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--- |
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library_name: transformers |
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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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--- |
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This model is for debugging. It is randomly initialized using the config from [mistralai/mathstral-7B-v0.1](https://huggingface.co/mistralai/mathstral-7B-v0.1) but with smaller size. |
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Codes: |
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```python |
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from huggingface_hub import create_repo, upload_folder |
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from transformers import ( |
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pipeline, |
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set_seed, |
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AutoConfig, |
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AutoModelForCausalLM, |
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AutoTokenizer, |
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GenerationConfig, |
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) |
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import torch |
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import transformers |
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import os |
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model_id = "mistralai/mathstral-7B-v0.1" |
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repo_id = "yujiepan/mathstral-v0.1-tiny-random" |
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save_path = f"/tmp/{repo_id}" |
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config = AutoConfig.from_pretrained(model_id) |
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config.hidden_size = 8 |
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config.intermediate_size = 32 |
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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.head_dim = 2 |
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print(config) |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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tokenizer.save_pretrained(save_path) |
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model = AutoModelForCausalLM.from_config(config, torch_dtype=torch.bfloat16) |
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model.generation_config = GenerationConfig.from_pretrained(model_id) |
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set_seed(42) |
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with torch.no_grad(): |
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for _, p in sorted(model.named_parameters()): |
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torch.nn.init.uniform_(p, -0.1, 0.1) |
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model.save_pretrained(save_path) |
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, do_sample=False, device="cuda") |
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print(pipe("Hello World!")) |
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messages = [ |
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{"role": "system", "content": "You are a robot."}, |
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{"role": "user", "content": "Hi!"}, |
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] |
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chatbot = pipeline("text-generation", model=save_path, max_length=1000, max_new_tokens=16) |
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print(chatbot(messages)) |
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``` |
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