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