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--- |
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library_name: transformers |
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tags: |
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- custom_generate |
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--- |
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## Description |
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Example repository used to document `generate` from the hub. It is a simplified implementation of greedy decoding. |
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## Base model: |
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`Qwen/Qwen2.5-0.5B-Instruct` |
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## Model compatibility |
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Most models. More specifically, any `transformer` LLM/VLM trained for causal language modeling. |
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## Additional Arguments |
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`left_padding` (`int`, *optional*): number of padding tokens to add before the provided input |
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## Output Type changes |
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(none) |
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## Example usage |
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```py |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct") |
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model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", device_map="auto") |
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inputs = tokenizer(["The quick brown"], return_tensors="pt").to(model.device) |
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# There is a print message hardcoded in the custom generation method |
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gen_out = model.generate(**inputs, left_padding=5, custom_generate="transformers-community/custom_generate_example", trust_remote_code=True) |
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print(tokenizer.batch_decode(gen_out)) # don't skip special tokens |
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#['<|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>The quick brown fox jumps over the lazy dog.\n\nThe sentence "The quick'] |
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``` |
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