初始化项目，由ModelHub XC社区提供模型

Model: ShushengYang/Qwen3-VL-2B-Instruct-LLM
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-VL-2B-Instruct
+library_name: transformers
+---
+
+# Qwen3-VL-2B-Instruct-LLM
+
+The language model of [Qwen/Qwen3-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct),
+extracted as a plain `Qwen3ForCausalLM` checkpoint (vision tower dropped).
+
+The Qwen3-VL-2B text tower is architecturally identical to Qwen3-1.7B dense
+(hidden 2048 / 28 layers / 16 Q + 8 KV heads / head_dim 128 / FFN 6144 /
+vocab 151936 / tied embeddings). Its interleaved MRoPE assigns text tokens the
+same index on all three axes, which reduces exactly to standard 1D RoPE, so
+this export keeps the VL `rope_theta` (5e6) and drops `rope_scaling`.
+
+**Equivalence**: text-only logits match the original VL model bit-for-bit in
+fp32 (`max |logit diff| = 0.0` on a held-out prompt).
+
+Load with:
+
+```python
+from transformers import AutoModelForCausalLM
+model = AutoModelForCausalLM.from_pretrained("shushengyang/Qwen3-VL-2B-Instruct-LLM")
+```
+
+Produced by `personal/shusheng/mllm/misc/convert_qwen3vl_llm_to_qwen3.py`
+(amilabs monorepo); intended as an LLM initialization for ScaleWM MLLM
+experiments (vision-language-pretrained LLM vs text-only Qwen3-1.7B).