Huihui Qwen3-VL-8B-Instruct Abliterated — Quantized checkpoints
Quantized variants of Aragduhl/Huihui-Qwen3-VL-8B-Instruct-abliterated-comfy, which packages the full vision tower and language model for ComfyUI. The base repository lists the model under the Apache-2.0 license.
These checkpoints were produced from the BF16 single-file model using Comfy-Org/comfy-model-tools and Comfy Kitchen.
Files
| File | Format | Size | Quantized weights |
|---|---|---|---|
Huihui-Qwen3-VL-8B-Instruct-abliterated_w4a8.safetensors |
W4A8 | 6.46 GiB | 252 language-model linear layers; embeddings in INT8 |
Huihui-Qwen3-VL-8B-Instruct-abliterated_int8_convrot.safetensors |
INT8-ConvRot | 9.29 GiB | 252 language-model linear layers and embeddings in INT8 |
The vision tower and lm_head are retained in BF16 in both files. W4A8 uses 4-bit weights with INT8 activations at runtime. INT8-ConvRot stores the language-model linear weights in INT8 with ConvRot; activations are quantized at runtime.
Quantization measurements
These are relative weight-reconstruction errors reported by the exporter, not output-quality scores:
- W4A8: 7.313% mean across 252 quantized layers.
- INT8-ConvRot: 0.888% mean and 1.021% maximum across 252 quantized layers.
The safetensors headers and quantization markers were checked. No ComfyUI inference or output-quality benchmark was run.