Qwen3-4B text encoder — quantized (Flux.2 Klein)

Quantized variants of the Qwen3-4B text encoder used by Flux.2 Klein (Comfy-Org/vae-text-encorder-for-flux-klein-4b, split_files/text_encoders/qwen_3_4b.safetensors), derived from the base language model Qwen/Qwen3-4B.

Files

embed_tokens and the first/last 2 of the model's 36 transformer blocks are kept at source BF16 precision in all three files.

• qwen3_4b_int8_convrot.safetensors: the remaining 224 attention/MLP weight tensors are quantized to INT8 with ConvRot (group-wise Hadamard rotation, group size 256) and row-wise scaling.
• qwen3_4b_nvfp4_convrot_int8.safetensors: the remaining 224 tensors are split by role — the 128 attention-projection tensors (self_attn.{q,k,v,o}_proj in the non-excluded blocks) are INT8 with ConvRot (group size 256, row-scaled), and the 96 MLP tensors (mlp.{gate,up,down}_proj) are NVFP4.
• qwen3_4b_mixed_int4_int8_convrot.safetensors: same role-based split as above, but the 96 MLP tensors are INT4 W4A4 ConvRot (group size 256) instead of NVFP4, and the 128 attention-projection tensors are promoted to INT8 ConvRot (row-scaled) to recover quality INT4 alone tends to lose on those layers. Quality caveat: per this project's quantization procedure, INT4 ConvRot output quality depends heavily on layer-selection method and rounding mode — this promotion criterion (attention projections promoted, bulk MLP left at INT4) is the same role-based split used for the NVFP4 variant above, not a result from a per-layer sensitivity sweep, and hasn't been separately evaluated for quality beyond the functional check below.

Hardware requirements

Quantization method

Produced with convert_to_quant (ctq):

ConvRot INT8 (qwen3_4b_int8_convrot.safetensors):

ctq -i qwen_3_4b.safetensors -o qwen3_4b_int8_convrot.safetensors \
    --int8 --scaling-mode row --dynamic-convrot --convrot-group-size 256 \
    --layer-config qwen3_4b_layer_config_int8_convrot.json \
    --comfy_quant --save-quant-metadata

Where the layer-config (qwen3_4b_layer_config_int8_convrot.json, included in this repo) excludes model.embed_tokens and model.layers.{0,1,34,35} from quantization (kept at source BF16 precision); every other 2D weight tensor falls through to the --int8 primary format.

NVFP4 + ConvRot INT8 (qwen3_4b_nvfp4_convrot_int8.safetensors):

ctq -i qwen_3_4b.safetensors -o qwen3_4b_nvfp4_convrot_int8.safetensors \
    --nvfp4 --low-memory \
    --custom-layers 'model\.layers\.(2|3|...|33)\.self_attn\.(q|k|v|o)_proj' \
    --custom-type int8 --custom-scaling-mode row --custom-convrot --custom-convrot-group-size 256 \
    --layer-config qwen3_4b_layer_config_nvfp4_skip_only.json \
    --comfy_quant --save-quant-metadata

Where the layer-config (qwen3_4b_layer_config_nvfp4_skip_only.json, included in this repo) only holds skip: true entries for model.embed_tokens and model.layers.{0,1,34,35}; --custom-layers/--custom-type route the attention-projection layers in the remaining blocks to INT8 ConvRot; everything else falls through to the --nvfp4 primary format. (--custom-type/--fallback must be given alongside --nvfp4 on this ctq build for --layer-config to be consulted at all — see this repo's quantization tooling notes for why a bare --nvfp4 --layer-config ... invocation silently ignores the layer-config.)

Mixed INT4/INT8 ConvRot (qwen3_4b_mixed_int4_int8_convrot.safetensors):

ctq -i qwen_3_4b.safetensors -o qwen3_4b_mixed_int4_int8_convrot.safetensors \
    --int4 --dynamic-convrot --convrot-group-size 256 --low-memory \
    --layer-config qwen3_4b_layer_config_mixed_int4_int8.json \
    --comfy_quant --save-quant-metadata

Where the layer-config (qwen3_4b_layer_config_mixed_int4_int8.json, included in this repo) holds skip: true entries for model.embed_tokens and model.layers.{0,1,34,35}, plus "format": "int8_tensorwise" (ConvRot, row-scaled) entries for the attention-projection layers in the remaining blocks; every other 2D weight tensor falls through to the --int4 primary format.

Learned-rounding optimization (not RTN) was used for all three files — the default when --simple is omitted.

All three files were verified to load and generate end-to-end in ComfyUI as the text encoder in a Flux.2 Klein 4B txt2img workflow. Output quality (vs. the BF16 source) has not been separately assessed beyond this functional check.

License

This is a derivative of Qwen/Qwen3-4B, licensed under the Apache License 2.0. A copy of the license is included below per the license's redistribution terms; this file has been modified (quantized) from the original.

                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

Copyright 2024 Alibaba Cloud

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