because they require specific detailed skills or knowledge tuned to a specific task (knowledge engineering bottleneck) Develop systems that can automatically adapt and customize them- selves to individual

shown␣ �→below prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) # Directly use generate() and tokenizer.decode() to the last code snippet from transformers import TextStreamer streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512

搜集说明数据（基于 prompt 训练方式的数 据集），训练监督策略 Labeler(标记者)揭 示期望的输出行为 这个数据用来联合监 督学习，对GPT-3进 行微调 对这个prompt训练的数据集 和若干模型的结果进行抽样 Labeler(标记者)从 最优到最差将输出 结果进行排序 这个数据用来训练反 馈模型 新的prompt从 数据集中抽样 借助模型生成输出

Location Seattle Prompt “When would you like to fly?” “When would you like to fly?” Polly Origin Destination Departure Date Flight Booking London Heathrow Seattle Prompt “When would you like

Replace pytorch with base if you use the default environment. You should see a prefix in your prompt showing the name of the activated environment, e.g.: 3. Install PyTorch It may be very slow to

com/distribution/ and choose download based on your OS. Create a new conda environment Launch anaconda prompt, this will open base Anaconda environment. Let us create a new conda environment. This process is

want to pull the latest container. docker pull nvcr.io/nvidia/pytorch:23.07-py3 2. Open a command prompt and paste the pull command. Running PyTorch PyTorch RN-08516-001_v23.07 | 4 Ensure that the