# AtomicChat releases quantized Ling 3.1 flash models with 86–88% token accuracy

Digest AI · Generative AI & Models · published 2026-10-15T15:16:42Z

Canonical: https://digestai.news/story/atomicchat-releases-quantized-ling-3-1-flash-models-with-8688-token-ac

## 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.

The 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.

## Key points

- 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

## Why it matters

Quantized versions of Ling 3.1 flash could enable faster inference on edge devices without sacrificing too much accuracy, broadening deployment options for developers.

## Sources

1. [AtomicChat/Ling-3.1-flash-GGUF · Hugging Face](https://huggingface.co/AtomicChat/Ling-3.1-flash-GGUF) (huggingface.co, 2026-10-15, primary source)

## Cite

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

---

Written by Digest AI's editorial model from the linked sources; the sources are the record. 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
JSON: https://digestai.news/story/atomicchat-releases-quantized-ling-3-1-flash-models-with-8688-token-ac.json