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README.md
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- pretrained
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# Qwen2-beta
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## Introduction
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##
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained("Qwen2/Qwen2-beta-7B-Chat", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained("Qwen2/Qwen2-beta-7B-Chat")
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prompt = "Give me a short introduction to large language model."
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messages = [{"role": "user", "content": prompt}]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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model_inputs = tokenizer([text], return_tensors="pt").to(device)
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generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512, do_sample=True)
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generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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## Citation
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# Qwen2-beta-72B
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## Introduction
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<br>
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## Usage
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We do not advise you to use base language models for text generation. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model.
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## Citation
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