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| """Builds a tiny, randomly initialised Qwen3-VL (model + processor) on disk with no downloads. | |
| It has the same architecture, processor, chat-template mechanics (image placeholder expansion, | |
| vision tower, deepstack, M-RoPE) as Qwen/Qwen3-VL-*-Instruct, so loading it through | |
| AutoProcessor / AutoModelForImageTextToText exercises exactly the code path the Space uses. | |
| Its outputs are of course meaningless. | |
| """ | |
| from __future__ import annotations | |
| from pathlib import Path | |
| SPECIAL = ["<|endoftext|>", "<|im_start|>", "<|im_end|>", "<|vision_start|>", "<|vision_end|>", | |
| "<|image_pad|>", "<|video_pad|>"] | |
| CHAT_TEMPLATE = ( | |
| "{% for message in messages %}<|im_start|>{{ message['role'] }}\n" | |
| "{% if message['content'] is string %}{{ message['content'] }}{% else %}" | |
| "{% for c in message['content'] %}{% if c['type'] == 'image' %}<|vision_start|><|image_pad|><|vision_end|>" | |
| "{% elif c['type'] == 'text' %}{{ c['text'] }}{% endif %}{% endfor %}{% endif %}<|im_end|>\n" | |
| "{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}" | |
| ) | |
| def build(out: str | Path) -> Path: | |
| import torch | |
| from tokenizers import Tokenizer, decoders, models, pre_tokenizers, trainers | |
| from transformers import (Qwen2TokenizerFast, Qwen2VLImageProcessorFast, Qwen3VLConfig, | |
| Qwen3VLForConditionalGeneration, Qwen3VLProcessor) | |
| from transformers.models.qwen3_vl.video_processing_qwen3_vl import Qwen3VLVideoProcessor | |
| out = Path(out) | |
| tok = Tokenizer(models.BPE()) | |
| tok.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False) | |
| tok.decoder = decoders.ByteLevel() | |
| corpus = ["The warranty is 36 months.", "Revenue by region FY2025.", "Text sources: [1] report.pdf, p.3", | |
| "Question: Answer: Cite text as [1] and images as [Img 1]."] * 50 | |
| tok.train_from_iterator(corpus, trainers.BpeTrainer(vocab_size=400, special_tokens=SPECIAL, | |
| initial_alphabet=pre_tokenizers.ByteLevel.alphabet())) | |
| tokenizer = Qwen2TokenizerFast(tokenizer_object=tok, eos_token="<|im_end|>", pad_token="<|endoftext|>", | |
| unk_token=None, bos_token=None) | |
| ids = {t: tokenizer.convert_tokens_to_ids(t) for t in SPECIAL} | |
| processor = Qwen3VLProcessor( | |
| image_processor=Qwen2VLImageProcessorFast(patch_size=16, merge_size=2, temporal_patch_size=2, | |
| size={"shortest_edge": 32 * 32, "longest_edge": 128 * 128}), | |
| tokenizer=tokenizer, video_processor=Qwen3VLVideoProcessor(), chat_template=CHAT_TEMPLATE) | |
| cfg = Qwen3VLConfig( | |
| text_config=dict(vocab_size=len(tokenizer), hidden_size=64, intermediate_size=128, num_hidden_layers=2, | |
| num_attention_heads=4, num_key_value_heads=2, head_dim=16, max_position_embeddings=4096, | |
| rope_scaling={"rope_type": "default", "mrope_section": [4, 2, 2], "mrope_interleaved": True}, | |
| eos_token_id=ids["<|im_end|>"], pad_token_id=ids["<|endoftext|>"]), | |
| vision_config=dict(depth=2, hidden_size=32, intermediate_size=64, num_heads=2, patch_size=16, | |
| spatial_merge_size=2, temporal_patch_size=2, out_hidden_size=64, | |
| num_position_embeddings=256, deepstack_visual_indexes=[0]), | |
| image_token_id=ids["<|image_pad|>"], video_token_id=ids["<|video_pad|>"], | |
| vision_start_token_id=ids["<|vision_start|>"], vision_end_token_id=ids["<|vision_end|>"], | |
| eos_token_id=ids["<|im_end|>"], pad_token_id=ids["<|endoftext|>"], | |
| ) | |
| torch.manual_seed(0) | |
| model = Qwen3VLForConditionalGeneration(cfg) | |
| model.generation_config.eos_token_id = ids["<|im_end|>"] | |
| model.generation_config.pad_token_id = ids["<|endoftext|>"] | |
| model.save_pretrained(out) | |
| processor.save_pretrained(out) | |
| return out | |
| if __name__ == "__main__": | |
| import sys | |
| print(build(sys.argv[1] if len(sys.argv) > 1 else "tiny_qwen3vl")) | |