Tag: lm
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Hacker News: Building Observability with ClickHouse
Source URL: https://cmtops.dev/posts/building-observability-with-clickhouse/ Source: Hacker News Title: Building Observability with ClickHouse Feedly Summary: Comments AI Summary and Description: Yes Summary: The text outlines the author’s journey in building an observability project using ClickHouse for data warehousing alongside Grafana for visualization and alerting. It highlights the limitations of various tech stacks considered, particularly focusing on Elasticsearch…
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Hacker News: Batched reward model inference and Best-of-N sampling
Source URL: https://raw.sh/posts/easy_reward_model_inference Source: Hacker News Title: Batched reward model inference and Best-of-N sampling Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses advancements in reinforcement learning (RL) models applied to large language models (LLMs), focusing particularly on reward models utilized in techniques like Reinforcement Learning with Human Feedback (RLHF) and dynamic…
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AWS News Blog: Build and modify apps using natural language with AWS App Studio, now generally available
Source URL: https://aws.amazon.com/blogs/aws/build-and-modify-apps-using-natural-language-with-aws-app-studio-now-generally-available/ Source: AWS News Blog Title: Build and modify apps using natural language with AWS App Studio, now generally available Feedly Summary: Unleash your inner developer with AWS App Studio, the generative AI-powered application builder. Turn your idea into fully-fledged, intelligent, custom, secure, and scalable software in minutes. AI Summary and Description: Yes…
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Hacker News: Show HN: FastGraphRAG – Better RAG using good old PageRank
Source URL: https://github.com/circlemind-ai/fast-graphrag Source: Hacker News Title: Show HN: FastGraphRAG – Better RAG using good old PageRank Feedly Summary: Comments AI Summary and Description: Yes Summary: The text introduces the Fast GraphRAG framework, highlighting its innovative approach to agent-driven retrieval workflows, which allow for high-precision query interpretations without extensive resource requirements. This tool is particularly…
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Hacker News: Qwen2.5 Turbo extends context length to 1M tokens
Source URL: http://qwenlm.github.io/blog/qwen2.5-turbo/ Source: Hacker News Title: Qwen2.5 Turbo extends context length to 1M tokens Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses the introduction of Qwen2.5-Turbo, a large language model (LLM) that significantly enhances processing capabilities, particularly with longer contexts, which are critical for many applications involving AI-driven natural language…
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Simon Willison’s Weblog: Pixtral Large
Source URL: https://simonwillison.net/2024/Nov/18/pixtral-large/ Source: Simon Willison’s Weblog Title: Pixtral Large Feedly Summary: Pixtral Large New today from Mistral: Today we announce Pixtral Large, a 124B open-weights multimodal model built on top of Mistral Large 2. Pixtral Large is the second model in our multimodal family and demonstrates frontier-level image understanding. The weights are out on…
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Simon Willison’s Weblog: Qwen: Extending the Context Length to 1M Tokens
Source URL: https://simonwillison.net/2024/Nov/18/qwen-turbo/#atom-everything Source: Simon Willison’s Weblog Title: Qwen: Extending the Context Length to 1M Tokens Feedly Summary: Qwen: Extending the Context Length to 1M Tokens The new Qwen2.5-Turbo boasts a million token context window (up from 128,000 for Qwen 2.5) and faster performance: Using sparse attention mechanisms, we successfully reduced the time to first…
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Simon Willison’s Weblog: Quoting Jack Clark
Source URL: https://simonwillison.net/2024/Nov/18/jack-clark/ Source: Simon Willison’s Weblog Title: Quoting Jack Clark Feedly Summary: The main innovation here is just using more data. Specifically, Qwen2.5 Coder is a continuation of an earlier Qwen 2.5 model. The original Qwen 2.5 model was trained on 18 trillion tokens spread across a variety of languages and tasks (e.g, writing,…