Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Input: {input} ### Response: 一些例子 { "instruction": "What are the three primary colors?", "i...
在打开的网页中,依次选择Chat setting->Instruction template,在Instruction template中下拉选择Llama-v2,并将Context输入框中的Answer the questions.提示语替换为You are a helpful assistant. 你是一个乐于助人的助手。,最后回到Text generation界面,在input输入框中输入你的指令,即可与chinese-alpaca-2对话了。 不过...
{ "instruction": "Detect the sentiment of the tweet.", "input": "@p0nd3ea Bitcoin wasn't built to live on exchanges.", "output": "Positive"} 然后就是保存生成的JSON文件，以便稍后使用它来训练模型:import jsonwith open("alpaca-bitcoin-sentiment-dataset.json", "w") as f: json.d...
defgenerate_prompt(data_point):returnf"""Below is an instruction that describes a task,pairedwithan input that provides further context.Write a response that appropriately completes the request.# noqa:E501### Instruction:{data_point["instruction"]}### Input:{data_point["input"]}### Response...
python-mtransformers.models.llama.convert_llama_weights_to_hf\--input_diroriginal-weights\--model_size7B\--output_dirweights 转换时间不会很长(我这里是 6 秒钟),稍等片刻即可: 代码语言:shell AI代码解释 # python -m transformers.models.llama.convert_llama_weights_to_hf \# > --input_dir origin...
"input": "@p0nd3ea Bitcoin wasn't built to live on exchanges.", "output": "Positive" } 1. 2. 3. 4. 5. 然后就是保存生成的JSON文件,以便稍后使用它来训练模型: import json with open("alpaca-bitcoin-sentiment-dataset.json", "w") as f: ...
(**inputs) File "<string>", line 125, in __init__ File "D:\Software\anaconda3\envs\KG\lib\site-packages\transformers\training_args.py", line 1400, in __post_init__ and (self.device.type != "cuda") File "D:\Software\anaconda3\envs\KG\lib\site-packages\transformers\training_...
--input_dir unconverted-weights \ --model_size 7B \ --output_dir weights 1. 2. 3. 4. 得到最终的目录结构应该是这样的: weights ├── llama-7b └── tokenizermdki 1. 2. 3. 处理好上述两步,来到第三步,安装 Cog: sudo curl -o /usr/local/bin/cog -L "https://github.com/replicate...
{"instruction":"Detect the sentiment of the tweet.","input":"@p0nd3eaBitcoin wasn't built to live on exchanges.","output":"Positive"} 然后就是保存生成的JSON文件,以便稍后使用它来训练模型: importjsonwithopen("alpaca-bitcoin-sentiment-dataset.json","w")asf: ...
Input:I hava a dream Response: "I have a dream, too!" 由于本次示例只做演示,仅使用极少的中文语料进行训练验证,因此,这里测试效果不太好,需自行添加更多的数据集进行模型预训练和精调。 结语 目前来看,虽然词表扩充+预训练+指令精调能够给模型带来明显的性能提升,但是该方案还是显得过于繁重。如果不是有...