Qwen2.5-VL AWQ Quantization: "Currently, quantification and calibration of Qwen2_5_VLTextModel are not supported" even though config.json is Qwen2_5_VLForConditionalGeneration

Hi team,

I’m trying to quantize the Qwen2.5-VL (3B) model using **lmdeploy** lite **auto_awq**, but I’m running into the following issue:

**Problem Description:**
My model’s config.json has "architectures": ["Qwen2_5_VLForConditionalGeneration"] and "model_type": "qwen2_5_vl".
However, when I run the quantization command, I get this error:
`RuntimeError: Currently, quantification and calibration of Qwen2_5_VLTextModel are not supported. The supported model types are ... Qwen2_5_VLForConditionalGeneration ...`

Here is a snippet from my config.json:
`"architectures": [`
`"Qwen2_5_VLForConditionalGeneration"`
`],`
`"model_type": "qwen2_5_vl"`

My quantization command:
`lmdeploy lite auto_awq \`
`./model_weight/Recognition \`
`--calib-dataset 'ptb' \`
`--calib-samples 64 \`
`--calib-seqlen 1024 \`
`--w-bits 4 \`
`--w-group-size 128 \`
`--batch-size 1 \`
`--work-dir ./monkeyocr_quantization`

Environment:
- transformers version: 4.52.4
- lmdeploy version: 0.9.0 (also tried latest from source)
- Qwen3 currently does not provide a pip package, and transformers cannot directly import qwen2_5_vl models

My Questions:
- The README of lmdeploy states that Qwen2.5-VL (3B, 7B, 72B) is supported for INT4 quantization and inference, but quantization fails with this error and the model is not recognized as ForConditionalGeneration.
- How can I quantize official Qwen2.5-VL weights with lmdeploy AWQ? Is there any temporary model code or script available for this?
- Or is it currently only possible to use already-quantized weights for inference, and self-quantization is not yet supported?

Thank you for your help!