metadata
library_name: transformers
tags:
- custom_generate
Description
Test repo to experiment with calling 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
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)
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, skip_special_tokens=True))