| | --- |
| | library_name: transformers |
| | base_model: |
| | - Qwen/Qwen3.5-27B |
| | --- |
| | |
| | This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [Qwen/Qwen3.5-27B](https://huggingface.co/Qwen/Qwen3.5-27B). |
| |
|
| | | File path | Size | |
| | |------|------| |
| | | model.safetensors | 8.8MB | |
| |
|
| |
|
| | ### Example usage: |
| |
|
| | - vLLM |
| |
|
| | ```bash |
| | # Multi-token prediction is supported |
| | model_id=yujiepan/qwen3.5-tiny-random |
| | 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 |
| |
|
| | ```bash |
| | # Multi-token prediction is supported |
| | model_id=yujiepan/qwen3.5-tiny-random |
| | 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 |
| |
|
| | ```python |
| | import numpy as np |
| | import torch |
| | import transformers |
| | from PIL import Image |
| | from transformers import ( |
| | AutoModel, |
| | AutoModelForCausalLM, |
| | AutoProcessor, |
| | AutoTokenizer, |
| | Qwen3_5ForConditionalGeneration, |
| | ) |
| | |
| | model_id = "yujiepan/qwen3.5-tiny-random" |
| | model = Qwen3_5ForConditionalGeneration.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], skip_special_tokens=False) |
| | print(output_text) |
| | ``` |
| |
|
| | ### Codes to create this repo: |
| |
|
| | <details> |
| | <summary>Click to expand</summary> |
| |
|
| | ```python |
| | 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_5ForConditionalGeneration, |
| | set_seed, |
| | ) |
| | |
| | source_model_id = "Qwen/Qwen3.5-27B" |
| | save_folder = "/tmp/yujiepan/qwen35-tiny-random" |
| | |
| | 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_5ForConditionalGeneration(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, 0.2) |
| | print(name, p.shape) |
| | model.save_pretrained(save_folder) |
| | ``` |
| |
|
| | </details> |
| |
|
| | ### Printing the model: |
| |
|
| | <details><summary>Click to expand</summary> |
| |
|
| | ```text |
| | Qwen3_5ForConditionalGeneration( |
| | (model): Qwen3_5Model( |
| | (visual): Qwen3_5VisionModel( |
| | (patch_embed): Qwen3_5VisionPatchEmbed( |
| | (proj): Conv3d(3, 64, kernel_size=(2, 16, 16), stride=(2, 16, 16)) |
| | ) |
| | (pos_embed): Embedding(2304, 64) |
| | (rotary_pos_emb): Qwen3_5VisionRotaryEmbedding() |
| | (blocks): ModuleList( |
| | (0-1): 2 x Qwen3_5VisionBlock( |
| | (norm1): LayerNorm((64,), eps=1e-06, elementwise_affine=True) |
| | (norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True) |
| | (attn): Qwen3_5VisionAttention( |
| | (qkv): Linear(in_features=64, out_features=192, bias=True) |
| | (proj): Linear(in_features=64, out_features=64, bias=True) |
| | ) |
| | (mlp): Qwen3_5VisionMLP( |
| | (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_5VisionPatchMerger( |
| | (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_5TextModel( |
| | (embed_tokens): Embedding(248320, 8) |
| | (layers): ModuleList( |
| | (0-2): 3 x Qwen3_5DecoderLayer( |
| | (linear_attn): Qwen3_5GatedDeltaNet( |
| | (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_5MLP( |
| | (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() |
| | ) |
| | (input_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06) |
| | (post_attention_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06) |
| | ) |
| | (3): Qwen3_5DecoderLayer( |
| | (self_attn): Qwen3_5Attention( |
| | (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_5RMSNorm((32,), eps=1e-06) |
| | (k_norm): Qwen3_5RMSNorm((32,), eps=1e-06) |
| | ) |
| | (mlp): Qwen3_5MLP( |
| | (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() |
| | ) |
| | (input_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06) |
| | (post_attention_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06) |
| | ) |
| | ) |
| | (norm): Qwen3_5RMSNorm((8,), eps=1e-06) |
| | (rotary_emb): Qwen3_5TextRotaryEmbedding() |
| | ) |
| | ) |
| | (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_5DecoderLayer( |
| | (self_attn): Qwen3_5Attention( |
| | (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_5RMSNorm((32,), eps=1e-06) |
| | (k_norm): Qwen3_5RMSNorm((32,), eps=1e-06) |
| | ) |
| | (mlp): Qwen3_5MLP( |
| | (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() |
| | ) |
| | (input_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06) |
| | (post_attention_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06) |
| | ) |
| | ) |
| | (norm): RMSNorm((8,), eps=None, elementwise_affine=True) |
| | (pre_fc_norm_hidden): RMSNorm((8,), eps=None, elementwise_affine=True) |
| | ) |
| | ) |
| | ``` |
| |
|
| | </details> |
