Meta offers Llama AI to US allies amid global tech race - Digital Watch Observatory
<a href="https://news.google.com/rss/articles/CBMiiAFBVV95cUxPQlRvLXdhTWw4aEhRcExnMm9CdTl3MVhpTjBiQzl0WmNnMXFnRkw1RUVIbDFTTi1mRGdyYXlHbHFfc2xWVXBjb2xuYWQwd2cyNmNHY2FLbmxZcGlZNnVESXJHdDlCTzZIbmJTZFgzX3RwVE9SZGNUdzZaSi0yb2ZudTBGT0FpT2Va?oc=5" target="_blank">Meta offers Llama AI to US allies amid global tech race</a> <font color="#6f6f6f">Digital Watch Observatory</font>
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Help running Qwen3-Coder-Next TurboQuant (TQ3) model
I found a TQ3-quantized version of Qwen3-Coder-Next here: https://huggingface.co/edwardyoon79/Qwen3-Coder-Next-TQ3_0 According to the page, this model requires a compatible inference engine that supports TurboQuant. It also provides a command, but it doesn’t clearly specify which version or fork of llama.cpp should be used (or maybe I missed it). llama-server I’ve tried the following llama.cpp forks that claim to support TQ3, but none of them worked for me: https://github.com/TheTom/llama-cpp-turboquant https://github.com/turbo-tan/llama.cpp-tq3 https://github.com/drdotdot/llama.cpp-turbo3-tq3 If anyone has successfully run this model, I’d really appreciate it if you could share how you did it. submitted by /u/UnluckyTeam3478 [link] [comments]
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