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per_device_train_batch_size=32,
logging_steps=3650,
gradient_accumulation_steps=8,
num_train_epochs=1,
weight_decay=0.1,
warmup_steps=1_000,
lr_scheduler_type="cosine",
learning_rate=5e-4,
save_steps=3650,
fp16=True,1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3mamba2 = AutoModelForCausalLM.from_pretrained("J4bb4wukis/llama_360m_wikipedia_en_shuffeld ")
4tokenizer = AutoTokenizer.from_pretrained("J4bb4wukis/llama_360m_wikipedia_en_shuffeld ")
5
6prompts = "Angela Merkel is"
7inputs = tokenizer(prompts,return_tensors='pt').input_ids
8outputs = mamba2.generate(inputs, max_new_tokens=100, do_sample=True, top_k=10, top_p=0.95)
9print(tokenizer.batch_decode(outputs, skip_special_tokens=True))