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Model: ShushengYang/Qwen3-VL-2B-Instruct-LLM
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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).

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{%- if tools %}
{{- '<|im_start|>system\n' }}
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{{- tool | tojson }}
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{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
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{{- tool_call.arguments }}
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{{- tool_call.arguments | tojson }}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
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{%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
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{
"architectures": [
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],
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"use_cache": true,
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"vocab_size": 151936
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