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### Instruction:
{instruction}
### Response:transformers library:1from transformers import AutoTokenizer
2import transformers
3import torch
4
5model = "tog/TinyLlama-1.1B-alpaca-chat-v1.5"
6tokenizer = AutoTokenizer.from_pretrained(model)
7
8pipeline = transformers.pipeline(
9 "text-generation",
10 model=model,
11 torch_dtype=torch.float16,
12 device_map="auto")
13
14sequences = pipeline(
15 '###Instruction:\nWhat is a large language model? Be concise.\n\n### Response:\n',
16 do_sample=True,
17 top_k=10,
18 num_return_sequences=1,
19 eos_token_id=tokenizer.eos_token_id,
20 max_length=200)
21
22for seq in sequences:
23 print(f"{seq['generated_text']}")Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
Result: ###Instruction:
What is a large language model? Be concise.
### Response:
A large language model is a type of natural language understanding model that can learn to accurately recognize and interpret text data by understanding the context of words. Languages used for text understanding are typically trained on a corpus of text data.