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anakin87's
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LLMs can leak their post-training data (RL included) π§
New interesting paper on this topic from Google DeepMind: Extracting alignment data in open models (2510.18554)
It's known that Language Models memorize data that can be extracted via prompting.
In this paper, the authors investigate this aspect:
- using open models, where prompting can be fully customized by the user, including special tokens.
- focusing on open-source models like Olmo, where full training data is available.
π€ How do they extract data?
During post-training (like SFT), new tokens such as <|user|> are introduced.
The authors hypothesize prompting the model with these tokens can make it output its alignment data (remember Magpie?).
For example, for SFT, their extraction prompt is <|endoftext|><|user|>.
π Evaluating memorization
The authors compare each sampled example with the original data using vector search with embedding similarity.
They find that many outputs are semantically very similar to the original data, even if the exact words differ.
Traditional string-matching algorithms underestimate memorization by 10x.
π What about RL?
Surprisingly, the same technique works to extract data from Reinforcement Learning (PPO/GRPO) phases.
This is counter-intuitive because the RL objective is not designed to increase sequence likelihoods (unlike SFT).
Practical limitation: in this case, extraction relies on using the initial part of the training prompt, which is not generally public.
π Are the extracted data effective for post-training?
Both in SFT and RL, the extracted data can be used to fine-tune models to similar performance to the originals.
The authors suggest that model distillation, where a stronger model is used to drive the training of a weaker one, may be a form of indirect training on the original dataset.
New interesting paper on this topic from Google DeepMind: Extracting alignment data in open models (2510.18554)
It's known that Language Models memorize data that can be extracted via prompting.
In this paper, the authors investigate this aspect:
- using open models, where prompting can be fully customized by the user, including special tokens.
- focusing on open-source models like Olmo, where full training data is available.
π€ How do they extract data?
During post-training (like SFT), new tokens such as <|user|> are introduced.
The authors hypothesize prompting the model with these tokens can make it output its alignment data (remember Magpie?).
For example, for SFT, their extraction prompt is <|endoftext|><|user|>.
π Evaluating memorization
The authors compare each sampled example with the original data using vector search with embedding similarity.
They find that many outputs are semantically very similar to the original data, even if the exact words differ.
Traditional string-matching algorithms underestimate memorization by 10x.
π What about RL?
Surprisingly, the same technique works to extract data from Reinforcement Learning (PPO/GRPO) phases.
This is counter-intuitive because the RL objective is not designed to increase sequence likelihoods (unlike SFT).
Practical limitation: in this case, extraction relies on using the initial part of the training prompt, which is not generally public.
π Are the extracted data effective for post-training?
Both in SFT and RL, the extracted data can be used to fine-tune models to similar performance to the originals.
The authors suggest that model distillation, where a stronger model is used to drive the training of a weaker one, may be a form of indirect training on the original dataset.
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unmodeled-tyler's
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NEW DATASET:
vanta-research/human-ai-collaboration-1
We are excited to share the first iteration of our dataset focused on human-AI collaborative tasks!
This dataset contains 3,050 lines of warm, collaborative, and natural conversational examples designed to teach the model how to effectively and efficiently problem solve back and forth with a human.
Additionally, the examples include <think> tags, showing the model proper internal reasoning.
VANTA Research is committed to contributing back to the open source community in order to make AI development more accessible, transparent, and beneficial for all.
We are excited to share the first iteration of our dataset focused on human-AI collaborative tasks!
This dataset contains 3,050 lines of warm, collaborative, and natural conversational examples designed to teach the model how to effectively and efficiently problem solve back and forth with a human.
Additionally, the examples include <think> tags, showing the model proper internal reasoning.
VANTA Research is committed to contributing back to the open source community in order to make AI development more accessible, transparent, and beneficial for all.
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SelmaNajih001's
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How Financial News Can Be Used to Train Good Financial Models π°
Numbers tell you what happened, but news tells you why.
Iβve written an article explaining how news can be used to train AI models for sentiment analysis and better forecasting. Hope you find it interesting!
Read it here: https://huggingface.co/blog/SelmaNajih001/llms-applied-to-finance
I would love to read your opinions! Iβm open to suggestions on how to improve the methodology and the training
Numbers tell you what happened, but news tells you why.
Iβve written an article explaining how news can be used to train AI models for sentiment analysis and better forecasting. Hope you find it interesting!
Read it here: https://huggingface.co/blog/SelmaNajih001/llms-applied-to-finance
I would love to read your opinions! Iβm open to suggestions on how to improve the methodology and the training
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tomaarsen's
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2 months ago
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π€ Sentence Transformers is joining Hugging Face! π€ This formalizes the existing maintenance structure, as I've personally led the project for the past two years on behalf of Hugging Face! Details:
Today, the Ubiquitous Knowledge Processing (UKP) Lab is transferring the project to Hugging Face. Sentence Transformers will remain a community-driven, open-source project, with the same open-source license (Apache 2.0) as before. Contributions from researchers, developers, and enthusiasts are welcome and encouraged. The project will continue to prioritize transparency, collaboration, and broad accessibility.
Read our full announcement for more details and quotes from UKP and Hugging Face leadership: https://huggingface.co/blog/sentence-transformers-joins-hf
We see an increasing wish from companies to move from large LLM APIs to local models for better control and privacy, reflected in the library's growth: in just the last 30 days, Sentence Transformer models have been downloaded >270 million times, second only to transformers.
I would like to thank the UKP Lab, and especially Nils Reimers and Iryna Gurevych, both for their dedication to the project and for their trust in myself, both now and two years ago. Back then, neither of you knew me well, yet you trusted me to take the project to new heights. That choice ended up being very valuable for the embedding & Information Retrieval community, and I think this choice of granting Hugging Face stewardship will be similarly successful.
I'm very excited about the future of the project, and for the world of embeddings and retrieval at large!
Today, the Ubiquitous Knowledge Processing (UKP) Lab is transferring the project to Hugging Face. Sentence Transformers will remain a community-driven, open-source project, with the same open-source license (Apache 2.0) as before. Contributions from researchers, developers, and enthusiasts are welcome and encouraged. The project will continue to prioritize transparency, collaboration, and broad accessibility.
Read our full announcement for more details and quotes from UKP and Hugging Face leadership: https://huggingface.co/blog/sentence-transformers-joins-hf
We see an increasing wish from companies to move from large LLM APIs to local models for better control and privacy, reflected in the library's growth: in just the last 30 days, Sentence Transformer models have been downloaded >270 million times, second only to transformers.
I would like to thank the UKP Lab, and especially Nils Reimers and Iryna Gurevych, both for their dedication to the project and for their trust in myself, both now and two years ago. Back then, neither of you knew me well, yet you trusted me to take the project to new heights. That choice ended up being very valuable for the embedding & Information Retrieval community, and I think this choice of granting Hugging Face stewardship will be similarly successful.
I'm very excited about the future of the project, and for the world of embeddings and retrieval at large!
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MLX port of BDH (Baby Dragon Hatchling) is up!
Iβve ported the BDH ( https://github.com/pathwaycom/bdh ) model to MLX for Apple Silicon. Itβs a faithful conversion of the PyTorch version: same math, same architecture (byte-level vocab, shared weights across layers, ReLU sparsity, RoPE attention with Q=K), with MLX-friendly APIs and a detailed README explaining the few API-level differences and why results are equivalent.
Code, docs, and training script are ready to use. You may need to adjust the training script a bit to fit your own custom dataset. Only tested on M4 so far, but should work perfect for any M1/M2/M3 users out there.
Iβm currently training this MLX build on my Internal Knowledge Map (IKM) dataset Severian/Internal-Knowledge-Map
Trainingβs underway; expect a day or so before I publish weights. When itβs done, Iβll upload the checkpoint to Hugging Face for anyone to test.
Repo: https://github.com/severian42/BDH-MLX
HF model (coming soon): Severian/BDH-MLX
If you try it on your own data, feedback and PRs are welcome.
Iβve ported the BDH ( https://github.com/pathwaycom/bdh ) model to MLX for Apple Silicon. Itβs a faithful conversion of the PyTorch version: same math, same architecture (byte-level vocab, shared weights across layers, ReLU sparsity, RoPE attention with Q=K), with MLX-friendly APIs and a detailed README explaining the few API-level differences and why results are equivalent.
Code, docs, and training script are ready to use. You may need to adjust the training script a bit to fit your own custom dataset. Only tested on M4 so far, but should work perfect for any M1/M2/M3 users out there.
Iβm currently training this MLX build on my Internal Knowledge Map (IKM) dataset Severian/Internal-Knowledge-Map
Trainingβs underway; expect a day or so before I publish weights. When itβs done, Iβll upload the checkpoint to Hugging Face for anyone to test.
Repo: https://github.com/severian42/BDH-MLX
HF model (coming soon): Severian/BDH-MLX
If you try it on your own data, feedback and PRs are welcome.
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