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--- |
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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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library_name: transformers |
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--- |
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This model is randomly initialized, using the config from [Qwen/Qwen1.5-72B-Chat](https://huggingface.co/Qwen/Qwen1.5-72B-Chat/blob/main/config.json) but with smaller size. |
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Note the model is in float16. |
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Codes: |
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```python |
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import transformers |
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import torch |
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import os |
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from huggingface_hub import create_repo, upload_folder |
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source_model_id = 'Qwen/Qwen1.5-72B-Chat' |
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tiny_random_name = 'qwen1.5-tiny-random' |
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save_path = f'/tmp/yujiepan/{tiny_random_name}' |
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repo_id = f'yujiepan/{tiny_random_name}' |
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config = transformers.AutoConfig.from_pretrained( |
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source_model_id, trust_remote_code=True) |
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config.hidden_size = 4 |
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config.intermediate_size = 6 |
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config.num_attention_heads = 4 |
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config.num_hidden_layers = 2 |
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config.num_key_value_heads = 2 |
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config.torch_dtype = torch.float16 |
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model = transformers.AutoModelForCausalLM.from_config( |
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config, trust_remote_code=True, torch_dtype=torch.float16) |
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model = model.half() |
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tokenizer = transformers.AutoTokenizer.from_pretrained( |
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source_model_id, trust_remote_code=True) |
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result = transformers.pipelines.pipeline( |
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'text-generation', |
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model=model, tokenizer=tokenizer, |
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device=0, |
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max_new_tokens=16, |
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)('Hello World!') |
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print(result) |
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model.save_pretrained(save_path) |
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tokenizer.save_pretrained(save_path) |
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os.system(f'ls -alh {save_path}') |
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create_repo(repo_id, exist_ok=True) |
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upload_folder(repo_id=repo_id, folder_path=save_path) |
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``` |
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