{"version":1,"type":"story","url":"https://digestai.news/story/atomicchat-releases-quantized-ling-3-1-flash-models-with-8688-token-ac","json":"https://digestai.news/story/atomicchat-releases-quantized-ling-3-1-flash-models-with-8688-token-ac.json","markdown":"https://digestai.news/story/atomicchat-releases-quantized-ling-3-1-flash-models-with-8688-token-ac.md","slug":"atomicchat-releases-quantized-ling-3-1-flash-models-with-8688-token-ac","headline":"AtomicChat releases quantized Ling 3.1 flash models with 86–88% token accuracy","summary":"The quantized versions match the base BF16 model’s token predictions 86 to 88% of the time on held-out text, with lower KL divergence scores indicating better alignment. Two 1-bit experimental files are included, but users are advised to test them on their own tasks first.\n\nThe builds use an AD-Q4KM quantization scheme, where routed experts are quantized to 1-bit while other layers remain at q8\\(0\\) or higher. The model supports 32,768 tokens of context, with 63 layers using linear attention and 9 retaining compressed attention. AtomicChat notes the model’s efficiency: around 0.5 GB per conversation plus 10 KB per token. The project is open-source under the MIT license, with downloads totaling 524 last month.","keyPoints":["AtomicChat’s quantized Ling 3.1 flash models achieve 86–88% top-1 token accuracy vs. base BF16 model","Two experimental 1-bit files included, but users must validate performance for their use cases","Model supports 32,768-token context with 63 linear-attention layers and 9 compressed-attention layers"],"whyItMatters":"Quantized versions of Ling 3.1 flash could enable faster inference on edge devices without sacrificing too much accuracy, broadening deployment options for developers.","category":{"slug":"models","name":"Generative AI & Models","url":"https://digestai.news/category/models"},"entities":{"companies":["AtomicChat","inclusionAI"],"models":["Ling 3.1 flash","Ling 3.1"],"people":[]},"firstPublishedAt":"2026-10-15T15:16:42Z","updatedAt":"2026-10-15T15:16:42Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"huggingface.co","title":"AtomicChat/Ling-3.1-flash-GGUF · Hugging Face","url":"https://huggingface.co/AtomicChat/Ling-3.1-flash-GGUF","publishedAt":"2026-10-15T15:16:42Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[{"site":"Reddit","url":"https://www.reddit.com/r/LocalLLaMA/comments/1x6of05/atomicchatling31flashgguf_hugging_face/","points":null}],"thread":null,"cite":{"text":"Digest AI, \"AtomicChat releases quantized Ling 3.1 flash models with 86–88% token accuracy\", 15 October 2026, https://digestai.news/story/atomicchat-releases-quantized-ling-3-1-flash-models-with-8688-token-ac","publisher":"Digest AI","title":"AtomicChat releases quantized Ling 3.1 flash models with 86–88% token accuracy","datePublished":"2026-10-15T15:16:42Z","url":"https://digestai.news/story/atomicchat-releases-quantized-ling-3-1-flash-models-with-8688-token-ac"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}