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Update README.md
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README.md
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@@ -40,25 +40,19 @@ The additional details of the Aquila model will be presented in the official tec
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_info = "BAAI/Aquila2-7B"
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tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(model_info, trust_remote_code=True)
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model.eval()
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model.to(
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text = "汽车EDR是什么"
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tokens = tokenizer.encode_plus(text)['input_ids'][:-1]
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with torch.no_grad():
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out = model.generate(tokens, do_sample=True, max_length=512, eos_token_id=100007)[0]
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out = tokenizer.decode(out.cpu().numpy().tolist())
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print(out)
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```
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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device = torch.device("cuda")
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model_info = "BAAI/Aquila2-7B"
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tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(model_info, trust_remote_code=True)
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model.eval()
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model.to(device)
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text = "请给出10个要到北京旅游的理由。"
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tokens = tokenizer.encode_plus(text)['input_ids'][:-1]
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tokens = torch.tensor(tokens)[None,].to(device)
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stop_tokens = ["###", "[UNK]", "</s>"]
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with torch.no_grad():
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out = model.generate(tokens, do_sample=True, max_length=512, eos_token_id=100007, bad_words_ids=[[tokenizer.encode(token)[0] for token in stop_tokens]])[0]
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out = tokenizer.decode(out.cpu().numpy().tolist())
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print(out)
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```
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