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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 model is for debugging. It is randomly initialized with the config from [Qwen/QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview) but is of smaller size. 
+
+Codes:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import transformers
+import torch
+import os
+from huggingface_hub import create_repo, upload_folder
+import accelerate
+
+model_id = 'Qwen/QwQ-32B-Preview'
+save_path = '/tmp/yujiepan/QwQ-tiny-random'
+repo_id = 'yujiepan/QwQ-tiny-random'
+
+os.system(f'rm -rf {save_path}')
+
+config = transformers.AutoConfig.from_pretrained(
+    model_id,
+    trust_remote_code=True,
+)
+config._name_or_path = model_id
+config.hidden_size = 8
+config.intermediate_size = 16
+config.num_key_value_heads = 1
+config.num_attention_heads = 2
+config.num_hidden_layers = 2
+config.max_window_layers = 1
+
+model = transformers.AutoModelForCausalLM.from_config(
+    config,
+    trust_remote_code=True,
+)
+model.generation_config = transformers.GenerationConfig.from_pretrained(
+    model_id)
+model = model.to(torch.bfloat16)
+
+transformers.set_seed(42)
+num_params = 0
+with torch.no_grad():
+    for name, p in sorted(model.named_parameters()):
+        print(name, p.shape)
+        torch.nn.init.uniform_(p, -0.5, 0.5)
+        num_params += p.numel()
+print("Total number of parameters:", num_params)
+model.save_pretrained(save_path)
+
+tokenizer = transformers.AutoTokenizer.from_pretrained(
+    model_id,
+    trust_remote_code=True,
+)
+tokenizer.save_pretrained(save_path)
+
+os.system(f'ls -alh {save_path}')
+create_repo(repo_id, exist_ok=True)
+upload_folder(repo_id=repo_id, folder_path=save_path)
+
+
+def try_example(model, tokenizer):
+    prompt = "How many r in strawberry."
+    messages = [
+        {"role": "system", "content": "You are a helpful and harmless assistant. You should think step-by-step."},
+        {"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)
+
+    generated_ids = model.generate(
+        **model_inputs,
+        max_new_tokens=32
+    )
+    generated_ids = [
+        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+
+    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    print(response)
+
+try_example(model, tokenizer)
+```