This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from Qwen/Qwen3-VL-235B-A22B-Instruct.
Example usage:
import numpy as np
import torch
import transformers
from PIL import Image
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoProcessor,
AutoTokenizer,
Qwen3VLMoeForConditionalGeneration,
)
model_id = "tiny-random/qwen3-vl-moe"
model = Qwen3VLMoeForConditionalGeneration.from_pretrained(
model_id, dtype=torch.bfloat16, device_map="cuda",
# attn_implementation="flash_attention_2",
)
processor = AutoProcessor.from_pretrained(model_id)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
},
{"type": "text", "text": "Describe this image."},
],
}
]
# Preparation for inference
inputs = processor.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt"
).to(model.device)
# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=32)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
Codes to create this repo:
import json
from pathlib import Path
import accelerate
import torch
from huggingface_hub import file_exists, hf_hub_download
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoProcessor,
GenerationConfig,
Qwen3VLMoeForConditionalGeneration,
set_seed,
)
source_model_id = "Qwen/Qwen3-VL-235B-A22B-Instruct"
save_folder = "/tmp/tiny-random/qwen3-vl-moe"
processor = AutoProcessor.from_pretrained(source_model_id, trust_remote_code=True)
processor.save_pretrained(save_folder)
with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f:
config_json = json.load(f)
config_json['text_config'].update({
'head_dim': 32,
'hidden_size': 8,
'intermediate_size': 64,
'moe_intermediate_size': 64,
'num_hidden_layers': 2,
'num_attention_heads': 8,
'num_key_value_heads': 4,
'num_experts': 16,
# 'decoder_sparse_step': 2,
})
config_json['text_config']['rope_scaling']['mrope_section'] = [8, 4, 4]
config_json['vision_config'].update(
{
'hidden_size': 64,
'intermediate_size': 64,
'num_heads': 2,
'out_hidden_size': 8,
'depth': 6,
'deepstack_visual_indexes': [1, 3, 5],
}
)
with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
json.dump(config_json, f, indent=2)
config = AutoConfig.from_pretrained(
save_folder,
trust_remote_code=True,
)
print(config)
torch.set_default_dtype(torch.bfloat16)
model = Qwen3VLMoeForConditionalGeneration(config)
torch.set_default_dtype(torch.float32)
if file_exists(filename="generation_config.json", repo_id=source_model_id, repo_type='model'):
model.generation_config = GenerationConfig.from_pretrained(
source_model_id, trust_remote_code=True,
)
model.generation_config.do_sample = True
print(model.generation_config)
model = model.cpu()
with torch.no_grad():
for name, p in sorted(model.named_parameters()):
torch.nn.init.normal_(p, 0, 0.1)
print(name, p.shape)
model.save_pretrained(save_folder)
Printing the model:
Qwen3VLMoeForConditionalGeneration(
(model): Qwen3VLMoeModel(
(visual): Qwen3VLMoeVisionModel(
(patch_embed): Qwen3VLMoeVisionPatchEmbed(
(proj): Conv3d(3, 64, kernel_size=(2, 16, 16), stride=(2, 16, 16))
)
(pos_embed): Embedding(2304, 64)
(rotary_pos_emb): Qwen3VLMoeVisionRotaryEmbedding()
(blocks): ModuleList(
(0-5): 6 x Qwen3VLMoeVisionBlock(
(norm1): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
(norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
(attn): Qwen3VLMoeVisionAttention(
(qkv): Linear(in_features=64, out_features=192, bias=True)
(proj): Linear(in_features=64, out_features=64, bias=True)
)
(mlp): Qwen3VLMoeVisionMLP(
(linear_fc1): Linear(in_features=64, out_features=64, bias=True)
(linear_fc2): Linear(in_features=64, out_features=64, bias=True)
(act_fn): PytorchGELUTanh()
)
)
)
(merger): Qwen3VLMoeVisionPatchMerger(
(norm): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
(linear_fc1): Linear(in_features=256, out_features=256, bias=True)
(act_fn): GELU(approximate='none')
(linear_fc2): Linear(in_features=256, out_features=8, bias=True)
)
(deepstack_merger_list): ModuleList(
(0-2): 3 x Qwen3VLMoeVisionPatchMerger(
(norm): LayerNorm((256,), eps=1e-06, elementwise_affine=True)
(linear_fc1): Linear(in_features=256, out_features=256, bias=True)
(act_fn): GELU(approximate='none')
(linear_fc2): Linear(in_features=256, out_features=8, bias=True)
)
)
)
(language_model): Qwen3VLMoeTextModel(
(embed_tokens): Embedding(151936, 8)
(layers): ModuleList(
(0-1): 2 x Qwen3VLMoeTextDecoderLayer(
(self_attn): Qwen3VLMoeTextAttention(
(q_proj): Linear(in_features=8, out_features=256, bias=False)
(k_proj): Linear(in_features=8, out_features=128, bias=False)
(v_proj): Linear(in_features=8, out_features=128, bias=False)
(o_proj): Linear(in_features=256, out_features=8, bias=False)
(q_norm): Qwen3VLMoeTextRMSNorm((32,), eps=1e-06)
(k_norm): Qwen3VLMoeTextRMSNorm((32,), eps=1e-06)
)
(mlp): Qwen3VLMoeTextSparseMoeBlock(
(gate): Qwen3VLMoeTextRouter(in_features=8, out_features=16, bias=False)
(experts): Qwen3VLMoeTextExperts(
(act_fn): SiLU()
)
)
(input_layernorm): Qwen3VLMoeTextRMSNorm((8,), eps=1e-06)
(post_attention_layernorm): Qwen3VLMoeTextRMSNorm((8,), eps=1e-06)
)
)
(norm): Qwen3VLMoeTextRMSNorm((8,), eps=1e-06)
(rotary_emb): Qwen3VLMoeTextRotaryEmbedding()
)
)
(lm_head): Linear(in_features=8, out_features=151936, bias=False)
)