# 输出结果的形状# (bs, head, seq_len, dk), (bs, head, seq_len, seq_len)print(res.shape, att_scores.shape) 接下来,我们再来看下LLaMA里是怎么实现这个旋转位置编码的,具体而言,LLaMA 的model.py文件里面实现了旋转位置编码(为方便大家理解,我给相关代码 加了下注释)首先,逐一实现这三个函数precompu...
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https://github.com/facebookresearch/llama-recipes/blob/main/examples/Getting_to_know_Llama.ipynb 。这是开始最常见的LLMs操作的好地方。 微调 全参数微调是一种微调所有层的所有参数的预训练模型的方法。总的来说,它可以达到最好的性能,但它也是最耗资源和时间的:它需要最多的GPU资源,并且需要最长的时间。...
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Here's how you can use llamafile to describe a jpg/png/gif/bmp image:llamafile -ngl 9999 --temp 0 \ --image ~/Pictures/lemurs.jpg \ -m llava-v1.5-7b-Q4_K.gguf \ --mmproj llava-v1.5-7b-mmproj-Q4_0.gguf \ -e -p '### User: What do you see?\n### Assistant: ' ...
# now append entry to the jsonl file. outfile.write('{"text": "' + text + '"}') outfile.write('\n') 如HF的Datasets库也是一个选择,但是我个人觉得他不好用。 在我们开始训练之前,我们要将文件作为数据集存储库推送到HF。可以直接使用huggingface-cli上传数据集。
print(dataset[randrange(len(dataset))]) 创建提示 为了执行指令微调,我们必须将每个数据示例转换为指令,并将其主要部分概述如下: def format_instruction(sample): return f"""### Instruction: Use the Task below and the Input given to write the Response, which is a programming code that can solve th...
--image ~/Pictures/lemurs.jpg \ -m llava-v1.5-7b-Q4_K.gguf \ --mmproj llava-v1.5-7b-mmproj-Q4_0.gguf \ -e -p '### User: What do you see?\n### Assistant: ' \ --no-display-prompt 2>/dev/null It's possible to use BNF grammar to enforce the output is predictable ...
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