritzLaurer

posted an update 27 days ago

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1284

I've been building a small library for working with prompt templates on the HF hub: pip install prompt-templates. Motivation:

The community currently shares prompt templates in a wide variety of formats: in datasets, in model cards, as strings in .py files, as .txt/.yaml/.json/.jinja2 files etc. This makes sharing and working with prompt templates unnecessarily complicated.

Prompt templates are currently the main hyperparameter that people tune when building complex LLM systems or agents. If we don't have a common standard for sharing them, we cannot systematically test and improve our systems. After comparing different community approaches, I think that working with modular .yaml or .json files is the best approach.

The prompt-templates library :
- proposes a standard for sharing prompts (entirely locally or on the HF hub)
- provides some utilities that are interoperable with the broader ecosystem

Try it:

# !pip install prompt-templates
from prompt_templates import PromptTemplateLoader 
prompt_template = PromptTemplateLoader.from_hub(repo_id="MoritzLaurer/closed_system_prompts", filename="claude-3-5-artifacts-leak-210624.yaml")

The library is in early stages, feedback is welcome!

More details in the docs: https://github.com/MoritzLaurer/prompt_templates/

1 reply

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m-ric

posted an update 28 days ago

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💥 𝗚𝗼𝗼𝗴𝗹𝗲 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝘀 𝗚𝗲𝗺𝗶𝗻𝗶 𝟮.𝟬, 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗮 𝗙𝗹𝗮𝘀𝗵 𝗺𝗼𝗱𝗲𝗹 𝘁𝗵𝗮𝘁 𝘀𝘁𝗲𝗮𝗺𝗿𝗼𝗹𝗹𝘀 𝗚𝗣𝗧-𝟰𝗼 𝗮𝗻𝗱 𝗖𝗹𝗮𝘂𝗱𝗲-𝟯.𝟲 𝗦𝗼𝗻𝗻𝗲𝘁! And they start a huge effort on agentic capabilities.

🚀 The performance improvements are crazy for such a fast model:
‣ Gemini 2.0 Flash outperforms the previous 1.5 Pro model at twice the speed
‣ Now supports both input AND output of images, video, audio and text
‣ Can natively use tools like Google Search and execute code

➡️ If the price is on par with previous Flash iteration ($0.30 / M tokens, to compare with GPT-4o's $1.25) the competition will have a big problem with this 4x cheaper model that gets better benchmarks 🤯

🤖 What about the agentic capabilities?

‣ Project Astra: A universal AI assistant that can use Google Search, Lens and Maps
‣ Project Mariner: A Chrome extension that can complete complex web tasks (83.5% success rate on WebVoyager benchmark, this is really impressive!)
‣ Jules: An AI coding agent that integrates with GitHub workflows

I'll be eagerly awaiting further news from Google!

Read their blogpost here 👉 https://blog.google/technology/google-deepmind/google-gemini-ai-update-december-2024/

m-ric

posted an update 29 days ago

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1806

𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐥𝐚𝐰𝐬 𝐚𝐫𝐞 𝐧𝐨𝐭 𝐝𝐞𝐚𝐝 𝐲𝐞𝐭! New blog post suggests Anthropic might have an extremely strong Opus-3.5 already available, but is not releasing it to keep their edge over the competition. 🧐

❓Since the release of Opus-3.5 has been delayed indefinitely, there have been lots of rumors and articles about LLMs plateauing. Scaling laws, the main powering factor of the LLM competence increase, could have stopped, according to these rumors, being the cause of this stalling of progress.

These rumors were quickly denied by many people at the leading LLM labs, including OpenAI and Anthropic. But these people would be expected to hype the future of LLMs even if scaling laws really plateaued, so the jury is still out.

🗞️ This new article by Semianalysis (generally a good source, specifically on hardware) provides a counter-rumor that I find more convincing:

➡️ Maybe scaling laws still work, Opus-3.5 is ready and as good as planned, but they just don't release it because the synthetic data it helps provide can bring cheaper/smaller models Claude and Haiku up in performance, without risking to leak this precious high-quality synthetic data to competitors.

Time will tell! I feel like we'll know more soon.

Read the article: https://semianalysis.com/2024/12/11/scaling-laws-o1-pro-architecture-reasoning-infrastructure-orion-and-claude-3-5-opus-failures/

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m-ric

posted an update about 1 month ago

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2230

Last week was crazy in OS AI, with important models and datasets releases every day.

Here are the most important ones I've pinned:

🌎 Cohere relased GLobal-MMLU, a multilingual version of MMLU, to evaluate AI models' world knowledge in many languages!

🦙 Meta released Llama-3.3-70B-Instruct, a 70B model that's on par with Llama-3.1-405B-Instruct, GPT-4o and Claude. Probably my new go-to for agentic workflows.

🔉 FishAudio released fish-speech-1.5, multilingual text to speech model

🎨 Microsoft Research released TRELLIS, an extremely impressive image-to-3D model, which you can try here: JeffreyXiang/TRELLIS

📚 Yesterday, Hugging Face release FineWeb 2, a new version that extends the previous FineWeb to over 1000 languages, including extended coverage in Russina, Mandarin, German, Japanese, Spanish, French, so a huge, high-quality dataset of > 3 trillion words! HuggingFaceFW/fineweb-2

Now let's go build to make this week as productive as last one!

m-ric

posted an update about 1 month ago

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1478

𝗦𝗵𝗼𝘄𝗨𝗜: 𝗮 𝘀𝗺𝗮𝗹𝗹 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗮𝗴𝗲𝗻𝘁 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻 𝗻𝗮𝘃𝗶𝗴𝗮𝘁𝗲 𝗮𝗻𝘆 𝗨𝗜 𝗮𝗻𝗱 𝗼𝘂𝘁𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝘀 𝗺𝘂𝗰𝗵 𝗯𝗶𝗴𝗴𝗲𝗿 𝘀𝘆𝘀𝘁𝗲𝗺𝘀! 📲

A team from NUS and Microsoft just released an agent that can act on any UI (Desktop, Android, Web) without needing additional text information. It works extremely well : they applied their method on a tiny Qwen2-VL-2B, and they managed to beat methods that use either much more powerful vision models (like GPT-4V) without using any additional info (e.g. leveraging the DOM of a webpage) like previous methods did ! 👏👏

They started from the idea that most existing methods rely heavily on text, which makes them less generalizable, while letting aside rich UI structure that user actually rely on when navigating this interfaces.

⚙️ They put several good ideas to work:

💡 Simplify screenshots to the max:
They prune a lot the heavy visual content of UI screenshots, by removing cloned image patches (like any vast patch of the same color will be reduced to a small patch, while maintaining positional embeddings), then group patches from the same GUI elements together to simplify even further

💡 Build a truly generalist dataset:
To train a general UI agent, you need trajectories from each possible UI, and express them in a common language. Authors merge datasets like OmniAct for Desktop, Mind2Web for websites, AMEX for Android trajectories to create a high-quality and diverse dataset.

➡️ Nice results ensued:
They fine-tune a tiny Qwen-2-VL-2B on their method, and it reaches SOTA on several task (element identification, web navigation), even beating methods that either use additional info from the DOM or use much bigger VLMS like GPT-4v! 🏆

And performance could certainly jump with a slightly bigger vision model. Let's hope the community builds this soon! 🚀

Paper added to my "Agents" collection 👉 m-ric/agents-65ba776fbd9e29f771c07d4e

m-ric

posted an update about 1 month ago

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1214

Need a measurement for traction of a GitHub repo, a more reliable one than Github star history? (which is a bit too hype-driven) 📈

➡️ I've made a Space to visualize PyPI downloads.

Try it here 👉 m-ric/package-download-history

1 reply

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m-ric

posted an update about 1 month ago

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1278

🤖 𝗔𝗱𝗼𝗯𝗲'𝘀 𝗰𝗼𝗱𝗲-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁 𝗿𝗲𝗮𝗰𝗵𝗲𝘀 𝘁𝗵𝗲 𝘁𝗼𝗽 𝗼𝗳 𝗚𝗔𝗜𝗔 𝗹𝗲𝗮𝗱𝗲𝗿𝗯𝗼𝗮𝗿𝗱 - and their paper cites my work!

💡 Reminder: In short, Agentic systems are a vehicle in which you put your LLM to allow it access to the outside world.

➡️ The team of researchers at Adobe started from the idea that current agentic systems lack the ability to define their own tools. So they decided to make an agent that writes actions as code, thus allowing it to write python functions that can be re-used later as tools!

Here's what the LLM generations can look like with the proper prompt:

Thought: I need to access the excel file using a different method.
Action:

def access_excel_file(file_path)
	... # rest of the code (the agent does writes it, but I don't have room in this post)
	return rows

Then your system executes this and appends the observation to the agent's memory.

Why is this code formulation better than classical tool use formulation as JSON? The paper explains:

"Most existing work uses text or JSON as the representation of actions, which significantly lacks the two criteria mentioned earlier: generality and composability. In contrast, DynaSaur can utilize available actions or create new ones if necessary, using code as a unified representation. In principle, acting with code enables agents to solve any Turing-complete problem."

The idea of using code is not new: in fact, we do it in transformers.agents (thus the citation that I got). They implementation adds further refinements, like using RAG to retrieve relevant functions before generating an action, which increases performance further.

And they observe that code agents perform much better, reaching the top of GAIA leaderboard! 🥇

Go take a look, it's really clear and informative!

Paper added to my agents collection 👉 m-ric/agents-65ba776fbd9e29f771c07d4e

m-ric

posted an update about 1 month ago

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2379

Single most important thing to do today: 𝗴𝗼 𝘁𝗿𝘆 𝗤𝘄𝗤 𝗼𝗻 𝗛𝘂𝗴𝗴𝗶𝗻𝗴 𝗖𝗵𝗮𝘁!

👉 https://huggingface.co/chat/models/Qwen/QwQ-32B-Preview

2 replies

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m-ric

posted an update about 1 month ago

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1081

🗞️ 𝗦𝘁𝗮𝘁𝗲 𝗼𝗳 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝟮𝟬𝟮𝟰: 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰 𝗲𝗮𝘁𝗶𝗻𝗴 𝘂𝗽 𝗢𝗽𝗲𝗻𝗔𝗜, 𝗔𝗴𝗲𝗻𝘁𝘀 𝗿𝗮𝗺𝗽 𝘂𝗽 𝘁𝗼 𝟭𝟮% 𝗼𝗳 𝘂𝘀𝗲-𝗰𝗮𝘀𝗲𝘀, 𝗼𝗽𝗲𝗻 𝗺𝗼𝗱𝗲𝗹𝘀 𝗺𝗮𝗸𝗲 𝟭𝟵% 𝗼𝗳 𝘂𝘀𝗮𝗴𝗲

Menlo Ventures surveyed 600 enterprise IT decision-makers for their 2024 report. They reveal that AI spending surged to $13.8 billion this year, more than 6x the $2.3 billion spent in 2023!

Companies are shifting from experimentation to serious implementation.

👷 Top enterprise use cases by adoption:
‣ Code copilots (51%)
- GitHub Copilot hit $300M revenue run rate
‣ Support chatbots (31%)
‣ RAG (28%)
‣ Data extraction/transformation (27%)
‣ Meeting summarization (25%)

📈 Market dynamics:
‣ OpenAI's enterprise share dropped from 50% to 34% 👎
‣ Anthropic doubled presence from 12% to 24% 🚀
‣ Open-source makes up 19% of usage 🤗

😬 Implementation challenges:
‣ 26% failed due to unexpected implementation costs
‣ 21% failed due to data privacy issues
‣ 18% failed due to disappointing ROI
‣ 15% failed due to hallucinations

Read the full report here 👉 https://menlovc.com/2024-the-state-of-generative-ai-in-the-enterprise/

m-ric

posted an update about 2 months ago

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1214

Made a new app to visualize the LLM race ⇒ 𝗡𝗼 𝗘𝘂𝗿𝗼𝗽𝗲𝗮𝗻 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗶𝗻 𝘁𝗵𝗲 𝘁𝗼𝗽 𝟭𝟬 🇪🇺❌

See the app here 👉 m-ric/llm-race-to-the-top

I've adapted an app by @andrewrreed that tracks progress of LLMs ( andrewrreed/closed-vs-open-arena-elo), on the Chatbot Arena leaderboard, to compare companies from different countries.

The outcome is quite sad, as a Frenchman and European.

The top 10 is exclusively US 🇺🇸 and Chinese 🇨🇳 companies (after great Chinese LLM releases recently, like the Qwen2.5 series), with the notable exception of Mistral AI 🇫🇷.

American companies are making fast progress, Chinese ones even faster. Europe is at risk of being left behind. And the EU AI Act hasn't even come into force yet to slow down the EU market. We need to wake up 😬

⚠️ Caution: This Chatbot Arena ELO ranking is not the most accurate, especially at high scores like this, because LLM makers can game it to some extent.

1 reply

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m-ric

posted an update about 2 months ago

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𝗡𝗲𝘄 𝗹𝗲𝗮𝗱𝗲𝗿𝗯𝗼𝗮𝗿𝗱 𝗿𝗮𝗻𝗸𝘀 𝗟𝗟𝗠𝘀 𝗳𝗼𝗿 𝗟𝗟𝗠-𝗮𝘀-𝗮-𝗷𝘂𝗱𝗴𝗲: 𝗟𝗹𝗮𝗺𝗮-𝟯.𝟭-𝟳𝟬𝗕 𝘁𝗼𝗽𝘀 𝘁𝗵𝗲 𝗿𝗮𝗻𝗸𝗶𝗻𝗴𝘀! 🧑‍⚖️

Evaluating systems is critical during prototyping and in production, and LLM-as-a-judge has become a standard technique to do it.

First, what is "LLM-as-a-judge"?
👉 It's a very useful technique for evaluating LLM outputs. If anything you're evaluating cannot be properly evaluated with deterministic criteria, like the "politeness" of an LLM output, or how faithful it is to an original source, you can use LLM-judge instead : prompt another LLM with "Here's an LLM output, please rate this on criterion {criterion} on a scale of 1 to 5", then parse the number from its output, and voilà, you get your score.

🧐 But who judges the judge?
How can you make sure your LLM-judge is reliable? You can have a specific dataset annotated with scores provided by human judges, and compare how LLM-judge scores correlate with human judge scores.

📊 Before even running that benchmark, to get you started, there's a new option to get you started: a leaderboard that measures how well different model perform as judges!

And the outcome is surprising, models come in quite different orders from what we're used to in general rankings: probably some have much better bias mitigation than others!

Take a deeper look here 👉 https://huggingface.co/blog/arena-atla

m-ric

posted an update about 2 months ago

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271

Lifehack of the day:
Adding "r.jina.ai/" before any url transforms it in Markdown using Jina AI's Reader! Here with @cyrilzakka 's blog post.

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