Tag: Qwen
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Hacker News: Alibaba releases an ‘open’ challenger to OpenAI’s O1 reasoning model
Source URL: https://techcrunch.com/2024/11/27/alibaba-releases-an-open-challenger-to-openais-o1-reasoning-model/ Source: Hacker News Title: Alibaba releases an ‘open’ challenger to OpenAI’s O1 reasoning model Feedly Summary: Comments AI Summary and Description: Yes Summary: The arrival of the QwQ-32B-Preview model from Alibaba’s Qwen team introduces a significant competitor to OpenAI’s offerings in the AI reasoning space. With its innovative self-fact-checking capabilities and ability…
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Simon Willison’s Weblog: QwQ: Reflect Deeply on the Boundaries of the Unknown
Source URL: https://simonwillison.net/2024/Nov/27/qwq/#atom-everything Source: Simon Willison’s Weblog Title: QwQ: Reflect Deeply on the Boundaries of the Unknown Feedly Summary: QwQ: Reflect Deeply on the Boundaries of the Unknown Brand openly licensed model from Alibaba Cloud’s Qwen team, this time clearly inspired by OpenAI’s work on reasoning in o1. I love how the introduce the new…
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Simon Willison’s Weblog: Quantization matters
Source URL: https://simonwillison.net/2024/Nov/23/quantization-matters/#atom-everything Source: Simon Willison’s Weblog Title: Quantization matters Feedly Summary: Quantization matters What impact does quantization have on the performance of an LLM? been wondering about this for quite a while, now here are numbers from Paul Gauthier. He ran differently quantized versions of Qwen 2.5 32B Instruct through his Aider code editing…
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Simon Willison’s Weblog: TextSynth Server
Source URL: https://simonwillison.net/2024/Nov/21/textsynth-server/ Source: Simon Willison’s Weblog Title: TextSynth Server Feedly Summary: TextSynth Server I’d missed this: Fabrice Bellard (yes, that Fabrice Bellard) has a project called TextSynth Server which he describes like this: ts_server is a web server proposing a REST API to large language models. They can be used for example for text…
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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: 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,…
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Simon Willison’s Weblog: NuExtract 1.5
Source URL: https://simonwillison.net/2024/Nov/16/nuextract-15/#atom-everything Source: Simon Willison’s Weblog Title: NuExtract 1.5 Feedly Summary: NuExtract 1.5 Structured extraction – where an LLM helps turn unstructured text (or image content) into structured data – remains one of the most directly useful applications of LLMs. NuExtract is a family of small models directly trained for this purpose, and released…