# Chinese text generation

The quantized version of Tencent Hunyuan A13B Instruction Model, which uses technical means to improve operational efficiency while ensuring performance.

A 6-bit quantized version converted from the DeepSeek-R1-0528-Qwen3-8B model, suitable for text generation tasks in the MLX framework.

This model is a 4-bit quantized version converted from DeepSeek-R1-0528-Qwen3-8B, optimized for the MLX framework and suitable for text generation tasks.

DeepSeek-R1-0528-4bit is a 4-bit quantized model converted from DeepSeek-R1-0528, optimized for the MLX framework.

This is a 3-bit deep quantized model converted from Qwen/Qwen3-235B-A22B, suitable for text generation tasks.

Qwen3-235B-A22B-4bit-DWQ is a 4-bit quantized version converted from the Qwen3-235B-A22B-8bit model, suitable for text generation tasks.

This model is a 4-bit DWQ quantized version of Qwen3-4B, converted to the MLX format for easy text generation using the mlx library.

This is a 4-bit quantized model converted from Qwen/Qwen3-30B-A3B to MLX format, suitable for text generation tasks.

Qwen3-30B-A3B-4bit-DWQ-0508 is a 4-bit quantized model converted from Qwen/Qwen3-30B-A3B to MLX format, suitable for text generation tasks.

Qwen3-14B-4bit-AWQ is an MLX-format model converted from Qwen/Qwen3-14B, using AWQ quantization technology to compress the model to 4bit, suitable for efficient inference on the MLX framework.

This is a 4-bit quantized version based on the Qwen3-30B-A3B model, created through custom DWQ quantization technology distilled from 6-bit to 4-bit, suitable for text generation tasks.

Qwen3-8B-4bit-AWQ is a 4-bit AWQ quantized version converted from Qwen/Qwen3-8B, suitable for text generation tasks in the MLX framework.

This model is a 4-bit quantized version of Qwen/Qwen3-235B-A22B converted to MLX format, suitable for text generation tasks.

Qwen3-30B-A3B-4bit is a 4-bit quantized version converted from Qwen/Qwen3-30B-A3B, suitable for efficient text generation tasks under the MLX framework.

Qwen3-14B-4bit is a 4-bit quantized version of the Qwen/Qwen3-14B model converted using mlx-lm, suitable for text generation tasks.

This is a GGUF format model converted from Google's Gemma 3.4B parameter model, suitable for text generation tasks.

Chinese language model developed based on bigscience/bloom-1b7 architecture with 1.4B parameters, reducing GPU memory usage through vocabulary compression

A Chinese GPT-2 model trained using the WECHSEL method, achieving cross-lingual transfer of monolingual language models through effective initialization of subword embeddings.