giux78 (Alessandro Ercolani)

reacted to clem's post with 🤗 4 months ago

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1493

You can now bill your inference costs from all our inference partners (together, fireworks, fal, sambanova, cerebras, hyperbolic,...) to your Hugging Face organization.

Useful to drive more company-wide usage of AI without the billing headaches!

1 reply

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reacted to their post with 👀 4 months ago

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2321

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.

posted an update 4 months ago

Post

2321

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 tomaarsen's post with 🔥 4 months ago

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2698

‼️Sentence Transformers v4.0 is out! You can now train and finetune reranker models with multi-GPU training, bf16 support, loss logging, callbacks & much more. I also prove that finetuning on your domain helps much more than you might think.

1️⃣ Reranker Training Refactor
Reranker models can now be trained using an extensive trainer with a lot of powerful features:
- MultiGPU Training (Data Parallelism (DP) and Distributed Data Parallelism (DDP))
- bf16 training support; loss logging
- Evaluation datasets + evaluation loss
- Improved callback support + an excellent Weights & Biases integration
- Gradient checkpointing, gradient accumulation
- Model card generation
- Resuming from a training checkpoint without performance loss
- Hyperparameter Optimization
and much more!

Read my detailed blogpost to learn about the components that make up this new training approach: https://huggingface.co/blog/train-reranker
Notably, the release is fully backwards compatible: all deprecations are soft, meaning that they still work but emit a warning informing you how to upgrade.

2️⃣ New Reranker Losses
- 11 new losses:
- 2 traditional losses: BinaryCrossEntropy and CrossEntropy
- 2 distillation losses: MSE and MarginMSE
- 2 in-batch negatives losses: MNRL (a.k.a. InfoNCE) and CMNRL
- 5 learning to rank losses: Lambda, p-ListMLE, ListNet, RankNet, ListMLE

3️⃣ New Reranker Documentation
- New Training Overview, Loss Overview, API Reference docs
- 5 new, 1 refactored training examples docs pages
- 13 new, 6 refactored training scripts
- Migration guides (2.x -> 3.x, 3.x -> 4.x)

4️⃣ Blogpost
Alongside the release, I've written a blogpost where I finetune ModernBERT on a generic question-answer dataset. My finetunes easily outperform all general-purpose reranker models, even models 4x as big. Finetuning on your domain is definitely worth it: https://huggingface.co/blog/train-reranker

See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/v4.0.1

reacted to their post with 👀🤗 4 months ago

Post

3222

This is truly an inspirational story please help us spread the word, @clem , @thomwolf and everyone who supports open source AI.

A few weeks ago, @mmuffo94 and @cittiberto from indigo_ai launched the Chatbot Arena for the Italian language: https://indigo.ai/it/chatbot-arena-italia/.

To our surprise, among the top-ranked models is mii-llm/maestrale-chat-v0.4-beta a carefully fine-tuned version of mistralai/Mistral-7B-v0.1, developed by @efederici and @mferraretto from

mii-llm , and released nearly a year ago.

At this very moment, as shown in the screenshot, mii-llm/maestrale-chat-v0.4-beta is ranked 8th right between ChatGPT-4.5 and ChatGPT-4o.

It's likely that for several months, the best Italian speaking LLM has been an open source 7B model created by open source contributors and hardly anyone knew it.

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posted an update 4 months ago

Post

3222

This is truly an inspirational story please help us spread the word, @clem , @thomwolf and everyone who supports open source AI.

A few weeks ago, @mmuffo94 and @cittiberto from indigo_ai launched the Chatbot Arena for the Italian language: https://indigo.ai/it/chatbot-arena-italia/.

To our surprise, among the top-ranked models is mii-llm/maestrale-chat-v0.4-beta a carefully fine-tuned version of mistralai/Mistral-7B-v0.1, developed by @efederici and @mferraretto from

mii-llm , and released nearly a year ago.

At this very moment, as shown in the screenshot, mii-llm/maestrale-chat-v0.4-beta is ranked 8th right between ChatGPT-4.5 and ChatGPT-4o.

It's likely that for several months, the best Italian speaking LLM has been an open source 7B model created by open source contributors and hardly anyone knew it.

2 replies

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reacted to their post with ❤️ 4 months ago

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2898

@ mii-llm with @efederici @mferraretto @FinancialSupport and @DeepMount00 we just released #Propaganda a framework designed to evaluate and train LLMs on political opinions and bias. We aim to analyze both open-source and closed-source LLMs to understand the political positions and biases expressed in their outputs. Moreover we provide a set of recipes to enforce political positions into the models by creating ad hoc curated datasets and by applying fine tuning techniques. By releasing our work in the open, we hope to foster contributions: https://github.com/mii-llm/propaganda

This framework offers opportunities for expansion in various directions and could become the standard reference for evaluating LLMs on political topics, particularly those that influence public opinion.

posted an update 5 months ago

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2898

@ mii-llm with @efederici @mferraretto @FinancialSupport and @DeepMount00 we just released #Propaganda a framework designed to evaluate and train LLMs on political opinions and bias. We aim to analyze both open-source and closed-source LLMs to understand the political positions and biases expressed in their outputs. Moreover we provide a set of recipes to enforce political positions into the models by creating ad hoc curated datasets and by applying fine tuning techniques. By releasing our work in the open, we hope to foster contributions: https://github.com/mii-llm/propaganda

This framework offers opportunities for expansion in various directions and could become the standard reference for evaluating LLMs on political topics, particularly those that influence public opinion.

reacted to their post with 🚀 about 1 year ago

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1707

We https://mii-llm.ai just released a new LLM Italian benchmark and a set of evaluation: MMLU-PRO-ITA

Thanks to @efederici who released efederici/MMLU-Pro-ita a machine translated version of MMLU-PRO and thanks to a community shared computational effort we published in the "Eval Aggiuntive" tab of https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard the results on Italian open source LLMs.

If you want to deepen read the blog article on hf https://huggingface.co/blog/giux78/mmlu-pro-ita

posted an update about 1 year ago

Post

1707

We https://mii-llm.ai just released a new LLM Italian benchmark and a set of evaluation: MMLU-PRO-ITA

Thanks to @efederici who released efederici/MMLU-Pro-ita a machine translated version of MMLU-PRO and thanks to a community shared computational effort we published in the "Eval Aggiuntive" tab of https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard the results on Italian open source LLMs.

If you want to deepen read the blog article on hf https://huggingface.co/blog/giux78/mmlu-pro-ita

reacted to dvilasuero's post with ❤️🤗🚀🔥 about 1 year ago

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8328

Today is a huge day in Argilla’s history. We couldn’t be more excited to share this with the community: we’re joining Hugging Face!

We’re embracing a larger mission, becoming part of a brilliant and kind team and a shared vision about the future of AI.

Over the past year, we’ve been collaborating with Hugging Face on countless projects: launching partner of Docker Spaces, empowering the community to clean Alpaca translations into Spanish and other languages, launching argilla/notus-7b-v1 building on Zephyr’s learnings, the Data is Better Together initiative with hundreds of community contributors, or releasing argilla/OpenHermesPreferences, one of the largest open preference tuning datasets

After more than 2,000 Slack messages and over 60 people collaborating for over a year, it already felt like we were part of the same team, pushing in the same direction. After a week of the smoothest transition you can imagine, we’re now the same team.

To those of you who’ve been following us, this won’t be a huge surprise, but it will be a big deal in the coming months. This acquisition means we’ll double down on empowering the community to build and collaborate on high quality datasets, we’ll bring full support for multimodal datasets, and we’ll be in a better place to collaborate with the Open Source AI community. For enterprises, this means that the Enterprise Hub will unlock highly requested features like single sign-on and integration with Inference Endpoints.

As a founder, I am proud of the Argilla team. We're now part of something bigger and a larger team but with the same values, culture, and goals. Grateful to have shared this journey with my beloved co-founders Paco and Amélie.

Finally, huge thanks to the Chief Llama Officer @osanseviero for sparking this and being such a great partner during the acquisition process.

Would love to answer any questions you have so feel free to add them below!

28 replies

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reacted to their post with ❤️🚀 about 1 year ago

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1512

@FinancialSupport and I just released a new version of the Italian LLMs leaderboard https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard
using the super useful

demo-leaderboard template from @clefourrier .
We’ve evaluated over 50 models (base, merged, fine-tuned, etc.) from:
- Major companies like Meta, Mistral, Google ...
- University groups such as

sapienzanlp or

swap-uniba
- Italian Companies like MoxoffSpA ,

FairMind or

raicrits
- Various communities and individuals
All models were tested on #Italian benchmarks #mmlu #arc-c #hellaswag, which we contributed to the opensource lm-evaluation-harness library from

EleutherAI .
Plus, you can now submit your model for automatic evaluation, thanks to to

seeweb sponsored computation.
Curious about the top Italian models? Check out the leaderboard and submit your model!

https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard

posted an update about 1 year ago

Post

1512

@FinancialSupport and I just released a new version of the Italian LLMs leaderboard https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard
using the super useful

demo-leaderboard template from @clefourrier .
We’ve evaluated over 50 models (base, merged, fine-tuned, etc.) from:
- Major companies like Meta, Mistral, Google ...
- University groups such as

sapienzanlp or

swap-uniba
- Italian Companies like MoxoffSpA ,

FairMind or

raicrits
- Various communities and individuals
All models were tested on #Italian benchmarks #mmlu #arc-c #hellaswag, which we contributed to the opensource lm-evaluation-harness library from

EleutherAI .
Plus, you can now submit your model for automatic evaluation, thanks to to

seeweb sponsored computation.
Curious about the top Italian models? Check out the leaderboard and submit your model!

https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard

reacted to efederici's post with 🔥 about 1 year ago

Post

1982

Finally, I can post! 🚀

I created a Capybara-inspired Italian dataset by translating the initial instruction and running it through a pipeline to generate conversations. I used Claude Sonnet for translation and instruction generation, and Opus for generating the answers.

I hope this dataset proves useful for people working on 🇮🇹 language models.

⛁ Open sourcing the dataset here: efederici/capybara-claude-15k-ita

1 reply

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reacted to their post with 🚀 about 1 year ago

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1595

@mik3ml just released ReDiX/wikipediaQA-ita an interesting synthetic dataset originated from wikipedia using a fine tuned version of mistral-7B specific for the Italian language 🇮🇹 .

1 reply

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Alessandro Ercolani

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Alessandro Ercolani

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