This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from Qwen/Qwen3.5-397B-A17B.
| File path | Size |
|---|---|
| model.safetensors | 9.6MB |
Example usage:
- vLLM
# Multi-token prediction is supported
model_id=tiny-random/qwen3.5-moe
vllm serve $model_id \
--tensor-parallel-size 2 \
--speculative-config.method qwen3_next_mtp \
--speculative-config.num_speculative_tokens 2 \
--reasoning-parser qwen3 \
--tool-call-parser qwen3_coder \
--enable-auto-tool-choice \
--max-cudagraph-capture-size 16
- SGLang
# Multi-token prediction is supported
model_id=tiny-random/qwen3.5-moe
python3 -m sglang.launch_server \
--model-path $model_id \
--tp-size 2 \
--tool-call-parser qwen3_coder \
--reasoning-parser qwen3 \
--speculative-algo NEXTN \
--speculative-num-steps 3 \
--speculative-eagle-topk 1 \
--speculative-num-draft-tokens 4
- Transformers
import numpy as np
import torch
import transformers
from PIL import Image
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoProcessor,
AutoTokenizer,
Qwen3_5MoeForConditionalGeneration,
)
model_id = "tiny-random/qwen3.5-moe"
model = Qwen3_5MoeForConditionalGeneration.from_pretrained(
model_id, dtype=torch.bfloat16, device_map="cuda",
)
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."},
],
}
]
inputs = processor.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt"
).to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=32)
output_text = processor.batch_decode(generated_ids[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
print(output_text)
Codes to create this repo:
import json
from copy import deepcopy
from pathlib import Path
import torch
from huggingface_hub import file_exists, hf_hub_download
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoProcessor,
GenerationConfig,
Qwen3_5MoeForConditionalGeneration,
set_seed,
)
source_model_id = "Qwen/Qwen3.5-397B-A17B"
save_folder = "/tmp/tiny-random/qwen35-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,
"layer_types": ['linear_attention'] * 3 + ['full_attention'],
'intermediate_size': 32,
'moe_intermediate_size': 32,
'num_hidden_layers': 4,
'num_attention_heads': 8,
'num_key_value_heads': 4,
'num_experts': 128,
# "num_experts_per_tok": 10,
'shared_expert_intermediate_size': 32,
"linear_key_head_dim": 32,
"linear_num_key_heads": 4,
"linear_num_value_heads": 8,
"linear_value_head_dim": 32,
})
config_json['text_config']['rope_parameters']['mrope_section'] = [1, 1, 2]
config_json["tie_word_embeddings"] = False
config_json['vision_config'].update(
{
'hidden_size': 64,
'intermediate_size': 128,
'num_heads': 2,
'out_hidden_size': 8,
'depth': 2,
}
)
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 = Qwen3_5MoeForConditionalGeneration(config)
with torch.no_grad():
for i in range(3):
attn = model.model.language_model.layers[i].linear_attn
attn.A_log = torch.nn.Parameter(attn.A_log.float())
attn.norm.float()
print(model.state_dict()['model.language_model.layers.0.linear_attn.A_log'].dtype)
print(model.state_dict()['model.language_model.layers.0.linear_attn.norm.weight'].dtype)
model.mtp = torch.nn.ModuleDict({
"pre_fc_norm_embedding": torch.nn.RMSNorm(config.text_config.hidden_size),
"fc": torch.nn.Linear(config.text_config.hidden_size * 2, config.text_config.hidden_size, bias=False),
"layers": torch.nn.ModuleList([deepcopy(model.model.language_model.layers[3])]),
"norm": torch.nn.RMSNorm(config.text_config.hidden_size),
"pre_fc_norm_hidden": torch.nn.RMSNorm(config.text_config.hidden_size),
})
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:
Qwen3_5MoeForConditionalGeneration(
(model): Qwen3_5MoeModel(
(visual): Qwen3_5MoeVisionModel(
(patch_embed): Qwen3_5MoeVisionPatchEmbed(
(proj): Conv3d(3, 64, kernel_size=(2, 16, 16), stride=(2, 16, 16))
)
(pos_embed): Embedding(2304, 64)
(rotary_pos_emb): Qwen3_5MoeVisionRotaryEmbedding()
(blocks): ModuleList(
(0-1): 2 x Qwen3_5MoeVisionBlock(
(norm1): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
(norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
(attn): Qwen3_5MoeVisionAttention(
(qkv): Linear(in_features=64, out_features=192, bias=True)
(proj): Linear(in_features=64, out_features=64, bias=True)
)
(mlp): Qwen3_5MoeVisionMLP(
(linear_fc1): Linear(in_features=64, out_features=128, bias=True)
(linear_fc2): Linear(in_features=128, out_features=64, bias=True)
(act_fn): GELUTanh()
)
)
)
(merger): Qwen3_5MoeVisionPatchMerger(
(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)
)
)
(language_model): Qwen3_5MoeTextModel(
(embed_tokens): Embedding(248320, 8)
(layers): ModuleList(
(0-2): 3 x Qwen3_5MoeDecoderLayer(
(linear_attn): Qwen3_5MoeGatedDeltaNet(
(act): SiLUActivation()
(conv1d): Conv1d(512, 512, kernel_size=(4,), stride=(1,), padding=(3,), groups=512, bias=False)
(norm): FusedRMSNormGated(32, eps=1e-06, activation=silu)
(out_proj): Linear(in_features=256, out_features=8, bias=False)
(in_proj_qkv): Linear(in_features=8, out_features=512, bias=False)
(in_proj_z): Linear(in_features=8, out_features=256, bias=False)
(in_proj_b): Linear(in_features=8, out_features=8, bias=False)
(in_proj_a): Linear(in_features=8, out_features=8, bias=False)
)
(mlp): Qwen3_5MoeSparseMoeBlock(
(gate): Qwen3_5MoeTopKRouter()
(experts): Qwen3_5MoeExperts(
(act_fn): SiLUActivation()
)
(shared_expert): Qwen3_5MoeMLP(
(gate_proj): Linear(in_features=8, out_features=32, bias=False)
(up_proj): Linear(in_features=8, out_features=32, bias=False)
(down_proj): Linear(in_features=32, out_features=8, bias=False)
(act_fn): SiLUActivation()
)
(shared_expert_gate): Linear(in_features=8, out_features=1, bias=False)
)
(input_layernorm): Qwen3_5MoeRMSNorm((8,), eps=1e-06)
(post_attention_layernorm): Qwen3_5MoeRMSNorm((8,), eps=1e-06)
)
(3): Qwen3_5MoeDecoderLayer(
(self_attn): Qwen3_5MoeAttention(
(q_proj): Linear(in_features=8, out_features=512, 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): Qwen3_5MoeRMSNorm((32,), eps=1e-06)
(k_norm): Qwen3_5MoeRMSNorm((32,), eps=1e-06)
)
(mlp): Qwen3_5MoeSparseMoeBlock(
(gate): Qwen3_5MoeTopKRouter()
(experts): Qwen3_5MoeExperts(
(act_fn): SiLUActivation()
)
(shared_expert): Qwen3_5MoeMLP(
(gate_proj): Linear(in_features=8, out_features=32, bias=False)
(up_proj): Linear(in_features=8, out_features=32, bias=False)
(down_proj): Linear(in_features=32, out_features=8, bias=False)
(act_fn): SiLUActivation()
)
(shared_expert_gate): Linear(in_features=8, out_features=1, bias=False)
)
(input_layernorm): Qwen3_5MoeRMSNorm((8,), eps=1e-06)
(post_attention_layernorm): Qwen3_5MoeRMSNorm((8,), eps=1e-06)
)
)
(norm): Qwen3_5MoeRMSNorm((8,), eps=1e-06)
(rotary_emb): Qwen3_5MoeTextRotaryEmbedding()
)
)
(lm_head): Linear(in_features=8, out_features=248320, bias=False)
(mtp): ModuleDict(
(pre_fc_norm_embedding): RMSNorm((8,), eps=None, elementwise_affine=True)
(fc): Linear(in_features=16, out_features=8, bias=False)
(layers): ModuleList(
(0): Qwen3_5MoeDecoderLayer(
(self_attn): Qwen3_5MoeAttention(
(q_proj): Linear(in_features=8, out_features=512, 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): Qwen3_5MoeRMSNorm((32,), eps=1e-06)
(k_norm): Qwen3_5MoeRMSNorm((32,), eps=1e-06)
)
(mlp): Qwen3_5MoeSparseMoeBlock(
(gate): Qwen3_5MoeTopKRouter()
(experts): Qwen3_5MoeExperts(
(act_fn): SiLUActivation()
)
(shared_expert): Qwen3_5MoeMLP(
(gate_proj): Linear(in_features=8, out_features=32, bias=False)
(up_proj): Linear(in_features=8, out_features=32, bias=False)
(down_proj): Linear(in_features=32, out_features=8, bias=False)
(act_fn): SiLUActivation()
)
(shared_expert_gate): Linear(in_features=8, out_features=1, bias=False)
)
(input_layernorm): Qwen3_5MoeRMSNorm((8,), eps=1e-06)
(post_attention_layernorm): Qwen3_5MoeRMSNorm((8,), eps=1e-06)
)
)
(norm): RMSNorm((8,), eps=None, elementwise_affine=True)
(pre_fc_norm_hidden): RMSNorm((8,), eps=None, elementwise_affine=True)
)
)