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MICHAEL A ALVES

wolverine604
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reacted to burtenshaw's post with 👍 16 days ago
I made a real time voice agent with FastRTC, smolagents, and hugging face inference providers. Check it out in this space: 🔗 https://huggingface.co/spaces/burtenshaw/coworking_agent
reacted to singhsidhukuldeep's post with 👍 16 days ago
O1 Embedder: Transforming Retrieval Models with Reasoning Capabilities Researchers from University of Science and Technology of China and Beijing Academy of Artificial Intelligence have developed a novel retrieval model that mimics the slow-thinking capabilities of reasoning-focused LLMs like OpenAI's O1 and DeepSeek's R1. Unlike traditional embedding models that directly match queries with documents, O1 Embedder first generates thoughtful reflections about the query before performing retrieval. This two-step process significantly improves performance on complex retrieval tasks, especially those requiring intensive reasoning or zero-shot generalization to new domains. The technical implementation is fascinating: - The model integrates two essential functions: Thinking and Embedding - It uses an "Exploration-Refinement" data synthesis workflow where initial thoughts are generated by an LLM and refined by a retrieval committee - A multi-task training method fine-tunes a pre-trained LLM to generate retrieval thoughts via behavior cloning while simultaneously learning embedding capabilities through contrastive learning - Memory-efficient joint training enables both tasks to share encoding results, dramatically increasing batch size The results are impressive - O1 Embedder outperforms existing methods across 12 datasets in both in-domain and out-of-domain scenarios. For example, it achieves a 3.9% improvement on Natural Questions and a 3.0% boost on HotPotQA compared to models without thinking capabilities. This approach represents a significant paradigm shift in retrieval technology, bridging the gap between traditional dense retrieval and the reasoning capabilities of large language models. What do you think about this approach? Could "thinking before retrieval" transform how we build search systems?
reacted to jasoncorkill's post with 👍 16 days ago
Has OpenGVLab Lumina Outperformed OpenAI’s Model? We’ve just released the results from a large-scale human evaluation (400k annotations) of OpenGVLab’s newest text-to-image model, Lumina. Surprisingly, Lumina outperforms OpenAI’s DALL-E 3 in terms of alignment, although it ranks #6 in our overall human preference benchmark. To support further development in text-to-image models, we’re making our entire human-annotated dataset publicly available. If you’re working on model improvements and need high-quality data, feel free to explore. We welcome your feedback and look forward to any insights you might share! https://huggingface.co/datasets/Rapidata/OpenGVLab_Lumina_t2i_human_preference
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wolverine604's activity

reacted to burtenshaw's post with 👍 16 days ago
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I made a real time voice agent with FastRTC, smolagents, and hugging face inference providers. Check it out in this space:

🔗 burtenshaw/coworking_agent
·
reacted to singhsidhukuldeep's post with 👍 16 days ago
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3469
O1 Embedder: Transforming Retrieval Models with Reasoning Capabilities

Researchers from University of Science and Technology of China and Beijing Academy of Artificial Intelligence have developed a novel retrieval model that mimics the slow-thinking capabilities of reasoning-focused LLMs like OpenAI's O1 and DeepSeek's R1.

Unlike traditional embedding models that directly match queries with documents, O1 Embedder first generates thoughtful reflections about the query before performing retrieval. This two-step process significantly improves performance on complex retrieval tasks, especially those requiring intensive reasoning or zero-shot generalization to new domains.

The technical implementation is fascinating:

- The model integrates two essential functions: Thinking and Embedding
- It uses an "Exploration-Refinement" data synthesis workflow where initial thoughts are generated by an LLM and refined by a retrieval committee
- A multi-task training method fine-tunes a pre-trained LLM to generate retrieval thoughts via behavior cloning while simultaneously learning embedding capabilities through contrastive learning
- Memory-efficient joint training enables both tasks to share encoding results, dramatically increasing batch size

The results are impressive - O1 Embedder outperforms existing methods across 12 datasets in both in-domain and out-of-domain scenarios. For example, it achieves a 3.9% improvement on Natural Questions and a 3.0% boost on HotPotQA compared to models without thinking capabilities.

This approach represents a significant paradigm shift in retrieval technology, bridging the gap between traditional dense retrieval and the reasoning capabilities of large language models.

What do you think about this approach? Could "thinking before retrieval" transform how we build search systems?
reacted to jasoncorkill's post with 👍 16 days ago
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3825
Has OpenGVLab Lumina Outperformed OpenAI’s Model?

We’ve just released the results from a large-scale human evaluation (400k annotations) of OpenGVLab’s newest text-to-image model, Lumina. Surprisingly, Lumina outperforms OpenAI’s DALL-E 3 in terms of alignment, although it ranks #6 in our overall human preference benchmark.

To support further development in text-to-image models, we’re making our entire human-annotated dataset publicly available. If you’re working on model improvements and need high-quality data, feel free to explore.

We welcome your feedback and look forward to any insights you might share!

Rapidata/OpenGVLab_Lumina_t2i_human_preference
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