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Oct 27, 2025
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update readme #949

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2 changes: 2 additions & 0 deletions README.md
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Expand Up @@ -27,6 +27,8 @@ and [fbaldassarri](https://huggingface.co/fbaldassarri). For usage instructions,


## 🆕 What's New
[2025/10] We enhanced the RTN mode (--iters 0) to significantly reduce quantization cost compared to the default tuning mode. Check out [this doc](./docs/opt_rtn.md) for some accuracy results. If you don’t have sufficient resources, you can use this mode for 4-bit quantization.

[2025/10] We proposed a fast algorithm to generate **mixed bits/datatypes** schemes in minutes. Please
refer to the documentation for accuracy [results](./docs/auto_scheme_acc.md) and [this guide](https://github.com/intel/auto-round/blob/main/docs/step_by_step.md#autoscheme) for usage instructions.

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3 changes: 1 addition & 2 deletions docs/auto_scheme_acc.md
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Expand Up @@ -6,8 +6,7 @@ to stabilize accuracy during evaluation. All other settings follow the default c
We ignore the scale and zp bits in the tables below. The accuracy may change a little as we modified a little of the
implementation. We will rerun all the experiments.

For mxfp experiment, we use fake model while for weight only model we use real model. **No tuning is applied unless explicit stated.
**
For mxfp experiment, we use fake model while for weight only model we use real model. **No tuning is applied unless explicit stated.**

*Average accuracy across `lambada_openai`, `hellaswag`, `piqa`, `winogrande`, and `mmlu`.*

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