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