Tag: language processing

  • Hacker News: Implementing LLaMA3 in 100 Lines of Pure Jax

    Source URL: https://saurabhalone.com/blogs/llama3/web Source: Hacker News Title: Implementing LLaMA3 in 100 Lines of Pure Jax Feedly Summary: Comments AI Summary and Description: Yes Summary: The text provides a comprehensive tutorial on implementing the LLaMA 3 language model using JAX, emphasizing its functional programming nature and its suitability for educational purposes. This tutorial is particularly relevant…

  • Simon Willison’s Weblog: Nomic Embed Text V2: An Open Source, Multilingual, Mixture-of-Experts Embedding Model

    Source URL: https://simonwillison.net/2025/Feb/12/nomic-embed-text-v2/#atom-everything Source: Simon Willison’s Weblog Title: Nomic Embed Text V2: An Open Source, Multilingual, Mixture-of-Experts Embedding Model Feedly Summary: Nomic Embed Text V2: An Open Source, Multilingual, Mixture-of-Experts Embedding Model Nomic continue to release the most interesting and powerful embedding models. Their latest is Embed Text V2, an Apache 2.0 licensed multi-lingual 1.9GB…

  • Cloud Blog: How to build a strong brand logo with Imagen 3 and Gemini

    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/build-a-brand-logo-with-imagen-3-and-gemini/ Source: Cloud Blog Title: How to build a strong brand logo with Imagen 3 and Gemini Feedly Summary: Last year we announced Imagen 3, our highest quality image generation model. Imagen 3 is available to Vertex AI customers, which means businesses can create high quality images that reflect their own brand style…

  • Hacker News: Calculate the number of language model tokens for a string

    Source URL: https://blog.mastykarz.nl/calculate-number-language-model-tokens-string/ Source: Hacker News Title: Calculate the number of language model tokens for a string Feedly Summary: Comments AI Summary and Description: Yes Summary: The text provides guidance on calculating the number of language model tokens for a given string, which is essential for developers working with AI and NLP applications. The method…

  • Simon Willison’s Weblog: A professional workflow for translation using LLMs

    Source URL: https://simonwillison.net/2025/Feb/2/workflow-for-translation/#atom-everything Source: Simon Willison’s Weblog Title: A professional workflow for translation using LLMs Feedly Summary: A professional workflow for translation using LLMs Tom Gally is a professional translator who has been exploring the use of LLMs since the release of GPT-4. In this Hacker News comment he shares a detailed workflow for how…

  • Hacker News: RLHF Book

    Source URL: https://rlhfbook.com/ Source: Hacker News Title: RLHF Book Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses the concept of Reinforcement Learning from Human Feedback (RLHF), particularly its relevance in the development of machine learning systems, particularly within language models. It highlights the foundational aspects of RLHF while aiming to provide…

  • Hacker News: Auto-Differentiating Any LLM Workflow: A Farewell to Manual Prompting

    Source URL: https://arxiv.org/abs/2501.16673 Source: Hacker News Title: Auto-Differentiating Any LLM Workflow: A Farewell to Manual Prompting Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses LLM-AutoDiff, a novel framework aimed at improving the efficiency of prompt engineering for large language models (LLMs) by utilizing automatic differentiation principles. This development has significant implications…

  • Hacker News: Multi-head latent attention (DeepSeek) and other KV cache tricks explained

    Source URL: https://www.pyspur.dev/blog/multi-head-latent-attention-kv-cache-paper-list Source: Hacker News Title: Multi-head latent attention (DeepSeek) and other KV cache tricks explained Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses advanced techniques in Key-Value (KV) caching that enhance the efficiency of language models like ChatGPT during text generation. It highlights how these optimizations can significantly reduce…

  • Simon Willison’s Weblog: Quoting Jack Clark

    Source URL: https://simonwillison.net/2025/Jan/28/jack-clark-r1/#atom-everything Source: Simon Willison’s Weblog Title: Quoting Jack Clark Feedly Summary: The most surprising part of DeepSeek-R1 is that it only takes ~800k samples of ‘good’ RL reasoning to convert other models into RL-reasoners. Now that DeepSeek-R1 is available people will be able to refine samples out of it to convert any other…

  • Simon Willison’s Weblog: DeepSeek Janus-Pro

    Source URL: https://simonwillison.net/2025/Jan/27/deepseek-janus-pro/#atom-everything Source: Simon Willison’s Weblog Title: DeepSeek Janus-Pro Feedly Summary: DeepSeek Janus-Pro Another impressive model release from DeepSeek. Janus is their series of “unified multimodal understanding and generation models" – these are models that can both accept images as input and generate images for output. Janus-Pro is a new 7B model accompanied by…