This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from Qwen/Qwen3-Next-80B-A3B-Instruct.
Example usage:
- vLLM
VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \
vllm serve tiny-random/qwen3-next-moe \
--tensor-parallel-size 4 \
--max-model-len 262144 \
--speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'
- SGLang
SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 \
python -m sglang.launch_server \
--model-path tiny-random/qwen3-next-moe \
--tp-size 4 --context-length 262144 \
--mem-fraction-static 0.8 \
--speculative-algo NEXTN \
--speculative-num-steps 3 \
--speculative-eagle-topk 1 \
--speculative-num-draft-tokens 4
- Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "tiny-random/qwen3-next-moe"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
dtype="auto",
device_map="cuda",
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt},
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=8,
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True)
print("content:", content)
Codes to create this repo:
from copy import deepcopy
import torch
import torch.nn as nn
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
GenerationConfig,
pipeline,
set_seed,
)
source_model_id = "Qwen/Qwen3-Next-80B-A3B-Instruct"
save_folder = "/tmp/tiny-random/qwen3-next-moe"
tokenizer = AutoTokenizer.from_pretrained(
source_model_id, trust_remote_code=True,
)
tokenizer.save_pretrained(save_folder)
config = AutoConfig.from_pretrained(
source_model_id, trust_remote_code=True,
)
config._name_or_path = source_model_id
config.hidden_size = 8
config.intermediate_size = 32
config.head_dim = 32
config.num_key_value_heads = 8
config.num_attention_heads = 16
config.num_hidden_layers = 4
config.tie_word_embeddings = False
config.linear_num_key_heads = 8
config.linear_num_value_heads = 16
config.moe_intermediate_size = 32
config.num_experts = 32
config.num_experts_per_tok = 10
config.layer_types = config.layer_types[:4]
config.shared_expert_intermediate_size = 32
model = AutoModelForCausalLM.from_config(
config,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
)
model.generation_config = GenerationConfig.from_pretrained(
source_model_id, trust_remote_code=True,
)
# MTP
model.mtp = nn.ModuleDict({
"pre_fc_norm_embedding": nn.RMSNorm(config.hidden_size),
"fc": nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False),
"norm": nn.RMSNorm(config.hidden_size),
"pre_fc_norm_hidden": nn.RMSNorm(config.hidden_size),
"layers": nn.ModuleList([deepcopy(model.model.layers[3])]),
})
model = model.to(torch.bfloat16)
set_seed(42)
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:
Qwen3NextForCausalLM(
(model): Qwen3NextModel(
(embed_tokens): Embedding(151936, 8)
(layers): ModuleList(
(0-2): 3 x Qwen3NextDecoderLayer(
(linear_attn): Qwen3NextGatedDeltaNet(
(act): SiLU()
(conv1d): Conv1d(4096, 4096, kernel_size=(4,), stride=(1,), padding=(3,), groups=4096, bias=False)
(in_proj_qkvz): Linear(in_features=8, out_features=6144, bias=False)
(in_proj_ba): Linear(in_features=8, out_features=32, bias=False)
(norm): FusedRMSNormGated(128, eps=1e-06, activation=silu)
(out_proj): Linear(in_features=2048, out_features=8, bias=False)
)
(mlp): Qwen3NextSparseMoeBlock(
(gate): Linear(in_features=8, out_features=32, bias=False)
(experts): ModuleList(
(0-31): 32 x Qwen3NextMLP(
(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): SiLU()
)
)
(shared_expert): Qwen3NextMLP(
(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): SiLU()
)
(shared_expert_gate): Linear(in_features=8, out_features=1, bias=False)
)
(input_layernorm): Qwen3NextRMSNorm((8,), eps=1e-06)
(post_attention_layernorm): Qwen3NextRMSNorm((8,), eps=1e-06)
)
(3): Qwen3NextDecoderLayer(
(self_attn): Qwen3NextAttention(
(q_proj): Linear(in_features=8, out_features=1024, bias=False)
(k_proj): Linear(in_features=8, out_features=256, bias=False)
(v_proj): Linear(in_features=8, out_features=256, bias=False)
(o_proj): Linear(in_features=512, out_features=8, bias=False)
(q_norm): Qwen3NextRMSNorm((32,), eps=1e-06)
(k_norm): Qwen3NextRMSNorm((32,), eps=1e-06)
)
(mlp): Qwen3NextSparseMoeBlock(
(gate): Linear(in_features=8, out_features=32, bias=False)
(experts): ModuleList(
(0-31): 32 x Qwen3NextMLP(
(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): SiLU()
)
)
(shared_expert): Qwen3NextMLP(
(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): SiLU()
)
(shared_expert_gate): Linear(in_features=8, out_features=1, bias=False)
)
(input_layernorm): Qwen3NextRMSNorm((8,), eps=1e-06)
(post_attention_layernorm): Qwen3NextRMSNorm((8,), eps=1e-06)
)
)
(norm): Qwen3NextRMSNorm((8,), eps=1e-06)
(rotary_emb): Qwen3NextRotaryEmbedding()
)
(lm_head): Linear(in_features=8, out_features=151936, 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)
(norm): RMSNorm((8,), eps=None, elementwise_affine=True)
(pre_fc_norm_hidden): RMSNorm((8,), eps=None, elementwise_affine=True)
(layers): ModuleList(
(0): Qwen3NextDecoderLayer(
(self_attn): Qwen3NextAttention(
(q_proj): Linear(in_features=8, out_features=1024, bias=False)
(k_proj): Linear(in_features=8, out_features=256, bias=False)
(v_proj): Linear(in_features=8, out_features=256, bias=False)
(o_proj): Linear(in_features=512, out_features=8, bias=False)
(q_norm): Qwen3NextRMSNorm((32,), eps=1e-06)
(k_norm): Qwen3NextRMSNorm((32,), eps=1e-06)
)
(mlp): Qwen3NextSparseMoeBlock(
(gate): Linear(in_features=8, out_features=32, bias=False)
(experts): ModuleList(
(0-31): 32 x Qwen3NextMLP(
(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): SiLU()
)
)
(shared_expert): Qwen3NextMLP(
(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): SiLU()
)
(shared_expert_gate): Linear(in_features=8, out_features=1, bias=False)
)
(input_layernorm): Qwen3NextRMSNorm((8,), eps=1e-06)
(post_attention_layernorm): Qwen3NextRMSNorm((8,), eps=1e-06)
)
)
)
)