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--- |
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library_name: transformers |
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pipeline_tag: text-generation |
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inference: true |
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widget: |
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- text: Hello! |
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example_title: Hello world |
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group: Python |
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base_model: |
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- Qwen/Qwen3-Next-80B-A3B-Instruct |
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--- |
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This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [Qwen/Qwen3-Next-80B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct). |
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### Example usage: |
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- vLLM |
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```bash |
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VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \ |
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vllm serve tiny-random/qwen3-next-moe \ |
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--tensor-parallel-size 4 \ |
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--max-model-len 262144 \ |
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--speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}' |
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``` |
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- SGLang |
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```bash |
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SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 \ |
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python -m sglang.launch_server \ |
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--model-path tiny-random/qwen3-next-moe \ |
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--tp-size 4 --context-length 262144 \ |
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--mem-fraction-static 0.8 \ |
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--speculative-algo NEXTN \ |
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--speculative-num-steps 3 \ |
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--speculative-eagle-topk 1 \ |
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--speculative-num-draft-tokens 4 |
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``` |
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- Transformers |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
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model_id = "tiny-random/qwen3-next-moe" |
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# load the tokenizer and the model |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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dtype="auto", |
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device_map="cuda", |
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) |
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# prepare the model input |
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prompt = "Give me a short introduction to large language model." |
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messages = [ |
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{"role": "user", "content": prompt}, |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True, |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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# conduct text completion |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=8, |
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) |
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() |
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content = tokenizer.decode(output_ids, skip_special_tokens=True) |
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print("content:", content) |
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``` |
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### Codes to create this repo: |
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```python |
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from copy import deepcopy |
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import torch |
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import torch.nn as nn |
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from transformers import ( |
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AutoConfig, |
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AutoModelForCausalLM, |
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AutoTokenizer, |
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GenerationConfig, |
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pipeline, |
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set_seed, |
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) |
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source_model_id = "Qwen/Qwen3-Next-80B-A3B-Instruct" |
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save_folder = "/tmp/tiny-random/qwen3-next-moe" |
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tokenizer = AutoTokenizer.from_pretrained( |
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source_model_id, trust_remote_code=True, |
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) |
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tokenizer.save_pretrained(save_folder) |
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config = AutoConfig.from_pretrained( |
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source_model_id, trust_remote_code=True, |
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) |
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config._name_or_path = source_model_id |
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config.hidden_size = 8 |
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config.intermediate_size = 32 |
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config.head_dim = 32 |
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config.num_key_value_heads = 8 |
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config.num_attention_heads = 16 |
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config.num_hidden_layers = 4 |
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config.tie_word_embeddings = False |
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config.linear_num_key_heads = 8 |
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config.linear_num_value_heads = 16 |
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config.moe_intermediate_size = 32 |
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config.num_experts = 32 |
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config.num_experts_per_tok = 10 |
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config.layer_types = config.layer_types[:4] |
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config.shared_expert_intermediate_size = 32 |
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model = AutoModelForCausalLM.from_config( |
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config, |
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torch_dtype=torch.bfloat16, |
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trust_remote_code=True, |
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) |
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model.generation_config = GenerationConfig.from_pretrained( |
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source_model_id, trust_remote_code=True, |
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) |
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# MTP |
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model.mtp = nn.ModuleDict({ |
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"pre_fc_norm_embedding": nn.RMSNorm(config.hidden_size), |
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"fc": nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False), |
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"norm": nn.RMSNorm(config.hidden_size), |
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"pre_fc_norm_hidden": nn.RMSNorm(config.hidden_size), |
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"layers": nn.ModuleList([deepcopy(model.model.layers[3])]), |
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}) |
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model = model.to(torch.bfloat16) |
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set_seed(42) |
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with torch.no_grad(): |
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for name, p in sorted(model.named_parameters()): |
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torch.nn.init.normal_(p, 0, 0.1) |
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print(name, p.shape) |
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model.save_pretrained(save_folder) |
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``` |
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### Printing the model: |
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```text |
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Qwen3NextForCausalLM( |
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(model): Qwen3NextModel( |
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(embed_tokens): Embedding(151936, 8) |
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(layers): ModuleList( |
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(0-2): 3 x Qwen3NextDecoderLayer( |
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(linear_attn): Qwen3NextGatedDeltaNet( |
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(act): SiLU() |
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(conv1d): Conv1d(4096, 4096, kernel_size=(4,), stride=(1,), padding=(3,), groups=4096, bias=False) |
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(in_proj_qkvz): Linear(in_features=8, out_features=6144, bias=False) |
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(in_proj_ba): Linear(in_features=8, out_features=32, bias=False) |
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(norm): FusedRMSNormGated(128, eps=1e-06, activation=silu) |
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(out_proj): Linear(in_features=2048, out_features=8, bias=False) |
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) |
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(mlp): Qwen3NextSparseMoeBlock( |
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(gate): Linear(in_features=8, out_features=32, bias=False) |
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(experts): ModuleList( |
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(0-31): 32 x Qwen3NextMLP( |
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(gate_proj): Linear(in_features=8, out_features=32, bias=False) |
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(up_proj): Linear(in_features=8, out_features=32, bias=False) |
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(down_proj): Linear(in_features=32, out_features=8, bias=False) |
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(act_fn): SiLU() |
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) |
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) |
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(shared_expert): Qwen3NextMLP( |
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(gate_proj): Linear(in_features=8, out_features=32, bias=False) |
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(up_proj): Linear(in_features=8, out_features=32, bias=False) |
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(down_proj): Linear(in_features=32, out_features=8, bias=False) |
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(act_fn): SiLU() |
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) |
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(shared_expert_gate): Linear(in_features=8, out_features=1, bias=False) |
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) |
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(input_layernorm): Qwen3NextRMSNorm((8,), eps=1e-06) |
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(post_attention_layernorm): Qwen3NextRMSNorm((8,), eps=1e-06) |
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) |
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(3): Qwen3NextDecoderLayer( |
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(self_attn): Qwen3NextAttention( |
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(q_proj): Linear(in_features=8, out_features=1024, bias=False) |
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(k_proj): Linear(in_features=8, out_features=256, bias=False) |
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(v_proj): Linear(in_features=8, out_features=256, bias=False) |
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(o_proj): Linear(in_features=512, out_features=8, bias=False) |
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(q_norm): Qwen3NextRMSNorm((32,), eps=1e-06) |
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(k_norm): Qwen3NextRMSNorm((32,), eps=1e-06) |
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) |
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(mlp): Qwen3NextSparseMoeBlock( |
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(gate): Linear(in_features=8, out_features=32, bias=False) |
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(experts): ModuleList( |
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(0-31): 32 x Qwen3NextMLP( |
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(gate_proj): Linear(in_features=8, out_features=32, bias=False) |
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(up_proj): Linear(in_features=8, out_features=32, bias=False) |
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(down_proj): Linear(in_features=32, out_features=8, bias=False) |
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(act_fn): SiLU() |
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) |
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) |
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(shared_expert): Qwen3NextMLP( |
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(gate_proj): Linear(in_features=8, out_features=32, bias=False) |
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(up_proj): Linear(in_features=8, out_features=32, bias=False) |
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(down_proj): Linear(in_features=32, out_features=8, bias=False) |
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(act_fn): SiLU() |
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) |
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(shared_expert_gate): Linear(in_features=8, out_features=1, bias=False) |
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) |
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(input_layernorm): Qwen3NextRMSNorm((8,), eps=1e-06) |
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(post_attention_layernorm): Qwen3NextRMSNorm((8,), eps=1e-06) |
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) |
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) |
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(norm): Qwen3NextRMSNorm((8,), eps=1e-06) |
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(rotary_emb): Qwen3NextRotaryEmbedding() |
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) |
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(lm_head): Linear(in_features=8, out_features=151936, bias=False) |
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(mtp): ModuleDict( |
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(pre_fc_norm_embedding): RMSNorm((8,), eps=None, elementwise_affine=True) |
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(fc): Linear(in_features=16, out_features=8, bias=False) |
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(norm): RMSNorm((8,), eps=None, elementwise_affine=True) |
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(pre_fc_norm_hidden): RMSNorm((8,), eps=None, elementwise_affine=True) |
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(layers): ModuleList( |
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(0): Qwen3NextDecoderLayer( |
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(self_attn): Qwen3NextAttention( |
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(q_proj): Linear(in_features=8, out_features=1024, bias=False) |
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(k_proj): Linear(in_features=8, out_features=256, bias=False) |
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(v_proj): Linear(in_features=8, out_features=256, bias=False) |
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(o_proj): Linear(in_features=512, out_features=8, bias=False) |
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(q_norm): Qwen3NextRMSNorm((32,), eps=1e-06) |
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(k_norm): Qwen3NextRMSNorm((32,), eps=1e-06) |
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) |
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(mlp): Qwen3NextSparseMoeBlock( |
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(gate): Linear(in_features=8, out_features=32, bias=False) |
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(experts): ModuleList( |
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(0-31): 32 x Qwen3NextMLP( |
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(gate_proj): Linear(in_features=8, out_features=32, bias=False) |
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(up_proj): Linear(in_features=8, out_features=32, bias=False) |
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(down_proj): Linear(in_features=32, out_features=8, bias=False) |
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(act_fn): SiLU() |
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) |
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) |
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(shared_expert): Qwen3NextMLP( |
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(gate_proj): Linear(in_features=8, out_features=32, bias=False) |
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(up_proj): Linear(in_features=8, out_features=32, bias=False) |
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(down_proj): Linear(in_features=32, out_features=8, bias=False) |
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(act_fn): SiLU() |
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) |
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(shared_expert_gate): Linear(in_features=8, out_features=1, bias=False) |
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) |
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(input_layernorm): Qwen3NextRMSNorm((8,), eps=1e-06) |
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(post_attention_layernorm): Qwen3NextRMSNorm((8,), eps=1e-06) |
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) |
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) |
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) |
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) |
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``` |
