--- library_name: transformers pipeline_tag: text-generation inference: true widget: - text: Hello! example_title: Hello world group: Python --- This model is randomly initialized, using the config from [Qwen/Qwen1.5-MoE-A2.7B-Chat](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B-Chat/blob/main/config.json) but with smaller size. Note the model is in float16. Codes: ```python import transformers import torch import os from huggingface_hub import create_repo, upload_folder source_model_id = 'Qwen/Qwen1.5-MoE-A2.7B-Chat' save_path = '/tmp/yujiepan/qwen1.5-moe-tiny-random' repo_id = 'yujiepan/qwen1.5-moe-tiny-random' config = transformers.AutoConfig.from_pretrained( source_model_id, trust_remote_code=True) config.hidden_size = 4 config.intermediate_size = 2 config.num_attention_heads = 4 config.num_hidden_layers = 2 config.num_key_value_heads = 2 config.moe_intermediate_size = 2 config.shared_expert_intermediate_size = 2 config.max_window_layers = 1 config.use_sliding_window = True config.torch_dtype = torch.float16 model = transformers.AutoModelForCausalLM.from_config( config, trust_remote_code=True, torch_dtype=torch.float16) model = model.half() tokenizer = transformers.AutoTokenizer.from_pretrained( source_model_id, trust_remote_code=True) result = transformers.pipelines.pipeline( 'text-generation', model=model, tokenizer=tokenizer, device=0, max_new_tokens=16, )('Hello World!') print(result) model.save_pretrained(save_path) 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) ```