Create README.md

README.md

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+---
+pipeline_tag: text-generation
+license: other
+license_name: modified-mit
+license_link: https://github.com/MiniMax-AI/MiniMax-M2.1/blob/main/LICENSE
+base_model:
+- MiniMaxAI/MiniMax-M2.1
+tags:
+- smoothie-qwen
+---
+
+# Smoothie-MiniMax-M2.1
+
+## Overview
+
+This is a modified version of [MiniMax-M2.1](https://huggingface.co/MiniMaxAI/MiniMax-M2.1), using [Smoothie-Qwen](https://github.com/dnotitia/smoothie-qwen).
+
+## What is it?
+
+Reduced probability of Kanji, Hanja, Chinese character(radical) tokens to reduce sudden language mixing.
+
+## For who?
+
+If you see Chinese characters during non-Chinese conversation, this model will help in this case.
+
+**For Chinese and Japanese users: Use original model!** This model will behave worse in these languages.
+
+## Result
+
+From my testing:
+
+* Chinese character did not appear on Korean conversation.
+* When I ask about Japanese topic, model sucessfully answered with Kanji and Hiragana (although I can't test correctness of response)
+
+## How I did it?
+
+I tried to replicate Unsloth's UD quant as possible because my system only can handle up to 3-bit quants.
+
+1. Download original model
+2. Apply Smoothie-qwen (See configs/config.yaml for reference)
+3. Convert to GGUF (BF16)
+4. Run llama-quantize with Unsloth imatrix and manual override to tensor type from UD quants
+5. Run llama-gguf-split (max size 50GB)
+
+## Recommendation
+
+At temperature 1.0, tool calling is bit unstable. I recommend temperature=0.7.