初始化项目，由ModelHub XC社区提供模型

Model: tiny-random/qwen3-moe
Source: Original Platform

README.md

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+---
+library_name: transformers
+pipeline_tag: text-generation
+inference: true
+widget:
+  - text: Hello!
+    example_title: Hello world
+    group: Python
+---
+
+This tiny model is for debugging. It is randomly initialized with the config adapted from [Qwen/Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B).
+
+### Example usage:
+
+```python
+from transformers import pipeline
+model_id = "tiny-random/qwen3-moe"
+pipe = pipeline(
+    "text-generation", model=model_id, device="cuda",
+    trust_remote_code=True, max_new_tokens=3,
+)
+print(pipe("Hello World!"))
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto"
+)
+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,
+    enable_thinking=True  # Switches between thinking and non-thinking modes. Default is True.
+)
+print(text)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=128
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+try:
+    # rindex finding 151668 (</think>)
+    index = len(output_ids) - output_ids[::-1].index(151668)
+except ValueError:
+    index = 0
+thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
+content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
+print("thinking content:", thinking_content)
+print("content:", content)
+```
+
+### Codes to create this repo:
+
+```python
+import torch
+
+from transformers import (
+    AutoConfig,
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    GenerationConfig,
+    pipeline,
+    set_seed,
+)
+
+source_model_id = "Qwen/Qwen3-235B-A22B"
+save_folder = "/tmp/tiny-random/qwen3-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 = 64
+config.intermediate_size = 128
+config.moe_intermediate_size = 128
+config.head_dim = 32
+config.decoder_sparse_step = 2  # layer0=mlp, layer1=moe
+config.num_experts = 8
+config.num_experts_per_tok = 2
+config.num_key_value_heads = 1
+config.num_attention_heads = 2
+config.num_hidden_layers = 2
+config.max_window_layers = 1
+config.tie_word_embeddings = True
+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,
+)
+set_seed(42)
+with torch.no_grad():
+    for name, p in sorted(model.named_parameters()):
+        torch.nn.init.normal_(p, 0, 0.5)
+        print(name, p.shape)
+model.save_pretrained(save_folder)
+```